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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Oncol.</journal-id>
<journal-title>Frontiers in Oncology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Oncol.</abbrev-journal-title>
<issn pub-type="epub">2234-943X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fonc.2021.736654</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Oncology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Comparison of PET/CT and MRI in the Diagnosis of Bone Metastasis in Prostate Cancer Patients: A Network Analysis of Diagnostic Studies</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Fanxiao</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1318378"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Dong</surname>
<given-names>Jinlei</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Shen</surname>
<given-names>Yelong</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/462299"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yun</surname>
<given-names>Canhua</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Ruixiao</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Ganggang</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Tan</surname>
<given-names>Jiyang</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Tao</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yao</surname>
<given-names>Qun</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Bomin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Lianxin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Mi</surname>
<given-names>Jingyi</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhou</surname>
<given-names>Dongsheng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Xiong</surname>
<given-names>Fei</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
<xref ref-type="aff" rid="aff7">
<sup>7</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1386283"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Department of Orthopaedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University</institution>, <addr-line>Jinan</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University</institution>, <addr-line>Jinan</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Department of Nuclear Medicine, The Second Hospital of Shandong University</institution>, <addr-line>Jinan</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Department of Urology Surgery, University Hospital of Munich, Ludwig-Maximilians-University</institution>, <addr-line>Munich</addr-line>, <country>Germany</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>Department of Urology Surgery, Shandong Provincial Hospital Affiliated to Shandong University</institution>, <addr-line>Jinan</addr-line>, <country>China</country>
</aff>
<aff id="aff6">
<sup>6</sup>
<institution>Department of Sports Medicine, Wuxi 9th People&#x2019;s Hospital Affiliated to Soochow University</institution>, <addr-line>Wuxi</addr-line>, <country>China</country>
</aff>
<aff id="aff7">
<sup>7</sup>
<institution>Orthopaedic Institute, Medical College, Soochow University</institution>, <addr-line>Suzhou</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Kyung Hyun Sung, UCLA Health System, United States</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Celine Taglang, University of California, San Francisco, United&#xa0;States; Daniel Margolis, Weill Cornell Medical Center, United&#xa0;States</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Fei Xiong, <email xlink:href="mailto:wxsdjrmyy_xf@outlook.com">wxsdjrmyy_xf@outlook.com</email>
</p>
</fn>
<fn fn-type="other" id="fn002">
<p>This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>04</day>
<month>10</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="collection">
<year>2021</year>
</pub-date>
<volume>11</volume>
<elocation-id>736654</elocation-id>
<history>
<date date-type="received">
<day>05</day>
<month>07</month>
<year>2021</year>
</date>
<date date-type="accepted">
<day>10</day>
<month>09</month>
<year>2021</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2021 Liu, Dong, Shen, Yun, Wang, Wang, Tan, Wang, Yao, Wang, Li, Mi, Zhou and Xiong</copyright-statement>
<copyright-year>2021</copyright-year>
<copyright-holder>Liu, Dong, Shen, Yun, Wang, Wang, Tan, Wang, Yao, Wang, Li, Mi, Zhou and Xiong</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Accurate diagnosis of bone metastasis status of prostate cancer (PCa) is becoming increasingly more important in guiding local and systemic treatment. Positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) have increasingly been utilized globally to assess the bone metastases in PCa. Our meta-analysis was a high-volume series in which the utility of PET/CT with different radioligands was compared to MRI with different parameters in this setting.</p>
</sec>
<sec>
<title>Materials and Methods</title>
<p>Three databases, including Medline, Embase, and Cochrane Library, were searched to retrieve original trials from their inception to August 31, 2019 according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement. The methodological quality of the included studies was assessed by two independent investigators utilizing Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A Bayesian network meta-analysis was performed using an arm-based model. Absolute sensitivity and specificity, relative sensitivity and specificity, diagnostic odds ratio (DOR), and superiority index, and their associated 95% confidence intervals (CI) were used to assess the diagnostic value.</p>
</sec>
<sec>
<title>Results</title>
<p>Forty-five studies with 2,843 patients and 4,263 lesions were identified. Network meta-analysis reveals that 68Ga-labeled prostate membrane antigen (68Ga-PSMA) PET/CT has the highest superiority index (7.30) with the sensitivity of 0.91 and specificity of 0.99, followed by 18F-NaF, 11C-choline, 18F-choline, 18F-fludeoxyglucose (FDG), and 18F-fluciclovine PET/CT. The use of high magnetic field strength, multisequence, diffusion-weighted imaging (DWI), and more imaging planes will increase the diagnostic value of MRI for the detection of bone metastasis in prostate cancer patients. Where available, 3.0-T high-quality MRI approaches 68Ga-PSMA PET/CT was performed in the detection of bone metastasis on patient-based level (sensitivity, 0.94 <italic>vs.</italic> 0.91; specificity, 0.94 <italic>vs.</italic> 0.96; superiority index, 4.43 <italic>vs.</italic> 4.56).</p>
</sec>
<sec>
<title>Conclusions</title>
<p>68Ga-PSMA PET/CT is recommended for the diagnosis of bone metastasis in prostate cancer patients. Where available, 3.0-T high-quality MRI approaches 68Ga-PSMA PET/CT should be performed in the detection of bone metastasis.</p>
</sec>
</abstract>
<kwd-group>
<kwd>prostate cancer</kwd>
<kwd>bone metastasis</kwd>
<kwd>diagnostic study</kwd>
<kwd>network meta-analysis</kwd>
<kwd>MRI</kwd>
<kwd>PET/CT</kwd>
</kwd-group>
<contract-num rid="cn001">201808080126, 201706920036</contract-num>
<contract-num rid="cn002">NO 81301556</contract-num>
<contract-sponsor id="cn001">China Scholarship Council<named-content content-type="fundref-id">10.13039/501100004543</named-content>
</contract-sponsor>
<contract-sponsor id="cn002">National Natural Science Foundation of China-China Academy of General Technology Joint Fund for Basic Research<named-content content-type="fundref-id">10.13039/501100019492</named-content>
</contract-sponsor>
<counts>
<fig-count count="4"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="138"/>
<page-count count="17"/>
<word-count count="8132"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>Prostate cancer (PCa) is the second most frequently diagnosed cancer and the fifth leading cause of cancer death in men according to global cancer statistics in 2018 (<xref ref-type="bibr" rid="B1">1</xref>). Although the 5-year survival rate is fairly high, the common cause of death is bone metastasis (<xref ref-type="bibr" rid="B2">2</xref>&#x2013;<xref ref-type="bibr" rid="B5">5</xref>), which is the second most site of metastases in PCa (<xref ref-type="bibr" rid="B6">6</xref>, <xref ref-type="bibr" rid="B7">7</xref>). Patients with an early diagnosis of localized disease may benefit from radical localized curative treatment (<xref ref-type="bibr" rid="B8">8</xref>), but patients who suffer from bone metastasis may be only eligible for hormone therapy or chemotherapy (<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B10">10</xref>). Hence, assessment of bone metastasis status, especially the early detection, is an important issue in the management of PCa. To follow and quantify the metastasis extent, which is an independent prognostic factor (<xref ref-type="bibr" rid="B11">11</xref>), the use of non-invasive imaging modalities is essential (<xref ref-type="bibr" rid="B12">12</xref>).</p>
<p>For decades, European and US guidelines recommend bone scintigraphy (BS) for bone metastasis diagnosis, which, if necessary, can be complemented by targeted X-rays (TXR) (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B14">14</xref>). This BS/TXR association is imperfect, which strikingly lacks diagnostic specificity (<xref ref-type="bibr" rid="B15">15</xref>, <xref ref-type="bibr" rid="B16">16</xref>), although the use of single-photon emission computed tomography (SPECT) improves the resolution (<xref ref-type="bibr" rid="B8">8</xref>, <xref ref-type="bibr" rid="B17">17</xref>). Equivocal imaging results of BS are required to be determined by the additional use of magnetic resonance imaging (MRI); however, this multiple approach can add to the cost and become inconvenient for patients (<xref ref-type="bibr" rid="B18">18</xref>). The accuracy of MRI to detect bone metastasis has been highlighted for almost 30 years, and the superiority than BS has also been repeatedly suggested (<xref ref-type="bibr" rid="B18">18</xref>&#x2013;<xref ref-type="bibr" rid="B21">21</xref>). A meta-analysis conducted by Woo et&#xa0;al. demonstrated the excellent diagnostic performance of MRI for the detection of bone metastasis in PCa (<xref ref-type="bibr" rid="B22">22</xref>). The development of new technology, such as diffusion-weighted imaging (DWI) (<xref ref-type="bibr" rid="B23">23</xref>), and the application of whole-body MRI (WB-MRI) may further expand the potential of MRI (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B25">25</xref>). However, the use of MRI for one-step cancer tumor&#x2013;node&#x2013;metastasis (TMN) staging is often presented as not feasible due to costs and the limited study validating this modality (<xref ref-type="bibr" rid="B26">26</xref>).</p>
<p>In recent years, positron emission tomography/computed tomography (PET/CT) has emerged as a promising molecular imaging tool in the diagnosis, staging, restaging, and therapeutic evaluation of several malignancies, as PET provides metabolic information and morphological imaging techniques offer anatomical data (<xref ref-type="bibr" rid="B27">27</xref>, <xref ref-type="bibr" rid="B28">28</xref>). 18F-fluorodeoxyglucose (18F-FDG) is the most widely used PET-imaging agent in oncology detection; however, the low glycolytic rate of most skeletal metastases in PCa and the influence of bladder activity limit the sensitivity for clinical detection (<xref ref-type="bibr" rid="B7">7</xref>). The European Nuclear Medical Association recommended PET/CT in their guidelines for bone imaging in 2015 (<xref ref-type="bibr" rid="B29">29</xref>), which can show areas of altered osteogenic activity by using 18F-sodium fluoride (18F-NaF), a bone-specific radiotracer (<xref ref-type="bibr" rid="B30">30</xref>). Additionally, 11C- or 18F-choline are designed to target tumor cells directly (<xref ref-type="bibr" rid="B31">31</xref>), and the European Association of Urology (EAU) once suggested referring patients for 11C- or 18F-choline PET/CT when the prostate-specific antigen (PSA) increases &gt;1 ng/ml, and the result is expected to change patient management (<xref ref-type="bibr" rid="B32">32</xref>). Over the last 5 years, 68Ga-labeled prostate-specific membrane antigen (68Ga-PSMA) PET/CT has gained widespread use to assess PCa (<xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B34">34</xref>), which could identify metastatic lesions in lymph node, bone, and soft tissue at low PSA levels (<xref ref-type="bibr" rid="B33">33</xref>&#x2013;<xref ref-type="bibr" rid="B35">35</xref>). Therefore, 68Ga-PSMA PET/CT is regarded as a more specific modality for diagnosing osseous metastases in PCa (<xref ref-type="bibr" rid="B36">36</xref>).</p>
