<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Archiving and Interchange DTD v2.3 20070202//EN" "archivearticle.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="systematic-review" dtd-version="2.3" xml:lang="EN">
<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.2023.1208531</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Oncology</subject>
<subj-group>
<subject>Systematic Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Prognostic prediction by <sup>18</sup>F-FDG-PET/CT parameters in patients with neuroblastoma: a systematic review and meta-analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Hu</surname><given-names>Ruimin</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2279040"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname><given-names>Yan</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1519608"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname><given-names>Siying</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Lee</surname><given-names>Pamela</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/419679"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname><given-names>Chaohong</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/492364"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Liu</surname><given-names>Aiguo</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn001"><sup>*</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1668002"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology</institution>, <addr-line>Wuhan</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong</institution>, <addr-line>Hong Kong</addr-line>, <country>Hong Kong SAR, China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Pathogen Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology</institution>, <addr-line>Wuhan</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Andrea Di Cataldo, University of Catania, Italy</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Salvatore Annunziata, Fondazione Policlinico Universitario A. Gemelli IRCCS, Italy; Fuqiang Shao, Zigong First People&#x2019;s Hospital, China</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Aiguo Liu, <email xlink:href="mailto:drliuaiguo@163.com">drliuaiguo@163.com</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>14</day>
<month>07</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>13</volume>
<elocation-id>1208531</elocation-id>
<history>
<date date-type="received">
<day>19</day>
<month>04</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>06</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Hu, Zhang, Liu, Lee, Liu and Liu</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Hu, Zhang, Liu, Lee, Liu and Liu</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>Purpose</title>
<p>Neuroblastoma is a solid malignant tumor with high malignancy and high risk for metastasis. The prognosis of neuroblastoma ranges from spontaneous regression to insensitivity to therapies and widespread metastasis. There is a non-invasive, panoramic imaging technique called <sup>18</sup>F-fluorodeoxyglucose (<sup>18</sup>F-FDG) positron emission tomography&#x2013;computed tomography (PET/CT), which can provide both complete anatomical information <italic>via</italic> CT and extent of FDG uptake value in tumors <italic>via</italic> positron emission detection. PET/CT is a powerful approach to estimating tumoral metabolic activities, and PET/CT parameters have been demonstrated to be associated with the prognosis of various tumors. However, the predictive performance of PET/CT for the prognosis of neuroblastoma remains unclear. This meta-analysis aims to assess the predictive values of maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for progression-free survival (PFS), event-free survival (EFS), and overall survival (OS) in neuroblastoma patients.</p>
</sec>
<sec>
<title>Methods</title>
<p>Literature in PubMed, Embase, Cochrane Library, and Web of Science from January 1985 to June 2023 was searched for studies evaluating predictive values of PET/CT parameters for the prognosis of neuroblastoma. Search items mainly included &#x201c;Positron Emission Tomography Computed Tomography&#x201d; and &#x201c;Neuroblastoma&#x201d;. Hazard ratio (HR) was used as a pooled statistic to assess the association of SUVmax, MTV, and TLG with PFS, EFS, and OS in neuroblastoma patients. Heterogeneity test and sensitivity analysis were performed.</p>
</sec>
<sec>
<title>Results</title>