<p>Despite the increasing numbers of studies regarding PET/CT and MRI in the diagnosis procedure for bone metastases in PCa, the effectiveness of these two modalities still remains no consensus. Zhou et&#xa0;al. (<xref ref-type="bibr" rid="B37">37</xref>) compared PET/CT and MRI; the final conclusion was very general because of the limitation of traditional meta-methods, which could not directly compare the PET/CT using different radioligands and MRI with different parameters. Recently, Nyaga et&#xa0;al. (<xref ref-type="bibr" rid="B38">38</xref>) developed a Bayesian network meta-analysis using an arm-based model based on the assumption that the missing arms occur at random. This method has been applied in several studies (<xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B40">40</xref>) because it could allow analysis of the variability in the accuracy of multiple tests within and between studies simultaneously (<xref ref-type="bibr" rid="B41">41</xref>).</p>
<p>The arm-based model is more appealing than traditional meta-analysis and the contrast-based model since the former not only permits more straightforward interpretation of the parameters, making use of all available data and yielding shorter credible intervals, but also provides more natural variance&#x2013;covariance matrix structures. We adopted this model, which makes our results more convincing.</p>
<p>Thus, the primary aim of our meta-analysis is to compare the diagnostic accuracy of PET/CT and MRI in detecting bone metastases in PCa on a per-patient and per-lesion basis, respectively. Additionally, thanks to the establishment of the network meta-analysis, a direct comparison is performed between PET/CT with different radioligands and MRI with different magnet field strengths, coverage, and parameters to provide better evidence-based advice to physicians. The hypothesis is that the multiparametric MRI equipped with high magnet field strength is an appropriate modality for the diagnosis of bone metastasis in PCa.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>2 Materials and Methods</title>
<sec id="s2_1">
<title>2.1 Protocol and Guidance</title>
<p>This network meta-analysis was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) (<xref ref-type="bibr" rid="B42">42</xref>) statement and PRISMA (PROSPERO registration number CRD42020148865).</p>
</sec>
<sec id="s2_2">
<title>2.2 Inclusion Criteria</title>
<p>The included studies should meet all of the following inclusion criteria: clinical trials evaluating the diagnostic value of PET/CT or/and MRI for bone metastasis in prostate cancer patients; articles published providing data to calculate diagnostic parameters, including true positive (TP), false positive (FP), false negative (FN), and true negative (TN); and studies having a conclusive anatomical or morphological verification standard to prove or disprove the imaging study result, such as pathological examination or clinical confirmation.</p>
</sec>
<sec id="s2_3">
<title>2.3 Exclusion Criteria</title>
<p>We excluded studies if they were commentaries, letters, case reports, reviews or non-full-text studies; if all the participants were prostate patients with bone metastasis; and if they lack of the conclusive anatomical or morphological verification standard to prove or disprove the imaging study result, such as pathological examination or clinical confirmation.</p>
</sec>
<sec id="s2_4">
<title>2.4 Outcomes</title>
<p>The outcomes are the absolute sensitivity and specificity, relative sensitivity and specificity, diagnostic odds ratio (DOR), and superiority index of PET/CT with numerous tracers and MRI with different parameters for the diagnosis of bone metastasis in prostate cancer patients. <xref ref-type="supplementary-material" rid="SF1">
<bold>Supplementary Table S1</bold>
</xref> shows the definition of these outcomes.</p>
</sec>
<sec id="s2_5">
<title>2.5 Search Strategy</title>
<p>Three databases, including Medline, Embase, and Cochrane Library, were searched to retrieve original trials from their inception to August 31, 2019. We also searched ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform to identify ongoing or unpublished eligible trials. To maximize the search for relevant articles, a manual search of the references listed in all included trials and systematic reviews was performed to retrieve any relevant articles that were not listed in the databases. <xref ref-type="supplementary-material" rid="SF2">
<bold>Supplementary Table S2</bold>
</xref> shows the search strategy.</p>
</sec>
<sec id="s2_6">
<title>2.6 Study Selection</title>
<p>After removal of duplicates, two investigators performed a blind systematic screening for all titles and abstracts in duplicate. Then, the full texts of the remainders were downloaded to confirm their eligibility based on the above criteria. To maximize the sensitivity of the screen, disagreements at the title and abstract stages were resolved by automatic inclusion, whereas discrepancies at the full-text stage were resolved by consensus with input from a senior third investigator.</p>
</sec>
<sec id="s2_7">
<title>2.7 Data Collection Process</title>
<p>Two independent investigators used a predesigned Microsoft Excel spreadsheet (Version 2013, Microsoft, Redmond, WA, USA) to extract basic information from the included studies. The diagnostic data (TPs, FPs, FNs, and TNs) were extracted or calculated using the following methods, which are presented in <xref ref-type="supplementary-material" rid="SF3">
<bold>Supplementary Table S3</bold>
</xref>. The spreadsheets were combined, and each investigator checked a random selection of the other&#x2019;s entries for quality control. Any discrepancies were resolved by consensus.</p>
</sec>
<sec id="s2_8">
<title>2.8 Quality Assessment of Included Studies</title>
<p>The methodological quality of the included studies was assessed by two independent investigators utilizing Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, which comprised of four key domains (patient selection, index test, reference standard, and flow and timing). Detailed information is shown in <xref ref-type="supplementary-material" rid="SF4">
<bold>Supplementary Table S4</bold>
</xref>.</p>
</sec>
<sec id="s2_9">
<title>2.9 Data Synthesis</title>
<p>A Bayesian network meta-analysis using an arm-based model, developed by Nyaga et&#xa0;al., was performed by running three chains in parallel until there is convergence. We used absolute sensitivity and specificity, relative sensitivity and specificity, DOR, and superiority index and their associated 95% confidence intervals (CI) to assess the diagnostic value of PET/CT and MRI. To assess the relative performance of diagnostic tests, the definitions of superior, inferior, equal, and not comparable were drawn. A diagnostic test, which is pairwise superior to a relatively large number of other tests and pairwise inferior to relatively few other tests, should have a high superiority value and be ranked higher than those tests that do not perform as well. In this network meta-analysis, superiority index was pooled to quantify rank probabilities of a PET/CT and MRI. All network meta-analyses were performed using R (v3.4.3; Comprehensive R Archive Network). <xref ref-type="supplementary-material" rid="SF5">
<bold>Supplementary Table S5</bold>
</xref> shows the main information of implementation process, software&#x2019;s packages, and models.</p>
<sec id="s2_9_1">
<title>2.9.1 Subgroup Analyses</title>
<p>For different tracers of PET/CT, we performed several subgroup analyses according to clinical settings of prostate cancer (new diagnoses, mixed, and treated), number of patients (&lt;50 and&#xa0;&#x2265;50), patient age (60&#x2013;70 and &gt; 70), continent of origin (Europe and others), study design (prospective and retrospective), and methods of imaging analyses (visual and semiquantitative evaluation). For the parameters of MRI, we performed several subgroup analyses according to magnetic field strength (1.5 and 3.0 T), the number of sequences (single sequences, or &#x2265; multisequences), whether using DWI or not, number of imaging planes (1 or &#x2265;2), and MRI coverage (pelvis skeleton, axial skeleton, or whole-body skeleton).</p>
</sec>
<sec id="s2_9_2">
<title>2.9.2 Sensitivity Analyses</title>
<p>The sensitivity analyses were performed by deleting studies involving only one diagnostic test for detecting bone metastasis, studies with low a QUADAS-2 score, studies with the maximum cases, studies with the minimum cases, and studies published before 2010.</p>
</sec>
</sec>
<sec id="s2_10">
<title>2.10 Patient and Public Involvement</title>
<p>No patients were involved in setting the research question or the outcome measures, nor were they involved in developing plans for design or implementation of the study. No patients were asked to advise on interpretation or writing up of results. There are no plans to disseminate the results of the research to study participants or the relevant patient community. It was not evaluated whether the studies included in the review had any patient involvement.</p>
</sec>
</sec>
<sec id="s3">
<title>3 Results</title>
<sec id="s3_1">
<title>3.1 Eligible Studies and Study Characteristics</title>
<p>We initially identified 24,491 records and included 45 eligible studies (<xref ref-type="bibr" rid="B8">8</xref>, <xref ref-type="bibr" rid="B43">43</xref>&#x2013;<xref ref-type="bibr" rid="B86">86</xref>) in the final network meta-analysis (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). The studies comprised 2,843 participants (4,263 lesions), with 978 bone metastasis patients (2,186 lesions), and a total of 32 studies assessed the diagnostic value of PET/CT and 21 studies for MRI. <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref> shows the main characteristics of the included studies. <xref ref-type="supplementary-material" rid="SF6">
<bold>Supplementary Tables S6</bold>
</xref>, <xref ref-type="supplementary-material" rid="SF7">
<bold>S7</bold>
</xref> show the main technical parameters of MRI and PET/CT, respectively. <xref ref-type="supplementary-material" rid="SF8">
<bold>Supplementary Tables S8</bold>
</xref>, <xref ref-type="supplementary-material" rid="SF9">
<bold>9</bold>
</xref> show the diagnostic data of each included studies on patient- and lesion-based level, respectively. <xref ref-type="supplementary-material" rid="SF10">
<bold>Supplementary Table S10</bold>
</xref> shows the quality assessment of the included studies. Four (8.9%), 11 (24.4%), 18 (40.0%), 11 (4.5%), and 1 (2.2%) studies scored 11, 10, 9, 8, and 7, respectively.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Selection flow chart for studies included in the network meta-analysis.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-11-736654-g001.tif"/>
</fig>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Main characteristics of the included studies.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" rowspan="3" align="left">Study, year</th>
<th valign="top" rowspan="3" align="center">Country</th>
<th valign="top" colspan="3" align="center">No. of patients</th>
<th valign="top" align="center">Clinical setting</th>
<th valign="top" colspan="2" align="center">Age (years)</th>
<th valign="top" colspan="2" align="center">PSA (ng/ml)</th>
<th valign="top" align="center">Clinical T stage</th>
<th valign="top" colspan="2" align="center">Gleason score</th>
<th valign="top" rowspan="3" align="center">Type of study</th>
<th valign="top" rowspan="3" align="center">Inclusion interval</th>
</tr>
<tr>
<th valign="top" rowspan="2" align="center">Total (n)</th>
<th valign="top" colspan="2" align="center">Metastasis</th>
<th valign="top" rowspan="2" align="center">New or treated</th>
<th valign="top" rowspan="2" align="center">Mean &#xb1; SD</th>
<th valign="top" rowspan="2" align="center">Range</th>
<th valign="top" rowspan="2" align="center">Mean &#xb1; SD</th>
<th valign="top" rowspan="2" align="center">Range</th>
<th valign="top" rowspan="2" align="center">Range</th>
<th valign="top" rowspan="2" align="center">Median</th>
<th valign="top" rowspan="2" align="center">Range</th>
</tr>
<tr>
<th valign="top" align="center">n</th>
<th valign="top" align="center">%</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Even-Sapir et&#xa0;al. (<xref ref-type="bibr" rid="B8">8</xref>)</td>
<td valign="top" align="left">Israel</td>
<td valign="top" align="center">44</td>
<td valign="top" align="center">23</td>
<td valign="top" align="center">52.27</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">71.6 &#xb1; 8.8</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">&#x2265;20</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">&#x2265;8</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Eschmann et&#xa0;al. (<xref ref-type="bibr" rid="B43">43</xref>)</td>
<td valign="top" align="left">Germany</td>
<td valign="top" align="center">44</td>
<td valign="top" align="center">44</td>
<td valign="top" align="center">100.00</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">Median 64.1</td>
<td valign="top" align="center">51&#x2013;79</td>
<td valign="top" align="left">Median 5.4</td>
<td valign="top" align="center">0.15&#x2013;200</td>
<td valign="top" align="left">T1&#x2013;T4</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">11.2004&#x2013;01.2006</td>
</tr>
<tr>
<td valign="top" align="left">Lecouvet et&#xa0;al. (<xref ref-type="bibr" rid="B44">44</xref>)</td>
<td valign="top" align="left">USA</td>
<td valign="top" align="center">66</td>
<td valign="top" align="center">41</td>
<td valign="top" align="center">62.12</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">74</td>
<td valign="top" align="center">46&#x2013;85</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Nemeth et&#xa0;al. (<xref ref-type="bibr" rid="B45">45</xref>)</td>
<td valign="top" align="left">USA</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">87.50</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">1998&#x2013;2004</td>
</tr>
<tr>
<td valign="top" align="left">Beheshti et&#xa0;al. (<xref ref-type="bibr" rid="B46">46</xref>)</td>
<td valign="top" align="left">Austria</td>
<td valign="top" align="center">38</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">69 &#xb1; 8</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">56 &#xb1; 64</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Beheshti et&#xa0;al. (<xref ref-type="bibr" rid="B6">6</xref>)</td>
<td valign="top" align="left">Austria</td>
<td valign="top" align="center">70</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">68 &#xb1; 7</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">&#x2265;10</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">&#x2265;7</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Venkitaraman et&#xa0;al. (<xref ref-type="bibr" rid="B47">47</xref>, <xref ref-type="bibr" rid="B48">48</xref>)</td>
<td valign="top" align="left">UK</td>
<td valign="top" align="center">99</td>
<td valign="top" align="center">14</td>
<td valign="top" align="center">14.14</td>
<td valign="top" align="left">Newly diagnosed</td>
<td valign="top" align="left">Median 66</td>
<td valign="top" align="center">44&#x2013;83</td>
<td valign="top" align="left">Median 26.5</td>
<td valign="top" align="center">2&#x2013;1600</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">6&#x2013;10</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">12.2001&#x2013;12.2005</td>
</tr>
<tr>
<td valign="top" align="left">Venkitaraman et&#xa0;al. (<xref ref-type="bibr" rid="B47">47</xref>, <xref ref-type="bibr" rid="B48">48</xref>)<sup>#</sup>
</td>
<td valign="top" align="left">UK</td>
<td valign="top" align="center">39</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">25.64</td>
<td valign="top" align="left">Newly diagnosed</td>
<td valign="top" align="left">Median 65</td>
<td valign="top" align="center">54&#x2013;82</td>
<td valign="top" align="left">Median 34</td>
<td valign="top" align="center">5&#x2013;1300</td>
<td valign="top" align="left">T1&#x2013;T4</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">5&#x2013;9</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">12.2001&#x2013;07.2004</td>
</tr>
<tr>
<td valign="top" align="left">Fuccio et&#xa0;al. (<xref ref-type="bibr" rid="B50">50</xref>)</td>
<td valign="top" align="left">Italy</td>
<td valign="top" align="center">25</td>
<td valign="top" align="center">22</td>
<td valign="top" align="center">88.00</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">70.2</td>
<td valign="top" align="center">58&#x2013;80</td>
<td valign="top" align="left">Median 6.3</td>
<td valign="top" align="center">0.2&#x2013;37.7</td>
<td valign="top" align="left">T2N0/Nx M0-T4N1M0</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">6&#x2013;9</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Iagaru et&#xa0;al. (<xref ref-type="bibr" rid="B51">51</xref>)</td>
<td valign="top" align="left">USA</td>
<td valign="top" align="center">18</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">50.00</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">09.2007&#x2013;12.2010</td>
</tr>
<tr>
<td valign="top" align="left">Langsteger et&#xa0;al. (<xref ref-type="bibr" rid="B52">52</xref>)</td>
<td valign="top" align="left">Austria</td>
<td valign="top" align="center">40</td>
<td valign="top" align="center">22</td>
<td valign="top" align="center">55.00</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">66</td>
<td valign="top" align="center">51&#x2013;82</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">0.38&#x2013;617</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">4&#x2013;9</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">01.2003&#x2013;12.2009</td>
</tr>
<tr>
<td valign="top" align="left">Bortot et&#xa0;al. (<xref ref-type="bibr" rid="B53">53</xref>)</td>
<td valign="top" align="left">Brazil</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">22.22</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="left">67.6</td>
<td valign="top" align="center">56&#x2013;82</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Jadvar et a (<xref ref-type="bibr" rid="B54">54</xref>)</td>
<td valign="top" align="left">USA</td>
<td valign="top" align="center">37</td>
<td valign="top" align="center">14</td>
<td valign="top" align="center">37.84</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">Median 71.1</td>
<td valign="top" align="center">53.5&#x2013;86.9</td>
<td valign="top" align="left">Median 3.2</td>
<td valign="top" align="center">0.5&#x2013;40.2</td>
<td valign="top" align="left">T1c&#x2013;T3</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">22.09.2010&#x2013;23.06.2011</td>
</tr>
<tr>
<td valign="top" align="left">Lecouvet et&#xa0;al. (<xref ref-type="bibr" rid="B55">55</xref>)</td>
<td valign="top" align="left">Belgium</td>
<td valign="top" align="center">100</td>
<td valign="top" align="center">51</td>
<td valign="top" align="center">51.00</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">69</td>
<td valign="top" align="center">53&#x2013;88</td>
<td valign="top" align="left">32</td>
<td valign="top" align="center">12&#x2013;78</td>
<td valign="top" align="left">&#x2265;T3b</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">&#x2265;8</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">03.2007&#x2013;03.2010</td>
</tr>
<tr>
<td valign="top" align="left">Mosavi et&#xa0;al. (<xref ref-type="bibr" rid="B56">56</xref>)</td>
<td valign="top" align="left">Sweden</td>
<td valign="top" align="center">49</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">10.20</td>
<td valign="top" align="left">Newly diagnosed</td>
<td valign="top" align="left">Median 67</td>
<td valign="top" align="center">57&#x2013;80</td>
<td valign="top" align="left">Median 14</td>
<td valign="top" align="center">1.3&#x2013;950</td>
<td valign="top" align="left">T1c&#x2013;T4</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">8&#x2013;10</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">01.2009&#x2013;03.2011</td>
</tr>
<tr>
<td valign="top" align="left">Picchio et&#xa0;al. (<xref ref-type="bibr" rid="B57">57</xref>)</td>
<td valign="top" align="left">Italy</td>
<td valign="top" align="center">78</td>
<td valign="top" align="center">27</td>
<td valign="top" align="center">34.62</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">69</td>
<td valign="top" align="center">47&#x2013;82</td>
<td valign="top" align="left">21.1</td>
<td valign="top" align="center">0.2&#x2013;500.0</td>
<td valign="top" align="left">T2N0&#x2013;T4N0</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">03.2005&#x2013;02.2010</td>
</tr>
<tr>
<td valign="top" align="left">Takesh et&#xa0;al. (<xref ref-type="bibr" rid="B58">58</xref>)</td>
<td valign="top" align="left">Germany</td>
<td valign="top" align="center">37</td>
<td valign="top" align="center">18</td>
<td valign="top" align="center">48.65</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">69 &#xb1; 7</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">2.6</td>
<td valign="top" align="center">0.3&#x2013;21</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">3&#x2013;9</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Damle et&#xa0;al. (<xref ref-type="bibr" rid="B59">59</xref>)</td>
<td valign="top" align="left">India</td>
<td valign="top" align="center">49</td>
<td valign="top" align="center">32</td>
<td valign="top" align="center">65.31</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">65</td>
<td valign="top" align="center">50&#x2013;84</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">T3/T4</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">8&#x2013;10</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Kitajima et&#xa0;al. (<xref ref-type="bibr" rid="B60">60</xref>)</td>
<td valign="top" align="left">USA</td>
<td valign="top" align="center">95</td>
<td valign="top" align="center">16</td>
<td valign="top" align="center">16.84</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">65.7</td>
<td valign="top" align="center">49&#x2013;87</td>
<td valign="top" align="left">Median 2.5</td>
<td valign="top" align="center">0.58&#x2013;68.3</td>
<td valign="top" align="left">T2N0&#x2013;any T N1</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">2&#x2013;10</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">12.2011&#x2013;01.2013</td>
</tr>
<tr>
<td valign="top" align="left">Pasoglou et&#xa0;al. (<xref ref-type="bibr" rid="B61">61</xref>)</td>
<td valign="top" align="left">Belgium</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">30.00</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">Median 62.5</td>
<td valign="top" align="center">51.0&#x2013;92.0</td>
<td valign="top" align="left">Median 30</td>
<td valign="top" align="center">1.7&#x2013;4612.0</td>
<td valign="top" align="left">cT2&#x2013;cT4</td>
<td valign="top" align="center">7.8</td>
<td valign="top" align="center">6&#x2013;9</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Piccardo et&#xa0;al. (<xref ref-type="bibr" rid="B62">62</xref>)</td>
<td valign="top" align="left">Italy</td>
<td valign="top" align="center">21</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">28.57</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">77.2 &#xb1; 5.1</td>
<td valign="top" align="center">70&#x2013;85</td>
<td valign="top" align="left">5.8 &#xb1; 3.4</td>
<td valign="top" align="center">2.2&#x2013;13.4</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">7&#x2013;9</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Poulsen et&#xa0;al. (<xref ref-type="bibr" rid="B63">63</xref>)</td>
<td valign="top" align="left">Denmark</td>
<td valign="top" align="center">50</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">73 &#xb1; 8.6</td>
<td valign="top" align="center">53&#x2013;94</td>
<td valign="top" align="left">Median 84</td>
<td valign="top" align="center">4&#x2013;5740</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">5&#x2013;10</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">05.2009&#x2013;03.2012</td>
</tr>
<tr>
<td valign="top" align="left">Evangelista et&#xa0;al. (<xref ref-type="bibr" rid="B64">64</xref>)</td>
<td valign="top" align="left">Italy</td>
<td valign="top" align="center">48</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">22.92</td>