<p>There were eight studies included, with 325 participants. Meta-analysis showed that higher SUVmax was associated with shorter OS [HR = 1.27, 95% CI (1.11, 1.45), p = 0.001], while no association with PFS [HR = 1.03, 95% CI (0.99, 1.07), p = 0.222] and EFS [HR = 2.58, 95% CI (0.37, 18.24), p = 0.341] was presented. MTV showed no association with OS [HR = 2.46, 95% CI (0.34, 18.06), p = 0.376] and PFS [HR = 2.60, 95% CI (0.68, 9.88), p = 0.161]. There was a statistically significant association between TLG and OS [HR = 1.00, 95% CI (1.00, 1.00), p = 0.00], while the HR was 1, so the association could not be concluded, and TLG showed no association with PFS [HR = 1.00, 95% CI (0.99, 1.00), p&#xa0;=&#xa0;0.974].</p>
</sec>
<sec>
<title>Conclusion</title>
<p>High SUVmax indicates poor OS in patients with neuroblastoma. The MTV and TLG are potential prognostic predictors that need to be further validated by more well-designed studies.</p>
</sec>
<sec>
<title>Systematic review registration</title>
<p>
<uri xlink:href="https://www.crd.york.ac.uk/PROSPERO/">https://www.crd.york.ac.uk/PROSPERO/</uri>, identifier 340729.</p>
</sec>
</abstract>
<kwd-group>
<kwd>neuroblastoma</kwd>
<kwd>prognosis prediction</kwd>
<kwd><sup>18</sup>F-FDG-PET/CT</kwd>
<kwd>meta-analysis</kwd>
<kwd>SUVmax</kwd>
<kwd>MTV</kwd>
<kwd>TLG</kwd>
</kwd-group>
<counts>
<fig-count count="3"/>
<table-count count="1"/>
<equation-count count="0"/>
<ref-count count="27"/>
<page-count count="9"/>
<word-count count="3297"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Pediatric Oncology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Neuroblastoma (NB) is a solid malignant tumor that prevalently occurs in the extracranial sympathetic nervous system in children (<xref ref-type="bibr" rid="B1">1</xref>). It accounts for approximately 15% of pediatric cancer fatalities due to its high malignancy and high risk for metastasis (<xref ref-type="bibr" rid="B2">2</xref>). Despite advances in multi-modal therapies including dose-intensive and myeloablative therapy with hematopoietic stem cell support, radiotherapy, and immunotherapy, the survival of children with metastatic neuroblastoma remains poor (International Neuroblastoma Risk Group Staging System [INRGSS] Stage M), with a 3-year event-free survival of 60% (<xref ref-type="bibr" rid="B3">3</xref>). The prognosis of neuroblastoma varies from individual to individual, ranging from spontaneous regression to insensitivity to therapies and widespread metastasis (<xref ref-type="bibr" rid="B4">4</xref>). Accurate predictors would be of great significance for risk stratification and individualized management for neuroblastoma patients so as to improve their prognosis.</p>
<p><sup>18</sup>F-Fluorodeoxyglucose (<sup>18</sup>F-FDG) positron emission tomography/computed tomography (PET/CT) is a non-invasive, panoramic imaging technique that can provide complete anatomical information <italic>via</italic> CT and detect the extent of FDG uptake in primary tumors and metastases (<xref ref-type="bibr" rid="B5">5</xref>). Maximum standardized uptake value (SUVmax) is the most commonly used PET/CT parameter for the estimation of tumoral metabolic activities, which has been demonstrated to be associated with the prognosis of various tumors. Several volumetric imaging parameters based on <sup>18</sup>F-FDG PET/CT, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG), have also been recommended as prognostic factors for various tumors (<xref ref-type="bibr" rid="B6">6</xref>&#x2013;<xref ref-type="bibr" rid="B10">10</xref>). For example, TLG with a cutoff value of 443.8 is significantly associated with the overall survival (OS) of patients with small cell lung cancer (<xref ref-type="bibr" rid="B6">6</xref>). A study has shown that SUVmax is significantly associated with modified Bloom-Richardson (MBR) grades in patients with triple-negative breast cancer (TNBC) (<xref ref-type="bibr" rid="B7">7</xref>). It has been reported that patients with high SUVmax often have poorer survival outcomes (<xref ref-type="bibr" rid="B7">7</xref>). A meta-analysis has indicated that SUVmax measured before treatment and its metabolic response after treatment are of predictive value for the long-term survival of head and neck cancer (<xref ref-type="bibr" rid="B8">8</xref>). Another two meta-analyses have concluded that high SUVmax, MTV, and TLG indicate a higher risk for recurrence or death in patients with pancreatic carcinoma (<xref ref-type="bibr" rid="B9">9</xref>) and patients with surgical non-small cell lung cancer (<xref ref-type="bibr" rid="B10">10</xref>). Despite the increasing application of <sup>18</sup>F-FDG PET/CT in pediatric neuroblastoma for diagnosis, staging, and prognosis prediction (<xref ref-type="bibr" rid="B11">11</xref>&#x2013;<xref ref-type="bibr" rid="B14">14</xref>), the consistency of SUVmax and volumetric PET parameters remains elusive in prognosis prediction of neuroblastoma. Therefore, we have conducted this systematic review and meta-analysis to assess the predictive values of <sup>18</sup>F-FDG PET/CT-based metabolic parameters for survival outcomes in patients with neuroblastoma.