<td valign="top" align="left">Newly diagnosed</td>
<td valign="top" align="left">70</td>
<td valign="top" align="center">49&#x2013;86</td>
<td valign="top" align="left">38.34 &#xb1; 90.12</td>
<td valign="top" align="center">2.80&#x2013;581.0</td>
<td valign="top" align="left">T2&#x2013;T4</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">6&#x2013;10</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">04.2010-04.2013</td>
</tr>
<tr>
<td valign="top" align="left">Pasoglou et&#xa0;al. (<xref ref-type="bibr" rid="B65">65</xref>)</td>
<td valign="top" align="left">Belgium</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">33.33</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">69</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">31 &#xb1; 28</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">02.2012&#x2013;12.2012</td>
</tr>
<tr>
<td valign="top" align="left">Sampath et&#xa0;al. (<xref ref-type="bibr" rid="B66">66</xref>)</td>
<td valign="top" align="left">USA</td>
<td valign="top" align="center">38</td>
<td valign="top" align="center">22</td>
<td valign="top" align="center">57.89</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">09.2007&#x2013;07.2013</td>
</tr>
<tr>
<td valign="top" align="left">Wieder et&#xa0;al. (<xref ref-type="bibr" rid="B67">67</xref>)</td>
<td valign="top" align="left">Germany</td>
<td valign="top" align="center">57</td>
<td valign="top" align="center">50</td>
<td valign="top" align="center">87.72</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">Median 86</td>
<td valign="top" align="center">54&#x2013;80</td>
<td valign="top" align="left">29.9</td>
<td valign="top" align="center">1&#x2013;670</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">6&#x2013;9</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Barchetti et&#xa0;al. (<xref ref-type="bibr" rid="B68">68</xref>)</td>
<td valign="top" align="left">Italy</td>
<td valign="top" align="center">152</td>
<td valign="top" align="center">70</td>
<td valign="top" align="center">46.05</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">53&#x2013;88</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">&#x2265;7</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">09.2011&#x2013;01.2014</td>
</tr>
<tr>
<td valign="top" align="left">Conde-Moreno et&#xa0;al. (<xref ref-type="bibr" rid="B69">69</xref>)</td>
<td valign="top" align="left">Spain</td>
<td valign="top" align="center">35</td>
<td valign="top" align="center">17</td>
<td valign="top" align="center">48.57</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">Median 70 &#xb1; 6.77</td>
<td valign="top" align="center">52&#x2013;80</td>
<td valign="top" align="left">Median 12</td>
<td valign="top" align="center">4.54&#x2013;75.86</td>
<td valign="top" align="left">T1N0M0&#x2013;T4N0M0</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">5&#x2013;9</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">01.2014&#x2013;03.2015</td>
</tr>
<tr>
<td valign="top" align="left">Nanni et&#xa0;al. (<xref ref-type="bibr" rid="B70">70</xref>)</td>
<td valign="top" align="left">Italy</td>
<td valign="top" align="center">89</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">6.74</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">69</td>
<td valign="top" align="center">55&#x2013;83</td>
<td valign="top" align="left">6.99</td>
<td valign="top" align="center">0.20&#x2013;20.72</td>
<td valign="top" align="left">T1N0/Nx&#x2013;T3N0/Nx</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Woo et&#xa0;al. (<xref ref-type="bibr" rid="B71">71</xref>)</td>
<td valign="top" align="left">Korea</td>
<td valign="top" align="center">308</td>
<td valign="top" align="center">21</td>
<td valign="top" align="center">6.82</td>
<td valign="top" align="left">Newly diagnosed</td>
<td valign="top" align="left">68.5 &#xb1; 7.8</td>
<td valign="top" align="center">38&#x2013;91</td>
<td valign="top" align="left">30.9</td>
<td valign="top" align="center">1.2&#x2013;955.5</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">6&#x2013;10</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">01.2013-12.2013</td>
</tr>
<tr>
<td valign="top" align="left">Yi et&#xa0;al. (<xref ref-type="bibr" rid="B72">72</xref>)</td>
<td valign="top" align="left">China</td>
<td valign="top" align="center">26</td>
<td valign="top" align="center">12</td>
<td valign="top" align="center">46.15</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">72.2</td>
<td valign="top" align="center">60&#x2013;88</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">&#x2265;20</td>
<td valign="top" align="left">T2&#x2013;T4</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">8&#x2013;10</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">08.2010&#x2013;11.2014</td>
</tr>
<tr>
<td valign="top" align="left">Fonager et&#xa0;al. (<xref ref-type="bibr" rid="B73">73</xref>)</td>
<td valign="top" align="left">Denmark</td>
<td valign="top" align="center">37</td>
<td valign="top" align="center">27</td>
<td valign="top" align="center">72.97</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">71</td>
<td valign="top" align="center">46&#x2013;87</td>
<td valign="top" align="left">Median 180</td>
<td valign="top" align="center">53&#x2013;9708</td>
<td valign="top" align="left">T1&#x2013;T4</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">7&#x2013;10</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">02.2014&#x2013;12.2015</td>
</tr>
<tr>
<td valign="top" align="left">Huysse et&#xa0;al. (<xref ref-type="bibr" rid="B74">74</xref>)</td>
<td valign="top" align="left">Belgium</td>
<td valign="top" align="center">64</td>
<td valign="top" align="center">62</td>
<td valign="top" align="center">96.88</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Janssen et&#xa0;al. (<xref ref-type="bibr" rid="B75">75</xref>)</td>
<td valign="top" align="left">Germany</td>
<td valign="top" align="center">54</td>
<td valign="top" align="center">29</td>
<td valign="top" align="center">53.70</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">69.6 &#xb1; 6.5</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">38.4 &#xb1; 77.9</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Kitajima et&#xa0;al. (<xref ref-type="bibr" rid="B76">76</xref>)</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="center">21</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">52.38</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">70.6 &#xb1; 10.8</td>
<td valign="top" align="center">47&#x2013;90</td>
<td valign="top" align="left">342.9</td>
<td valign="top" align="center">0.2&#x2013;5916</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">01.2015&#x2013;01.2017</td>
</tr>
<tr>
<td valign="top" align="left">Vargas et&#xa0;al. (<xref ref-type="bibr" rid="B77">77</xref>)</td>
<td valign="top" align="left">USA</td>
<td valign="top" align="center">228</td>
<td valign="top" align="center">57</td>
<td valign="top" align="center">25.00</td>
<td valign="top" align="left">Newly diagnosed</td>
<td valign="top" align="left">Median 63</td>
<td valign="top" align="center">36-83</td>
<td valign="top" align="left">Median 6.3</td>
<td valign="top" align="center">0.4&#x2013;222</td>
<td valign="top" align="left">T1c&#x2013;T4</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">6&#x2013;&#x2265;8</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">01.2000&#x2013;06.2014</td>
</tr>
<tr>
<td valign="top" align="left">Wondergem et&#xa0;al. (<xref ref-type="bibr" rid="B78">78</xref>)</td>
<td valign="top" align="left">Netherlands</td>
<td valign="top" align="center">104</td>
<td valign="top" align="center">61</td>
<td valign="top" align="center">58.65</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">74.9</td>
<td valign="top" align="center">49&#x2013;93</td>
<td valign="top" align="left">Median 88.7</td>
<td valign="top" align="center">2.5&#x2013;13500</td>
<td valign="top" align="left">T1&#x2013;T4</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">6&#x2013;10</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">01.2011&#x2013;04.2012</td>
</tr>
<tr>
<td valign="top" align="left">Dyrberg et al. (<xref ref-type="bibr" rid="B79">79</xref>)</td>
<td valign="top" align="left">Denmark</td>
<td valign="top" align="center">55</td>
<td valign="top" align="center">20</td>
<td valign="top" align="center">36.36</td>
<td valign="top" align="left">Newly diagnosed</td>
<td valign="top" align="left">75 &#xb1; 9</td>
<td valign="top" align="center">54&#x2013;91</td>
<td valign="top" align="left">85</td>
<td valign="top" align="center">5&#x2013;1000</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">6&#x2013;10</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">05.2016&#x2013;06.2017</td>
</tr>
<tr>
<td valign="top" align="left">Kawanaka et al. (<xref ref-type="bibr" rid="B80">80</xref>)</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">17</td>
<td valign="top" align="center">56.67</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">71.3 &#xb1; 9.0</td>
<td valign="top" align="center">47&#x2013;90</td>
<td valign="top" align="left">65.2 &#xb1; 177.4</td>
<td valign="top" align="center">0.23&#x2013;946</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">01.2015&#x2013;07.2017</td>
</tr>
<tr>
<td valign="top" align="left">Larbi et&#xa0;al. (<xref ref-type="bibr" rid="B81">81</xref>)</td>
<td valign="top" align="left">Belgium</td>
<td valign="top" align="center">50</td>
<td valign="top" align="center">37</td>
<td valign="top" align="center">74.00</td>
<td valign="top" align="left">Newly diagnosed</td>
<td valign="top" align="left">67 &#xb1; 10</td>
<td valign="top" align="center">59&#x2013;87</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">&#x2265;20</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">&#x2265; 8</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">01.2015&#x2013;12.2015</td>
</tr>
<tr>
<td valign="top" align="left">Lengana et&#xa0;al. (<xref ref-type="bibr" rid="B82">82</xref>)</td>
<td valign="top" align="left">South Africa</td>
<td valign="top" align="center">113</td>
<td valign="top" align="center">26</td>
<td valign="top" align="center">23.01</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">66.65</td>
<td valign="top" align="center">43&#x2013;88</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">NR</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Zacho et&#xa0;al. (<xref ref-type="bibr" rid="B83">83</xref>)</td>
<td valign="top" align="left">Denmark</td>
<td valign="top" align="center">68</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">14.71</td>
<td valign="top" align="left">Treated</td>
<td valign="top" align="left">67.2</td>
<td valign="top" align="center">47&#x2013;80</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">0.2&#x2013;11</td>
<td valign="top" align="left">M0&#x2013;M1</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">5&#x2013;9</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">NR</td>
</tr>
<tr>
<td valign="top" align="left">Chen et&#xa0;al. (<xref ref-type="bibr" rid="B84">84</xref>)</td>
<td valign="top" align="left">USA</td>
<td valign="top" align="center">106</td>
<td valign="top" align="center">14</td>
<td valign="top" align="center">13.21</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">Median 70</td>
<td valign="top" align="center">47&#x2013;80</td>
<td valign="top" align="left">Median 1.3</td>
<td valign="top" align="center">0&#x2013;61</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">6&#x2013;10</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">01.2017&#x2013;01.2018</td>
</tr>
<tr>
<td valign="top" align="left">Johnston et&#xa0;al. (<xref ref-type="bibr" rid="B85">85</xref>)</td>
<td valign="top" align="left">UK</td>
<td valign="top" align="center">56</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">8.93</td>
<td valign="top" align="left">Newly diagnosed</td>
<td valign="top" align="left">67.9</td>
<td valign="top" align="center">57.9&#x2013;84.4</td>
<td valign="top" align="left">Median 20.05</td>
<td valign="top" align="center">10.07&#x2013;61.20</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">6&#x2013;10</td>
<td valign="top" align="center">P</td>
<td valign="top" align="center">07.2012&#x2013;11.2015</td>
</tr>
<tr>
<td valign="top" align="left">Uslu&#x2013;besli et&#xa0;al. (<xref ref-type="bibr" rid="B86">86</xref>)</td>
<td valign="top" align="left">Turkey</td>
<td valign="top" align="center">28</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">39.29</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">67.3 &#xb1; 7.4</td>
<td valign="top" align="center">49&#x2013;82</td>
<td valign="top" align="left">25.49 &#xb1; 32.7</td>
<td valign="top" align="center">0.5&#x2013;125.1</td>
<td valign="top" align="left">NR</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">6&#x2013;9</td>
<td valign="top" align="center">R</td>
<td valign="top" align="center">03.2015-03.2016</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>SD, Standard deviation; NR, Not reported; PSA, Prostate specific antigen; P, Prospective; R, Retrospective. <sup>#</sup>Represents different study.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<title>3.2 Diagnostic Value of PET/CT on Per-Patient Analysis</title>
<p>Regarding the tracers of the included studies, nine used 18F-NaF, seven selected 18F-choline, seven applied 11C-choline, five used 68Ga-PSMA, three selected 18F-FDG, and two applied 18F-FACBC. As shown in <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>, 18F-NaF PET/CT has the highest sensitivity of 0.95 (95% CI, 0.91&#x2013;0.99), followed closely by 68Ga-PSMA and 18F-choline PET/CT; 68Ga-PSMA PET/CT has the highest specificity of 0.99 (95% CI, 0.94-1.04), followed closely by 11C-choline and 18F-choline PET/CT. Network meta-analysis demonstrated that 68Ga-PSMA PET/CT has the highest diagnostic value with the highest superiority index of 7.30 (95% CI, 0.60&#x2013;11.00), followed closely by 18F-NaF (3.33; 95% CI, 0.20&#x2013;9.00), 11C-choline, 18F-choline, 18F-FACBC, and 18F-FDG PET/CT (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Network meta-analysis results including sensitivity, specificity, and superiority index values of PET/CT with six commonly used tracers for the detection of bone metastasis in prostate cancer patients. Sensitivity, specificity, and superiority index are reported as mean (range) unless otherwise indicated. PET/CT, positron emission tomography/computed tomography; NaF, sodium fluoride; PSMA, prostate membrane antigen; FDG, fludeoxyglucose; FACBC, fluciclovine.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-11-736654-g002.tif"/>
</fig>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>The network meta-analysis results of PET/CT with different tracers to detect bone metastasis in PCa.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Test</th>
<th valign="top" align="center">Absolute Sensitivity</th>
<th valign="top" align="center">Absolute Specificity</th>
<th valign="top" align="center">Diagnostic OR [Rank]</th>
<th valign="top" colspan="2" align="center">SuperiorityIndex [Rank]</th>
<th valign="top" align="center">Relative Sensitivity</th>
<th valign="top" align="center">Relative Specificity</th>
<th valign="top" align="center">Datasets, n</th>
<th valign="top" align="center">Studies, n</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">18F-NaF</td>
<td valign="top" align="center">0.95<break/>(0.91&#x2013;0.99)</td>
<td valign="top" align="center">0.88<break/>(0.83&#x2013;0.93)</td>
<td valign="top" align="center">248.15<break/>(34.14&#x2013;799.75)<break/>[2]</td>
<td valign="top" align="center">3.33<break/>(0.20&#x2013;9.00)<break/>[2]</td>
<td valign="top" colspan="2" align="center">1.00<break/>(1.00&#x2013;1.00)</td>
<td valign="top" align="center">1.00<break/>(1.00&#x2013;1.00)</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">9</td>
</tr>
<tr>
<td valign="top" align="left">18F-Choline</td>
<td valign="top" align="center">0.89<break/>(0.85&#x2013;0.93)</td>
<td valign="top" align="center">0.91<break/>(0.86&#x2013;0.96)</td>
<td valign="top" align="center">123.18<break/>(21.09&#x2013;361.86)<break/>[4]</td>
<td valign="top" align="center">1.71<break/>(0.14&#x2013;7.00)<break/>[4]</td>
<td valign="top" colspan="2" align="center">0.94<break/>(0.88&#x2013;1.00)</td>
<td valign="top" align="center">1.03<break/>(0.95&#x2013;1.11)</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">7</td>
</tr>
<tr>
<td valign="top" align="left">11C-Choline</td>
<td valign="top" align="center">0.86<break/>(0.81&#x2013;0.91)</td>
<td valign="top" align="center">0.94<break/>(0.89&#x2013;0.99)</td>
<td valign="top" align="center">208.88<break/>(21.37&#x2013;780.88)<break/>[3]</td>
<td valign="top" align="center">1.92<break/>(0.14&#x2013;7.00)<break/>[3]</td>
<td valign="top" colspan="2" align="center">0.91<break/>(0.84&#x2013;0.98)</td>
<td valign="top" align="center">1.07<break/>(0.98&#x2013;1.16)</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">7</td>
</tr>
<tr>
<td valign="top" align="left">68Ga-PSMA</td>
<td valign="top" align="center">0.91<break/>(0.83&#x2013;0.99)</td>
<td valign="top" align="center">0.99<break/>(0.94&#x2013;1.04)</td>
<td valign="top" align="center">3379817.37<break/>(49.99&#x2013;5941029.19)<break/>[1]</td>
<td valign="top" align="center">7.30<break/>(0.60&#x2013;11.00)<break/>[1]</td>
<td valign="top" colspan="2" align="center">0.96<break/>(0.87&#x2013;1.05)</td>
<td valign="top" align="center">1.12<break/>(1.04&#x2013;1.20)</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">5</td>
</tr>
<tr>
<td valign="top" align="left">18F-FDG</td>
<td valign="top" align="center">0.69<break/>(0.54&#x2013;0.84)</td>
<td valign="top" align="center">0.85<break/>(0.70&#x2013;1.00)</td>
<td valign="top" align="center">81.17<break/>(1.35&#x2013;497.30)<break/>[6]</td>
<td valign="top" align="center">0.49<break/>(0.09&#x2013;3.00)<break/>[6]</td>
<td valign="top" colspan="2" align="center">0.73<break/>(0.57&#x2013;0.89)</td>
<td valign="top" align="center">0.96<break/>(0.78&#x2013;1.14)</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">3</td>
</tr>
<tr>
<td valign="top" align="left">18F-FACBC</td>
<td valign="top" align="center">0.80<break/>(0.62&#x2013;0.98)</td>
<td valign="top" align="center">0.59<break/>(0.37&#x2013;0.81)</td>
<td valign="top" align="center">92.40<break/>(0.40&#x2013;598.84)<break/>[5]</td>
<td valign="top" align="center">0.55<break/>(0.09&#x2013;3.00)<break/>[5]</td>
<td valign="top" colspan="2" align="center">0.84<break/>(0.65&#x2013;1.03)</td>
<td valign="top" align="center">0.67<break/>(0.42&#x2013;0.92)</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">2</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data are reported as mean (range) unless otherwise indicated.</p>
</fn>
<fn>
<p>PET/CT, positron emission tomography/computed tomography; PCa, prostate cancer; NaF, sodium fluoride; PSMA, prostate membrane antigen; FDG, fludeoxyglucose; FACBC, fluciclovine; CI, credible interval; OR, odds ratio.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<sec id="s3_2_1">
<title>3.2.1 Sensitivity Analysis</title>
<p>The sensitivity analyses were performed by deleting studies involving only one diagnostic test for detecting bone metastasis, studies with low QUADAS-2 score, studies with the maximum cases, studies with the minimum cases, and studies published before 2010, and the results were stable (<xref ref-type="supplementary-material" rid="SF11">
<bold>Supplementary Tables S11</bold>
</xref>&#x2013;<xref ref-type="supplementary-material" rid="SF15">
<bold>S15</bold>
</xref>).</p>
</sec>
<sec id="s3_2_2">
<title>3.2.2 Subgroup Analysis</title>
<p>The subgroup analyses were performed according to clinical settings of prostate cancer (newly diagnoses, mixed, and treated), number of patients (&lt;50 and &#x2265;50), patient age (60&#x2013;70 and &gt;70), continent of origin (Europe and others), study design (prospective and retrospective), and methods of imaging analyses (visual and semiquantitative evaluation), and the results were stable (<xref ref-type="supplementary-material" rid="SF16">
<bold>Supplementary Tables S16</bold>
</xref>&#x2013;<xref ref-type="supplementary-material" rid="SF21">
<bold>S21</bold>
</xref>).</p>
</sec>
</sec>
<sec id="s3_3">
<title>3.3 Diagnostic Value of MRI on Per-Patient Analysis</title>
<sec id="s3_3_1">
<title>3.3.1 Magnetic Field Strength of MRI</title>
<p>The pooled sensitivity and specificity of 1.5-T MRI were 0.82 (95% CI, 0.73&#x2013;0.91) and 0.92 (95% CI, 0.87&#x2013;0.97), respectively, while the pooled sensitivity and specificity of 3.0-T MRI were 0.89 (95% CI, 0.80&#x2013;0.98) and 0.88 (95% CI, 0.79&#x2013;0.97). Network meta-analysis demonstrated that 68Ga-PSMA PET/CT had the highest diagnostic value with the highest superiority index, followed closely by 18F-NaF PET/CT, 3.0-T MRI [1.76, (95% CI, 0.09&#x2013;9.00)], 11C-choline PET/CT, 18F-choline PET/CT, and 1.5-T MRI [0.77, (95% CI, 0.09&#x2013;5.00)] (<xref ref-type="supplementary-material" rid="SF22">
<bold>Supplementary Table S22</bold>
</xref> and <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Network meta-analysis results including sensitivity, specificity, and superiority index values of PET/CT with four commonly used tracers and MRI with numerous characteristics for the detection of bone metastasis. Sensitivity, specificity, and superiority index are reported as mean (range) unless otherwise indicated. PET/CT, positron emission tomography/computed tomography; NaF, sodium fluoride; PSMA, prostate membrane antigen; DWI, diffusion-weighted imaging.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-11-736654-g003.tif"/>
</fig>
</sec>
<sec id="s3_3_2">
<title>3.3.2 Sequence of MRI</title>
<p>The pooled sensitivity and specificity of multisequence MRI were 0.91 (95% CI, 0.87&#x2013;0.95) and 0.93 (95% CI, 0.90&#x2013;0.96), while the pooled sensitivity and specificity of single-sequence MRI were 0.64 (95% CI, 0.48&#x2013;0.80) and 0.89 (95% CI, 0.76&#x2013;1.02), respectively. Network meta-analysis demonstrated that 68Ga-PSMA PET/CT had the highest diagnostic value with the highest superiority index, followed closely by 18F-NaF PET/CT, multisequence MRI [2.04, (95% CI, 0.14&#x2013;7.00)], 11C-choline PET/CT, 18F-choline PET/CT, and single-sequence MRI [0.36, (95% CI, 0.09&#x2013;1.02)] (<xref ref-type="supplementary-material" rid="SF23">
<bold>Supplementary Table S23</bold>
</xref> and <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
</sec>
<sec id="s3_3_3">
<title>3.3.3 Whether DWI Was Used</title>
<p>The pooled sensitivity and specificity of DWI MRI were 0.94 (95% CI, 0.89&#x2013;0.99) and 0.93 (95% CI 0.88&#x2013;0.98), while the pooled sensitivity and specificity of no-DWI MRI were 0.86 (95% CI, 0.71&#x2013;1.01) and 0.86 (95% CI, 0.74&#x2013;0.98), respectively. Network meta-analysis demonstrated that 68Ga-PSMA PET/CT had the highest diagnostic value with the highest superiority index, followed closely by DWI MRI [3.63, (95% CI 0.14&#x2013;9.00)], 18F-NaF PET/CT, no-DWI MRI [1.44, (95% CI, 0.09&#x2013;9.00)], 11C-choline PET/CT, and 18F-choline PET/CT (<xref ref-type="supplementary-material" rid="SF24">
<bold>Supplementary Table S24</bold>
</xref> and <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
</sec>
<sec id="s3_3_4">
<title>3.3.4 Imaging Plane of MRI</title>
<p>The pooled sensitivity and specificity of &#x2265;2 imaging planes MRI were 0.95 (95% CI, 0.90&#x2013;1.00) and 0.93 (95% CI, 0.89&#x2013;0.97), while the pooled sensitivity and specificity of 1 imaging plane MRI were 0.75 (95% CI, 0.69&#x2013;0.81) and 0.92 (95% CI, 0.86&#x2013;0.98), respectively. Network meta-analysis revealed that 68Ga-PSMA PET/CT had the highest diagnostic value with the highest superiority index, followed closely by two or more imaging planes MRI [4.27, (95% CI, 0.20&#x2013;9.00)], 18F-NaF PET/CT, 11C-choline PET/CT, 18F-choline PET/CT, and one imaging plane MRI [0.25, (95% CI, 0.09&#x2013;1.00)] (<xref ref-type="supplementary-material" rid="SF25">
<bold>Supplemental Table S25</bold>
</xref> and <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
</sec>
<sec id="s3_3_5">
<title>3.3.5 MRI Coverage</title>
<p>The pooled results demonstrated that axial skeleton or whole-body skeleton MRI had similar sensitivity of 0.84 <italic>vs.</italic> 0.82 and specificity of 0.93 <italic>vs.</italic> 0.94, compared with pelvis MRI. Network meta-analysis demonstrated that 68Ga-PSMA PET/CT had the highest diagnostic value with the highest superiority index, followed closely by 18F-NaF PET/CT, 11C-choline PET/CT, pelvis skeleton MRI, 18F-choline PET/CT, and axial skeleton or whole-body skeleton MRI (<xref ref-type="supplementary-material" rid="SF26">