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<p>This study is conducted in strict accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (<xref ref-type="bibr" rid="B15">15</xref>).</p>
<sec id="s2_1">
<title>Literature search and study selection</title>
<p>PubMed, Embase, Web of Science, and Cochrane Library were searched from January 1985 to June 2023 for relevant studies, with language restriction to English. Search items mainly contained the following: (&#x201c;Neuroblastoma&#x201d; or &#x201c;Neuroblastomas&#x201d;) and (&#x201c;Positron Emission Tomography Computed Tomography&#x201d;). The detailed search strategy is shown in the <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Material</bold></xref>.</p>
<p>Studies meeting the following criteria were included: observational study (prospective and retrospective) or clinical trial that applied <sup>18</sup>F-FDG PET/CT and relevant parameters (SUVmax, MTV, and TLG) in NB patients and reported survival data, such as OS, progression-free survival (PFS), and event-free survival (EFS).</p>
<p>Literature review, conference summary, case report, and editorial materials were excluded.</p>
<p>Literature search and study selection were conducted by two reviewers independently, and disagreements were settled <italic>via</italic> discussion.</p>
</sec>
<sec id="s2_2">
<title>Quality assessment and data extraction</title>
<p>Quality assessment of included studies was performed by two reviewers independently using the Quality in Prognostic Studies (QUIPS) tool (<xref ref-type="bibr" rid="B16">16</xref>) <italic>via</italic> Review Manager 5.4 software. QUIPS contains six domains: study participation, study attrition, measurement of prognostic factors, measurement of outcome, study confounding, and statistical analysis and reporting. Disagreements were settled <italic>via</italic> discussion.</p>
<p>Data were extracted independently by two reviewers using a pre-designed form that included the following: name of the first author, publication date, sample size, country, study design, characteristics of participants (gender distribution, tumor grade, tumor site, treatment after PET/CT scans, volumes of interest (VOIs) for recording SUVmax, and reported survival), PET parameters, and cutoff values of parameters.</p>
</sec>
<sec id="s2_3">
<title>Statistical analysis</title>
<p>The primary endpoint was OS, defined as the time interval from the initiation of treatment to all-cause death. The secondary outcome was PFS, referring to recurrence-free survival and the time interval from the date of treatment initiation to tumoral recurrence or metastasis. EFS was calculated from diagnosis to the first occurrence of relapse, progression, secondary malignancy, death, or the last contact if no event occurred. Hazard ratio (HR) was applied as the statistic for the association of SUVmax, MTV, or TLG with PFS, EFS, and OS. PFS, EFS, or OS data were extracted using methods mentioned previously (<xref ref-type="bibr" rid="B17">17</xref>). Univariate or multivariate HR with a 95% confidence interval (95% CI) were extracted from each study if provided; otherwise, Engauge Digitizer would be applied (<ext-link ext-link-type="uri" xlink:href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</ext-link>) to estimate the survival rate through Kaplan&#x2013;Meier curve and reconstruct HR estimate and its variance, assuming that patients were censored at a constant rate during the follow-up. A heterogeneity test was performed using chi-square (&#x3c7;<sup>2</sup>) test and I<sup>2</sup>statistic (<xref ref-type="bibr" rid="B18">18</xref>). I<sup>2</sup> less than 50% with a p-value not less than 0.1 indicated no significant heterogeneity among the studies, and a fixed-effects model would be applied; otherwise (I<sup>2</sup> greater than 50% with a p-value less than 0.1), a random-effects model would be applied. Meanwhile, sensitivity analysis was performed by removing each included study one by one to assess the robustness of the results. Statistical analysis was performed using Stata Version 16.0 (College Station, TX, USA). A p-value less than 0.05 indicated statistical significance.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Characteristics of included studies</title>