<bold>Supplementary Table S26</bold>
</xref> and <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
</sec>
<sec id="s3_3_6">
<title>3.3.6 Sensitivity Analysis</title>
<p>In five subgroup analyses above, we performed sensitivity analysis by deleting studies involving only one test for detecting bone metastasis, which show the similar results (<xref ref-type="supplementary-material" rid="SF27">
<bold>Supplementary Tables S27</bold>
</xref>&#x2013;<xref ref-type="supplementary-material" rid="SF31">
<bold>S31</bold>
</xref>).</p>
</sec>
</sec>
<sec id="s3_4">
<title>3.4 Diagnostic Value of High-Quality MRI <italic>vs.</italic> PET/CT on Per-Patient Analysis</title>
<p>In order to achieve the highest accuracy of MRI, we define a high-quality MRI which is referred to the MRI equipped with multisequence, DWI used, and &#x2265;2 imaging planes. High-quality MRI (1.5-T) has a sensitivity of 0.96 (95% CI, 0.90&#x2013;1.02) and a specificity of 0.90 (95% CI, 0.81&#x2013;0.99), while 3.0-T high-quality MRI has a sensitivity of 0.94 (95% CI, 0.86&#x2013;1.02) and a specificity of 0.94 (95% CI, 0.86&#x2013;1.02), respectively. Network meta-analysis demonstrates that 68Ga-PSMA PET/CT has the highest diagnostic value with the highest superiority index [4.56, (95% CI, 0.11&#x2013;11.00)], followed closely by 3.0-T high-quality MRI [4.43, (95% CI, 0.14&#x2013;11.00)], 1.5-T high-quality MRI [3.38, (95% CI, 0.11&#x2013;9.00)], 18F-NaF PET/CT, 11C-choline PET/CT, and 18F-choline PET/CT (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). The sensitivity analyses were performed by deleting studies involving only one test for detecting bone metastasis, and the results were stable (<xref ref-type="supplementary-material" rid="SF32">
<bold>Supplemental Table S32</bold>
</xref> and <xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4</bold>
</xref>).</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>PET/CT with different tracers and high-quality MRI to detect bone metastasis in PCa.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Test</th>
<th valign="top" align="center">Absolute Sensitivity</th>
<th valign="top" align="center">Absolute Specificity</th>
<th valign="top" align="center">Diagnostic OR[Rank]</th>
<th valign="top" align="center">Superiority Index [Rank]</th>
<th valign="top" align="center">Relative Sensitivity</th>
<th valign="top" align="center">Relative Specificity</th>
<th valign="top" align="center">Datasets, n</th>
<th valign="top" align="center">Studies, n</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">18F-NaF PET/CT</td>
<td valign="top" align="center">0.95<break/>(0.91&#x2013;0.99)</td>
<td valign="top" align="center">0.89<break/>(0.84&#x2013;0.94)</td>
<td valign="top" align="center">256.89<break/>(34.08&#x2013;796.33)<break/>[4]</td>
<td valign="top" align="center">1.08<break/>(0.11&#x2013;5.00)<break/>[4]</td>
<td valign="top" align="center">1.00<break/>(1.00&#x2013;1.00)</td>
<td valign="top" align="center">1.00<break/>(1.00&#x2013;1.00)</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">9</td>
</tr>
<tr>
<td valign="top" align="left">18F-Choline PET/CT</td>
<td valign="top" align="center">0.89<break/>(0.84&#x2013;0.94)</td>
<td valign="top" align="center">0.91<break/>(0.86&#x2013;0.96)</td>
<td valign="top" align="center">121.19<break/>(22.43&#x2013;355.84)<break/>[6]</td>
<td valign="top" align="center">0.51<break/>(0.09&#x2013;3.00)<break/>[6]</td>
<td valign="top" align="center">0.94<break/>(0.87&#x2013;1.01)</td>
<td valign="top" align="center">1.03<break/>(0.95&#x2013;1.11)</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">7</td>
</tr>
<tr>
<td valign="top" align="left">11C-Choline PET/CT</td>
<td valign="top" align="center">0.86<break/>(0.81&#x2013;0.91)</td>
<td valign="top" align="center">0.94<break/>(0.89&#x2013;0.99)</td>
<td valign="top" align="center">212.63<break/>(20.64&#x2013;791.67)<break/>[5]</td>
<td valign="top" align="center">0.75<break/>(0.09&#x2013;5.00)<break/>[5]</td>
<td valign="top" align="center">0.91<break/>(0.84&#x2013;0.98)</td>
<td valign="top" align="center">1.07<break/>(0.98&#x2013;1.16)</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">7</td>
</tr>
<tr>
<td valign="top" align="left">68Ga-PSMA PET/CT</td>
<td valign="top" align="center">0.91<break/>(0.83&#x2013;0.99)</td>
<td valign="top" align="center">0.96<break/>(0.87&#x2013;1.05)</td>
<td valign="top" align="center">4,633,299.15<break/>(14.51&#x2013;4,438,033.15)<break/>[1]</td>
<td valign="top" align="center">4.56<break/>(0.11&#x2013;11.00)<break/>[1]</td>
<td valign="top" align="center">0.96<break/>(0.86&#x2013;1.06)</td>
<td valign="top" align="center">1.09<break/>(0.96&#x2013;1.22)</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">5</td>
</tr>
<tr>
<td valign="top" align="left">3.0-T high-quality MRI</td>
<td valign="top" align="center">0.94<break/>(0.86&#x2013;1.02)</td>
<td valign="top" align="center">0.94<break/>(0.86&#x2013;1.02)</td>
<td valign="top" align="center">6033.33<break/>(27.09&#x2013;39,719.47)<break/>[2]</td>
<td valign="top" align="center">4.43<break/>(0.14&#x2013;11.00)<break/>[2]</td>
<td valign="top" align="center">0.99<break/>(0.89&#x2013;1.09)</td>
<td valign="top" align="center">1.07<break/>(0.96&#x2013;1.18)</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">4</td>
</tr>
<tr>
<td valign="top" align="left">1.5-T high-quality MRI</td>
<td valign="top" align="center">0.96<break/>(0.90&#x2013;1.02)</td>
<td valign="top" align="center">0.90<break/>(0.81&#x2013;0.99)</td>
<td valign="top" align="center">2,056.83<break/>(23.96&#x2013;11,589.08)<break/>[3]</td>
<td valign="top" align="center">3.38<break/>(0.11&#x2013;9.00)<break/>[3]</td>
<td valign="top" align="center">1.02<break/>(0.93&#x2013;1.11)</td>
<td valign="top" align="center">1.02<break/>(0.90&#x2013;1.14)</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">4</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data are reported as mean (range) unless otherwise indicated.</p>
</fn>
<fn>
<p>PET/CT, positron emission tomography/computed tomography; MRI, magnetic resonance imaging; PCa, Prostate cancer; NaF, Sodium fluoride; PSMA, Prostate membrane antigen; T, Tesla; CI credible interval; OR, odds ratio.</p>
</fn>
<fn>
<p>High-quality MRI was referred to the MRI equipped with multisequence, DWI used, and &#x2265;2 imaging planes.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Network meta-analysis results including sensitivity, specificity, and superiority index values of PET/CT with four commonly used tracers and high-quality MRI for the detection of bone metastasis in prostate cancer patients. Sensitivity, specificity, and superiority index are reported as mean (range) unless otherwise indicated. PET/CT, positron emission tomography/computed tomography; NaF, sodium fluoride; PSMA, prostate membrane antigen; DWI diffusion-weighted imaging; *high-quality MRI was referred to the MRI equipped with multi-sequence, DWI used, and &#x2265;2 imaging planes.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-11-736654-g004.tif"/>
</fig>
</sec>
<sec id="s3_5">
<title>3.5 Diagnostic Value of PET/CT <italic>vs.</italic> MRI on Per-Lesion Analysis</title>
<p>Regarding the tracers of the included studies, three studies used 18F-NaF, four selected 18F-choline, two applied 11C-choline, and one selected 68Ga-PSMA PET/CT. Only five studies reported MRI data. The pooled results demonstrated that 11C-choline PET/CT had the highest sensitivity of 0.84 (95% CI, 0.70&#x2013;0.98), and 18F-choline PET/CT has the highest specificity of 0.91 (95% CI, 0.83&#x2013;0.99). Network meta-analysis revealed that 11C-choline PET/CT had the highest diagnostic value with the highest superiority index, followed closely by 68Ga-PSMA PET/CT, 18F-choline PET/CT, MRI, and 18F-NaF PET/CT (<xref ref-type="supplementary-material" rid="SF33">
<bold>Supplementary eTable&#xa0;33</bold>
</xref>). Subgroup analysis based on the analysis of high-quality MRI (only 1.5-T, insufficient data for 3.0-T high-quality MRI) showed similar results (<xref ref-type="supplementary-material" rid="SF34">
<bold>Supplementary Table&#xa0;S34</bold>
</xref>).</p>
</sec>
</sec>
<sec id="s4">
<title>4 Discussion</title>
<p>&#x201c;In the current meta-analysis, we compared the diagnostic accuracy of MRI and PET/CT for the detection of bone metastasis in PCa. On patient-based level, network meta-analysis reveals that for numerous tracers, 68Ga-PSMA PET/CT has the highest superiority index, followed by 18F-NaF, 11C-choline, 18F-choline, 18F-FDG, and 18F-fluciclovine PET/CT; for the parameters of MRI, higher magnetic field strength, multisequence, more imaging planes, and MRI with DWI will increase the diagnostic value for bone metastasis in prostate cancer patients. Where available, 3.0-T high-quality MRI approaches 68Ga-PSMA PET/CT was performed in the detection of bone metastasis (sensitivity, 0.94 <italic>vs.</italic> 0.91; specificity, 0.94 <italic>vs.</italic> 0.96; superiority index, 4.43 <italic>vs.</italic> 4.56).</p>
<p>Given this hybrid method, as PET provides metabolic information and morphological imaging techniques offer anatomical data (<xref ref-type="bibr" rid="B27">27</xref>, <xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B87">87</xref>, <xref ref-type="bibr" rid="B88">88</xref>), a more accurate delineation of bone metastases is allowed. As well-known, the radioactive tracer is one of the most important cores of nuclear medicine imaging. Antoch et&#xa0;al. (<xref ref-type="bibr" rid="B28">28</xref>) demonstrated that the selection of the appropriate radioligands could increase accuracy when detecting micrometastases. Hence, we conducted the analysis based on the several popular tracers and found that 68Ga-PSMA PET/CT possessed the highest diagnostic value [superiority index, 7.30 (95% CI, 0.60&#x2013;11.00)]. Pyka et&#xa0;al. (<xref ref-type="bibr" rid="B89">89</xref>) performed a retrospective study and showed a higher sensitivity and specificity of 68Ga-PSMA PET/CT (100% and 100%) when compared to BS. Uslu-Besli et&#xa0;al. (<xref ref-type="bibr" rid="B86">86</xref>) conducted a cohort study of 28 patients and demonstrated that 68Ga-PSMA PET/CT changed management of seven patients by confirming the presence of bone metastasis, which was overlooked by BS in three patients and by excluding the false negative lesions on BS in four patients. Through binding to PSMA, a transmembrane protein expressed predominantly in prostate cells and especially in prostate cancer cells, 68Ga-PSMA leads to the internalization and accumulation in primary and metastatic cancer lesions (<xref ref-type="bibr" rid="B90">90</xref>), which is proven to have the higher yield of positive scans than obtained with other tracers at a low PSA level (<xref ref-type="bibr" rid="B91">91</xref>, <xref ref-type="bibr" rid="B92">92</xref>). However, the half-life and yield of 68Ga significantly limited the ability 68Ga-PSMA to meet the demand for imaging in PCa. To this regard, 18F-PSMA was considered as the ideal PET/CT tracer (<xref ref-type="bibr" rid="B93">93</xref>), but we were not able to analyze it because of the insufficient study.</p>