<p>The flow diagram of the study selection process is presented in <xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>.&#xa0;A total of eight studies, involving 325 participants, were included, among which seven studies (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B19">19</xref>&#x2013;<xref ref-type="bibr" rid="B24">24</xref>) were retrospective design and one study (<xref ref-type="bibr" rid="B25">25</xref>) was prospective. According to the INRGSS grade, one study (<xref ref-type="bibr" rid="B25">25</xref>) only recruited patients with stage IV neuroblastoma; one study recruited those at stages I, II, and IV (<xref ref-type="bibr" rid="B20">20</xref>); four studies recruited patients at all grades (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B24">24</xref>); the remaining two studies failed to clearly describe the grading of the patients (<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>). There were six studies that included neuroblastoma originating in the adrenal glands, retroperitoneum, and mediastinum (<xref ref-type="bibr" rid="B19">19</xref>&#x2013;<xref ref-type="bibr" rid="B24">24</xref>), and the other two studies (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B25">25</xref>) failed to clearly state the tumor sites. The characteristics of included studies are shown in <xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>. All the studies used <sup>18</sup>F-FDG for PET imaging, among which seven studies reported SUVmax (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B21">21</xref>&#x2013;<xref ref-type="bibr" rid="B25">25</xref>), four studies reported MTV and TLG (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B24">24</xref>), four studies reported the predictive value of SUVmax for OS (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B25">25</xref>), three studies reported association of SUVmax with PFS (or recurrence-free survival) (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B22">22</xref>), two studies reported association of SUVmax with EFS (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B24">24</xref>), two studies reported association of MTV and TLG with OS (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>), and three studies reported the predictive value of MTV and TLG for PFS (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>). One study (<xref ref-type="bibr" rid="B19">19</xref>) provided spheroid-shaped VOI for the primary tumor lesion and metastatic lesions of each patient to evaluate FDG uptake of neuroblastoma lesions, and SUVmax in each VOI was measured, while another study measured SUVmax in VOI for the most intense lesion (<xref ref-type="bibr" rid="B25">25</xref>). The cutoff value of SUVmax ranged from 3.31 to 12.01, and those of MTV in two studies (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>) were 88.1 and 191 cm<sup>3</sup>, respectively. The cutoff values of TLG in two studies (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B5">5</xref>) were 1,045.2 and 341.41&#xa0;g, respectively.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>PRISMA flow diagram of the study process. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-13-1208531-g001.tif"/>
</fig>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Characteristics of the included study.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">No.</th>
<th valign="top" align="left">Study</th>
<th valign="top" align="left">Year</th>
<th valign="top" align="left">Patient no.</th>
<th valign="top" align="left">Country</th>
<th valign="top" align="left">Study design</th>
<th valign="top" align="left">Male (%)</th>
<th valign="top" align="left">Stage</th>
<th valign="top" align="left">Tumor location</th>
<th valign="top" align="left">Treatment</th>
<th valign="top" align="left">VOI for recording SUVmax</th>
<th valign="top" align="left">Studied PET parameters</th>
<th valign="top" align="left">Endpoint</th>
<th valign="top" align="left">Cutoff value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">1</td>
<td valign="top" align="left">Nikolaos</td>
<td valign="top" align="left">2011</td>
<td valign="top" align="left">28</td>
<td valign="top" align="left">The United Kingdom</td>
<td valign="top" align="left">Prospective</td>
<td valign="top" align="left">57.14</td>
<td valign="top" align="left">IV</td>
<td valign="top" align="left">(&#x2013;)</td>
<td valign="top" align="left">High-dose <sup>131</sup>I-MIBG and topotecan</td>
<td valign="top" align="left">Most intense lesion</td>
<td valign="top" align="left"><sup>18</sup>F-FDG uptake pattern, SUVmax, <sup>18</sup>F-FDG skeletal extent score</td>
<td valign="top" align="left">OS</td>
<td valign="top" align="left">SUVmax = 5.3</td>
</tr>
<tr>
<td valign="top" align="left">2</td>
<td valign="top" align="left">Lee</td>
<td valign="top" align="left">2015</td>
<td valign="top" align="left">50</td>
<td valign="top" align="left">South Korea</td>
<td valign="top" align="left">Retrospective</td>
<td valign="top" align="left">68</td>
<td valign="top" align="left">I&#x2013;IVs</td>
<td valign="top" align="left">Adrenal gland:retroperitoneum:mediastinum = 36: 9: 5</td>
<td valign="top" align="left">Chemotherapy, surgical resection, PBSCT, radiotherapy, I-131 MIBG</td>
<td valign="top" align="left">Primary tumor lesions and metastatic lesions</td>
<td valign="top" align="left">Pmax, Tmax, Tmax/Lmean</td>
<td valign="top" align="left">OS, PFS</td>
<td valign="top" align="left">SUVmax = 4</td>
</tr>
<tr>
<td valign="top" align="left">3</td>
<td valign="top" align="left">Li</td>
<td valign="top" align="left">2018</td>
<td valign="top" align="left">47</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">Retrospective</td>
<td valign="top" align="left">68.09</td>
<td valign="top" align="left">I, II, IV</td>
<td valign="top" align="left">Adrenal:paraspinal:periaortic regions = 16:2:29</td>
<td valign="top" align="left">(-)</td>
<td valign="top" align="left">Primary tumor lesion</td>
<td valign="top" align="left">SUVmax,MTV, TLG, BMU patterns</td>
<td valign="top" align="left">RFS, OS</td>
<td valign="top" align="left">SUVmax = 4.15; TLG = 10,454.2 g; MTV = 88.1 cm<sup>3</sup>
</td>
</tr>
<tr>
<td valign="top" align="left">4</td>
<td valign="top" align="left">Liu</td>
<td valign="top" align="left">2017</td>
<td valign="top" align="left">25</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">Retrospective</td>
<td valign="top" align="left">80</td>
<td valign="top" align="left">I&#x2013;IV</td>
<td valign="top" align="left">(-)</td>
<td valign="top" align="left">(-)</td>
<td valign="top" align="left">Primary tumor lesion</td>
<td valign="top" align="left">SUVmax,MTV, TLG</td>
<td valign="top" align="left">OS, PFS</td>
<td valign="top" align="left">(-)</td>
</tr>
<tr>
<td valign="top" align="left">5</td>
<td valign="top" align="left">Liu</td>
<td valign="top" align="left">2016</td>
<td valign="top" align="left">42</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">Retrospective</td>
<td valign="top" align="left">66.67</td>
<td valign="top" align="left">I&#x2013;IVs</td>
<td valign="top" align="left">Adrenal : RP/Med = 29: 11/2</td>
<td valign="top" align="left">(-)</td>
<td valign="top" align="left">Primary tumor lesion</td>
<td valign="top" align="left">SUVmax</td>
<td valign="top" align="left">OS, EFS</td>
<td valign="top" align="left">SUVmax = 3.31</td>
</tr>
<tr>
<td valign="top" align="left">6</td>
<td valign="top" align="left">Man</td>
<td valign="top" align="left">2021</td>
<td valign="top" align="left">40</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">Retrospective</td>
<td valign="top" align="left">47.5</td>
<td valign="top" align="left">(-)</td>
<td valign="top" align="left">Retroperitoneal:mediastinal:other = 31:7:2</td>
<td valign="top" align="left">Comprehensive treatment</td>
<td valign="top" align="left">Primary tumor lesion</td>
<td valign="top" align="left">SUVmax, MTV, TLG</td>
<td valign="top" align="left">OS, PFS</td>
<td valign="top" align="left">SUVmax = 12.01; TLG = 341.41&#xa0;g; MTV = 191 cm<sup>3</sup>
</td>
</tr>
<tr>
<td valign="top" align="left">7</td>
<td valign="top" align="left">Sung</td>
<td valign="top" align="left">2020</td>
<td valign="top" align="left">55</td>
<td valign="top" align="left">The USA</td>
<td valign="top" align="left">Retrospective</td>
<td valign="top" align="left">52.7</td>
<td valign="top" align="left">(-)</td>
<td valign="top" align="left">Adrenal:extra-adrenal = 33: 22</td>
<td valign="top" align="left">(-)</td>
<td valign="top" align="left">Primary tumor lesion</td>
<td valign="top" align="left">SUVmax, SUVmax/SUVliver, SUVmean</td>