<p>Although the recommendation of the use of 18F-NaF PET/CT in PCa was refrained in guidelines, it is routinely used worldwide (<xref ref-type="bibr" rid="B94">94</xref>, <xref ref-type="bibr" rid="B95">95</xref>). Our results showed that 18F-NaF PET/CT had the highest sensitivity (0.95) but relative low specificity (0.88). The uptake of 18F-NaF, a bone-specific imaging radiotracer, is correlated to the blood flow and, especially, to the activity of local osteoblasts (<xref ref-type="bibr" rid="B30">30</xref>, <xref ref-type="bibr" rid="B96">96</xref>&#x2013;<xref ref-type="bibr" rid="B98">98</xref>), and the study by the National Oncologic PET Registry (NOPR) showed that the intended managements in approximately 44&#x2013;53% of prostate cancer patients had been significantly impacted by the application of 18F-Na PET/CT (<xref ref-type="bibr" rid="B99">99</xref>, <xref ref-type="bibr" rid="B100">100</xref>). However, 18F-NaF has the same shortcoming as the other bone-seeking agents, such as 99m-technetium used in BS, leading to confusion between benign lesion and metastases, and which creates more false positives and causes lower specificity (<xref ref-type="bibr" rid="B78">78</xref>, <xref ref-type="bibr" rid="B101">101</xref>). Choline is an essential component of the phospholipids, whose increase is associated with the high proliferation of prostate cancer cells, and both 11C-Choline and 18F-choline have been investigated in particular for the detection of relapse and metastasis of PCa (<xref ref-type="bibr" rid="B102">102</xref>&#x2013;<xref ref-type="bibr" rid="B104">104</xref>). Our results demonstrated that the urinary excretion of 18F-choline was slightly higher than 11C-Choline (superiority index, 1.92 <italic>vs.</italic> 1.71), which may affect the interpretation of findings in the pelvis and cause lower accuracy (<xref ref-type="bibr" rid="B105">105</xref>, <xref ref-type="bibr" rid="B106">106</xref>). Additionally, our study validated that 11C-Choline PET/CT processed the highest sensitivity&#x2013;specificity and superiority index on the per-lesion basis (preformed in a situation of the lack the data of 68GA-PMSA PET/CT). The commonly used tracer 18F-FDG appears to be less useful in PCa because of the low avidity of most prostate cancer cells and urinary activity (<xref ref-type="bibr" rid="B107">107</xref>). Osseous metastases in PCa are typically osteoblastic (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B108">108</xref>), while 18F-FDG are more sensitive in osteolytic lesions than in osteogenic lesions (<xref ref-type="bibr" rid="B72">72</xref>).</p>
<p>According to different factors (magnetic field strength, coverage, sequence used, the participation of DWI, and the number of imaging planes), MRI was grouped and directly compared to PET/CT using four commonly used radioligands (68Ga-PSMA, 18F-NaF, 11C-choline, and 18F-choline). 68Ga-PSMA PET/CT was still predominating, which was followed by the MRI equipped with better options. It is possible for MRI to detect metastasis lesion at an early stage owing to the high soft-tissue resolution (<xref ref-type="bibr" rid="B109">109</xref>&#x2013;<xref ref-type="bibr" rid="B111">111</xref>). Meanwhile, numerous clinical advantages of 3.0-T MRI over 1.5-T have been demonstrated (<xref ref-type="bibr" rid="B112">112</xref>), which was also confirmed in our analysis (sensitivity, 0.89 <italic>vs.</italic> 0.82; specificity, 0.88 <italic>vs.</italic> 0.92; superiority index, 1.76 <italic>vs.</italic> 0.77). However, Woo et&#xa0;al. (<xref ref-type="bibr" rid="B22">22</xref>) reported that there existed no significant heterogeneity among different magnetic field strength. Recently, DWI is of increasing interest for the detection of primary or metastatic cancers (<xref ref-type="bibr" rid="B113">113</xref>, <xref ref-type="bibr" rid="B114">114</xref>), benefiting from the ability to differentiate malignant from benign prostatic tissues according to different water diffusivity (<xref ref-type="bibr" rid="B68">68</xref>). Our analysis, according to whether DWI was involved, indicated the usefulness of DWI in the evaluation of bone metastases in PCa (sensitivity, 0.94 <italic>vs.</italic> 0.86; specificity, 0.93 <italic>vs.</italic> 0.86; superiority index, 3.63 <italic>vs.</italic> 1.44). A previous meta-analysis reported similar results that of improved diagnostic performance for identifying tumor foci in PCa due to the useful complement from DWI (<xref ref-type="bibr" rid="B115">115</xref>). Barchetti et&#xa0;al. (<xref ref-type="bibr" rid="B68">68</xref>) also revealed that conventional imaging, including T1-weighted (T1W), T1-weighted images (T2W), and short tau inversion recovery (STIR) sequence, may improve the specificity of DWI in detecting bone metastases. Regarding the coverage of MRI, whether a dedicated axial skeleton or whole-body MRI was utilized or only covered the pelvis, the results showed a similar performance (sensitivity, 0.44 <italic>vs.</italic> 0.82; specificity, 0.94 <italic>vs.</italic> 0.93). Although it is necessary to assess the extent of extra-prostatic extension, which is an independent prognostic factor (<xref ref-type="bibr" rid="B11">11</xref>), the possibility of distant bone metastasis without pelvic or lumbar spinal involvement is negligible (<xref ref-type="bibr" rid="B44">44</xref>, <xref ref-type="bibr" rid="B116">116</xref>). The motion-related signal intensity also decreases, leading to the failure in depicting lesions in ribs, sternum, and scapula (<xref ref-type="bibr" rid="B117">117</xref>, <xref ref-type="bibr" rid="B118">118</xref>), and the increased cost and acquisition time remain obstacles to the use of WB-MRI. However, the application of wider coverage enables the detection of extra-skeletal involvement, including lymph nodes (<xref ref-type="bibr" rid="B119">119</xref>, <xref ref-type="bibr" rid="B120">120</xref>), allows their monitoring under therapy, and helps to assess the efficacy of many new drugs in advanced PCa (<xref ref-type="bibr" rid="B121">121</xref>&#x2013;<xref ref-type="bibr" rid="B123">123</xref>). Additionally, the use of more imaging planes and sequences to determine bone metastases can achieve better diagnostic accuracy, attributed to the acquisition of more information. These analyses proved that the optimization of MRI parameters could significantly improve the diagnostic ability of bone metastasis in PCa.</p>
<p>The highlight of the current meta-analysis was to directly compare the high-quality MRI (multisequence, DWI involved, more imaging planes) with PET/CT using four commonly used tracers. The results showed that although the 3.0-T high-quality MRI did not surpass the 68Ga-PSMA PET/CT at the patient-based level, the diagnostic ability was very close (sensitivity, 0.94 <italic>vs.</italic> 0.91; specificity, 0.94 <italic>vs.</italic> 0.96; superiority index, 4.43 <italic>vs.</italic> 4.56). In recent decades, MRI and PET/CT compete for the single-step whole-body technique for assessing metastases and imaging of response to treatment in solid cancers. Lecouvet et&#xa0;al. (<xref ref-type="bibr" rid="B44">44</xref>) demonstrated that MRI was a highly sensitive and specific one-step modality to diagnose bone metastases in patients with high-risk PCa and leads to changes in treatment strategy in 22% of patients. Echmann et&#xa0;al. (<xref ref-type="bibr" rid="B43">43</xref>) proved the similarly high accuracy of 11C-Choline PET/CT and 1.5-T multiparametric WB-MRI, while Conde-Moreno et&#xa0;al. (<xref ref-type="bibr" rid="B124">124</xref>) found a significantly lower ability of DWI WB-MRI for detecting bone metastasis in recurrent PCa. Although previous studies have yielded inconsistent conclusions, our results demonstrated that 3.0-T multiparametric WB-MRI has a comparable high sensitivity, specificity, and superiority index at the patient-based level, following the 68Ga-PSMA PET/CT. Nevertheless, multiparametric WB-MRI seems to fulfill the requirements of no ionizing radiation and no intravenous injection of isotopes or any contrast medium, and it is also adept at depicting all metastatic bone lesions. T1W imaging can describe the infiltration of the bone marrow, T2W imaging has also been the mainstay of MRI due to its high tissue contrast resolution, while DWI can help discover the areas of increased cellularity and highly vascularized structures (<xref ref-type="bibr" rid="B71">71</xref>, <xref ref-type="bibr" rid="B125">125</xref>, <xref ref-type="bibr" rid="B126">126</xref>). Additionally, the efforts for standardization of prostate MRI acquisition and reporting (<xref ref-type="bibr" rid="B127">127</xref>, <xref ref-type="bibr" rid="B128">128</xref>), including MR Prostate Imaging Reporting and Data System (PI-RADS) (<xref ref-type="bibr" rid="B129">129</xref>) and the Prostate Diagnostic Imaging Consensus Meeting (PREDICT) (<xref ref-type="bibr" rid="B130">130</xref>), have further contributed to the use and emphasized the importance of interpreting MRI in the context of clinical features.</p>
<sec id="s4_1">
<title>4.1 Strengths and Limitations</title>
<p>The arm-based model is more appealing than traditional meta-analysis and the contrast-based model since the former not only permits more straightforward interpretation of the parameters, making use of all available data and yielding shorter credible intervals but also provides more natural variance&#x2013;covariance matrix structures. We adopted this model, which makes our results more convincing.</p>
<p>We adopted numerous statistical indicators including absolute sensitivity and specificity, relative sensitivity and specificity, diagnostic odds ratio (DOR), and superiority index to compare the diagnostic value of PET/CT and MRI with different parameter systematically. It is still a challenge to rank competing diagnostic tests especially when a test does not outperform the others on both sensitivity and specificity. DOR is commonly used in traditional meta-analysis, but it cannot distinguish between tests with high sensitivity but low specificity or vice versa. Deutsch et&#xa0;al. (<xref ref-type="bibr" rid="B131">131</xref>) introduced a superiority index to quantify the superiority of a diagnostic test. The superiority index is designed to consider the joint performance of the assessment measures. Corresponding weight is given to diagnostic tests based on their performance.</p>
<p>Our network meta-analysis included a total of 45 studies involving 2,843 patients and 4,263 lesions, which, to our knowledge, is the largest among similar studies. While the data in our study can be used to demonstrate numerical superiority of one imaging modality in terms of sensitivity, specificity, etc., they are even richer in that these variables can be compared across all nine imaging modalities simultaneously to assess their relative accuracy. Using this large amount of diagnostic data, we compared these imaging modalities at patient- and lesion-based level. Furthermore, a series of subgroup analyses were conducted to explore the potential influencing factors. Other subgroup analysis suggested a similar direction and magnitude of effect for studies investigating diagnostic value. We also performed sensitivity analyses by removing studies involving only one diagnostic test to confirm the stability of our results. Our results provide a comprehensive overview of the existing evidence on the imaging diagnosis of bone metastasis and have implications for clinicians, researchers, radiologists, and guideline committees. While many oncologists already consider PET/CT as the preferred method for detecting bone metastasis, however, this study provide formal quantification of the relative value of MRI.</p>
<p>This study is not without weakness. The first limitation was the lack of a well-accepted reference standard; all included studies used best value comparator (BVC) or predominantly BVC as the reference standard, which is according to a combination of clinical, laboratory, imaging, and follow-up studies (<xref ref-type="bibr" rid="B22">22</xref>). Although it is a more accurate reference standard to obtain pathological results by biopsy or surgery when determining bone metastasis, it is neither feasible nor ethical to conduct such further examinations solely. Second, patients were categorized at diagnosis to low or high risk based on clinical characteristics, such as PSA, Gleason score at biopsy, and clinical stage. It should be taken into account the potential value of the respective modalities according to the cancer grade and stage. For example, several studies reported that PSA values is strictly correlated with 11C-choline PET/CT sensitivity (<xref ref-type="bibr" rid="B102">102</xref>, <xref ref-type="bibr" rid="B132">132</xref>). Hence, the specific imaging strategies should be adopted for PCa with different risk (<xref ref-type="bibr" rid="B133">133</xref>&#x2013;<xref ref-type="bibr" rid="B135">135</xref>). Unfortunately, due to insufficient raw data, we are unable to do more subgroup analysis. Additionally, the same limitation made a comparison based on different metastatic sites difficult to accomplish. It is challenging for both PET/CT and MRI to diagnose rib metastases due to thoracic respiratory movements (<xref ref-type="bibr" rid="B103">103</xref>, <xref ref-type="bibr" rid="B136">136</xref>&#x2013;<xref ref-type="bibr" rid="B138">138</xref>), and further research on the diagnostic efficacy of different imaging modalities on thoracic sites will be interesting. Third, several other imaging modalities, such as BS, X-ray, BS/TXR, CT, PET, SPECT/CT, and especially, PET/MRI, a potentially disruptive technology synergizing PET and MRI, are also commonly used to assess bone metastasis. However, the purpose of this study was only to compare PET/CT and MRI in detecting bone metastasis in PCa; hence, caution is needed in applying our results to routine clinical practice. Fourth, studies assessing cost effectiveness should be performed to assist in making the clinical decision. In our study, insufficient data prevented us from assessing the economic effectiveness and social benefits systematically.</p>