<td valign="top" align="left">OS</td>
<td valign="top" align="left">SUVmax = 4.77</td>
</tr>
<tr>
<td valign="top" align="left">8</td>
<td valign="top" align="left">Liu</td>
<td valign="top" align="left">2022</td>
<td valign="top" align="left">38</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">Retrospective</td>
<td valign="top" align="left">36.8</td>
<td valign="top" align="left">I&#x2013;IVs</td>
<td valign="top" align="left">Abdomen:non-abdomen = 34:4</td>
<td valign="top" align="left">Chemotherapy, surgery</td>
<td valign="top" align="left">Primary tumor lesion</td>
<td valign="top" align="left">SUVmax, MTV, TLG</td>
<td valign="top" align="left">EFS</td>
<td valign="top" align="left">(-)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>SUVmax, maximum standardized uptake value; MTV, metabolic tumor volume; TLG, total lesion glycolysis; PBSCT, autologous peripheral blood stem cell transplantation; VOI, volume of interest; BMU patterns, bone marrow uptake patterns; Pmax, the SUVmax of the primary tumor lesion; Tmax, the SUVmax of all the tumor lesions including the primary tumor lesion and metastatic lesions; Tmax/Lmean, the uptake ratio of Tmax to mean SUV of normal liver tissue; OS, overall survival; PFS, progression-free survival; RFS, recurrence-free survival; EFS, event-free survival.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<title>Quality assessment</title>
<p>There were four studies (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B21">21</xref>&#x2013;<xref ref-type="bibr" rid="B23">23</xref>) that were graded as unclear in selection bias due to no description of consecutive selection for participants, one study (<xref ref-type="bibr" rid="B13">13</xref>) was graded as high selection bias due to limited sample size, and one study (<xref ref-type="bibr" rid="B25">25</xref>) was graded as high selection due to recruitment of only stage IV patients. All the studies were graded as low risk of attrition bias. There was one study graded as unclear risk of bias in prognostic factor measurement (<xref ref-type="bibr" rid="B23">23</xref>) because it failed to state the participation of two experienced nuclear medicine physicians in the measurement. There were five studies (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B19">19</xref>&#x2013;<xref ref-type="bibr" rid="B22">22</xref>) graded as having unclear risk of bias in outcome measurement due to no description of detailed methods for measurement. There were four studies (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B25">25</xref>) graded as high risk in confounding bias due to the lack of multivariate analysis and one study (<xref ref-type="bibr" rid="B22">22</xref>) due to an unclear risk because it performed both multivariate analysis and univariate survival analysis. In terms of statistical analysis and reporting, six studies (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B19">19</xref>&#x2013;<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B25">25</xref>) were graded as high risk of bias in that these studies failed to provide the HRs of non-significant factors. The overall quality of included studies was moderate (<xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2</bold></xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p><bold>(A)</bold> QUIPS risk of bias graph: the judgments about each risk of bias domain are presented as percentages across all included studies (n = 8). <bold>(B)</bold> Summary of quality assessment of individual studies according to Quality in Prognostic Studies (QUIPS).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-13-1208531-g002.tif"/>
</fig>
</sec>
<sec id="s3_3">
<title>Predictive value of SUVmax, MTV, and TLG on PFS, EFS, and OS</title>
<p>There were four studies that reported an association of SUVmax with OS (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B25">25</xref>). No significant heterogeneity was considered among the studies (I<sup>2&#xa0;=&#xa0;</sup>1.5%), followed by a fixed-effects model applied. Meta-analysis showed that the value of SUVmax was negatively associated with the OS of NB patients [HR = 1.27, 95% CI (1.11, 1.45), p = 0.001] (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3A</bold></xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Forest plot results of the OS based on SUVmax <bold>(A)</bold>, MTV <bold>(C)</bold>, and TLG <bold>(E)</bold>; PFS based on SUVmax <bold>(B)</bold>, MTV <bold>(D)</bold>, and TLG <bold>(F)</bold>; and EFS based on SUVmax <bold>(G)</bold>. OS, overall survival; SUVmax, maximum standardized uptake value; MTV, metabolic tumor volume; TLG, total lesion glycolysis; PFS, progression-free survival; EFS, event-free survival.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-13-1208531-g003.tif"/>