</sec>
</sec>
<sec id="s5">
<title>5 Conclusion</title>
<p>This systematic review and network meta-analysis of diagnostic tests, which included 45 studies involving 2,843 patients and 4,263 lesions, indicates that 68Ga-PSMA PET/CT is recommended for the diagnosis of bone metastasis in prostate cancer patients. Where available, 3.0-T high-quality MRI approaches 68Ga-PSMA PET/CT should be performed the detection of bone metastasis.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data Availability Statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="s11">
<bold>Supplementary Material</bold>
</xref>. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author Contributions</title>
<p>FL, JD, and FX conceived and designed the project. FL, JD, and FX supervised the project. FL, JD, and FX performed the review and approval of the manuscript. FL, JD, DZ, YS, CY, RW, GW, JT, QY, BW, LL, JM, and FX contributed to the design of the study, writing the protocol, screening trials, data extraction, analysis and interpretation, and writing and final approval of the report. FL, JD, and FX generated the tables and figures. FL, JD, and FX assessed the quality of included trials. FL, JD, and FX performed the literature search. FL, JD, DZ, YS, CY, RW, GW, JT, QY, BW, LL, JM, and FX drafted the manuscript. FL, JD, DZ, YS, CY, RW, GW, JT, QY, BW, LL, JM, and FX participated in revising the manuscript before submission. FL, JD, DZ, YS, CY, RW, GW, JT, QY, BW, LL, JM, and FX participated in the formal revision, including data processing, statistical analysis, generating figures and tables, and text modification. All authors had full access to the data in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. FX is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>This study was supported by China Scholarship Council, Nos. 201808080126 and 201706920036; the National Natural Science Foundation of China, No. 81301556; and the Key R&amp;D program in Shandong Province, No. 2016GSF201214. The sponsors or funders had no involvements in any parts of this study.</p>
</sec>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s10" sec-type="disclaimer">
<title>Publisher&#x2019;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
</body>
<back>
<ack>
<title>Acknowledgments</title>
<p>We thank Chevalier Yan, Roland Klar, and Xiangyun Cheng, from Ludwig-Maximilians-Universit&#xe4;t M&#xfc;nchen for their helpful comments on this study. We thank Shen Lao, Hongxin Wang (Technische Universit&#xe4;t M&#xfc;nchen), and Daxia Feng (Department of Nuclear Medicine, Qilu Hospital, Shandong University) who provided valuable input on the manuscript. No compensation was received for these contributions.</p>
</ack>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fonc.2021.736654/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fonc.2021.736654/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="DataSheet_1.docx" id="SF1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;1</label>
<caption>
<p>Definitions of outcomes.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="DataSheet_1.docx" id="SF2" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;2</label>
<caption>
<p>Search strategy.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="DataSheet_1.docx" id="SF3" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;3</label>
<caption>
<p>The extraction process of diagnostic data.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF4" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;4</label>
<caption>
<p>The detail methods of QUADAS-2 score.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF5" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;5</label>
<caption>
<p>Implementation methods and process using arm-based model for network meta-analysis of diagnostic test accuracy data.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF6" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;6</label>
<caption>
<p>Main characteristics of MRI in the included studies.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF7" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;7</label>
<caption>
<p>Main characteristics of PET/CT in the included studies.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF8" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;8</label>
<caption>
<p>Diagnostic data of the included studies on patient-based level.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF9" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;9</label>
<caption>
<p>Diagnostic data of the included studies on lesion-based level.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF10" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;10</label>
<caption>
<p>The results of quality assessment of the included studies.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF11" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;11</label>
<caption>
<p>PET/CT with different tracers for detecting bone metastasis in patients with PCa by deleting studies with only one diagnostic test.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF12" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;12</label>
<caption>
<p>PET/CT with different tracers for detecting bone metastasis in patients with PCa by deleting studies with a QUADAS-2 score of 7.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF13" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;13</label>
<caption>
<p>PET/CT with different tracers for detecting bone metastasis in patients with PCa by deleting study with the maximum sample size.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF14" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;14</label>
<caption>
<p>PET/CT with different tracers for detecting bone metastasis in patients with PCa by deleting study with the minimum sample size.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF15" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;15</label>
<caption>
<p>PET/CT with different tracers for detecting bone metastasis in patients with PCa by detecting studies published before 2010.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF16" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;16</label>
<caption>
<p>PET/CT with different tracers for detecting bone metastasis in patients with PCa based on clinical settings of cancers.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF17" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;17</label>
<caption>
<p>PET/CT with different trances for detecting bone metastasis in patients with PCa based on numbers of patients.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF18" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;18</label>
<caption>
<p>PET/CT with different tracers for detecting bone metastasis in patients with PCa based on ages of patients.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF19" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;19</label>
<caption>
<p>PET/CT with different tracers for detecting bone metastasis in patients with PCa based on continents of studies.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF20" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;20</label>
<caption>
<p>PET/CT with different tracers for detecting bone metastasis in patients with PCa based on study design.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF21" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;21</label>
<caption>
<p>PET/CT with different tracers for detecting bone metastasis in patients with PCa based on evaluation methods of imaging analyses.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF22" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;22</label>
<caption>
<p>PET/CT with different tracers and MRI with different field strength for detecting bone metastasis in patients with PCa.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF23" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;23</label>
<caption>
<p>PET/CT with different tracers and MRI with different numbers of sequences for detecting bone metastasis in patients with PCa.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF24" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;24</label>
<caption>
<p>PET/CT with different tracers and MRI with/without DWI for detecting bone metastasis in patients with PCa.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF25" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;25</label>
<caption>
<p>PET/CT with different tracers and MRI with different numbers of imaging planes for detecting bone metastasis in patients with PCa.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF26" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;26</label>
<caption>
<p>PET/CT with different tracers and MRI with different coverage for detecting bone metastasis in patients with PCa.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF27" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;27</label>
<caption>
<p>PET/CT with different tracers and MRI with different field strength for detecting bone metastasis in patients with PCa by deleting studies with only one diagnostic test.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF28" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;28</label>
<caption>
<p>PET/CT with different tracers and MRI with different numbers of sequences for detecting bone metastasis in patients with PCa by deleting studies with only one diagnostic test.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF29" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;29</label>
<caption>
<p>PET/CT with different tracers and MRI with/without DWI for detecting bone metastasis in patients with PCa by deleting studies with only one diagnostic test.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF30" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;30</label>
<caption>
<p>PET/CT with different tracers and MRI with different numbers of imaging planes for detecting bone metastasis in patients with PCa by deleting studies with only one diagnostic test.</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="DataSheet_1.docx" id="SF31" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;31</label>
<caption>
<p>PET/CT with different tracers and MRI with different coverage for detecting bone metastasis in patients with PCa by deleting studies with only one diagnostic test.</p>
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  <supplementary-material xlink:href="DataSheet_1.docx" id="SF32" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;32</label>
<caption>
<p>PET/CT with different tracers and 3.0-T high-quality MRI for detecting bone metastasis in patients with PCa by deleting studies with only one diagnostic test.</p>
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  <supplementary-material xlink:href="DataSheet_1.docx" id="SF33" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;33</label>
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<p>PET/CT with different tracers and MRI for detecting bone metastasis in patients with PCa on lesion-based level.</p>
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  <supplementary-material xlink:href="DataSheet_1.docx" id="SF34" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
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<p>PET/CT with different tracers and 1.5-T high-quality MRI for detecting bone metastasis in patients with PCa on lesion-based level.</p>
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