</fig>
<p>There were three studies that reported the association of SUVmax with PFS (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B22">22</xref>). No significant heterogeneity was considered among the studies (I<sup>2&#xa0;=&#xa0;</sup>46.8%, p = 0.153), followed by the fixed-effects model applied. Meta-analysis showed no significant association between SUVmax and the PFS of NB patients [HR = 1.03, 95% CI (0.99, 1.07), p = 0.222] (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3B</bold></xref>).</p>
<p>There were two studies that reported the association of MTV with OS (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>). Significant heterogeneity was observed (I<sup>2&#xa0;</sup>=<sup>&#xa0;</sup>88.1%, p = 0.004), so a random-effects model was used. Meta-analysis showed no significant association between MTV and the OS of NB patients [HR = 2.46, 95% CI (0.34, 18.06), p = 0.376] (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3C</bold></xref>).</p>
<p>There were three studies that reported an association of MTV with PFS (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>). Significant heterogeneity was observed (I<sup>2&#xa0;</sup>=<sup>&#xa0;</sup>85.8%, p = 0.001), and a random-effects model was used. Meta-analysis showed no significant association between MTV and the PFS of NB patients [HR = 2.60, 95% CI (0.68, 9.88), p = 0.161] (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3D</bold></xref>).</p>
<p>There were two studies that reported the association of TLG with OS (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>). No significant heterogeneity was considered among the studies (I<sup>2&#xa0;</sup>=<sup>&#xa0;</sup>43.4%, p = 0.184), and a fixed-effects model was applied. Meta-analysis showed that TLG was significantly associated with the OS of NB patients [HR = 0.99, 95% CI (0.99, 0.99), p = 0.00] (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3E</bold></xref>).</p>
<p>There were three studies that reported the association of TLG with PFS (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>). Significant heterogeneity was observed (I<sup>2&#xa0;</sup>=<sup>&#xa0;</sup>87.6%, p = 0.000), and a random-effects model was applied. Meta-analysis showed no significant association between TLG and the PFS of NB patients [HR = 1.00, 95% CI (1.00, 1.00), p = 0.974] (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3F</bold></xref>).</p>
<p>There were two studies that reported the association of SUVmax with EFS (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B24">24</xref>). Significant heterogeneity was observed (I<sup>2&#xa0;</sup>=<sup>&#xa0;</sup>86.2%, p = 0.007), so a random-effects model was used. Meta-analysis showed no significant association between SUVmax and the EFS of NB patients [HR = 2.58, 95% CI (0.37, 18.24), p = 0.341] (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3G</bold></xref>).</p>
</sec>
<sec id="s3_4">
<title>Sensitivity analysis</title>
<p>A sensitivity analysis was performed (<xref ref-type="supplementary-material" rid="SF1"><bold>Supplementary Figure S1</bold></xref>) to assess the robustness of the results. Since the research data on OS based on MTV, EFS based on SUVmax, and OS based on TLG are relatively small, only sensitivity analysis was performed on OS based on SUVmax, PFS based on SUVmax, PFS based on MTV, and PFS based on TLG. Among studies of SUVmax on OS, the combined HRs were found to be stable, suggesting that no individual study significantly affected the results (<xref ref-type="supplementary-material" rid="SF1"><bold>Supplementary Figure S1A</bold></xref>). Of all studies of SUVmax on PFS, one study (<xref ref-type="bibr" rid="B22">22</xref>) had a great impact on the results. After this study was excluded, the combined HR was far larger than before. As for studies of MTV on PFS, the combined HRs were also found to be stable, indicating that no individual study significantly affected the results. Among studies of TLG on PFS, after excluding one study (<xref ref-type="bibr" rid="B13">13</xref>), the value of HR remained unchanged. This indicated that this study had no effect on the results.</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>In this meta-analysis, we have found that a higher SUVmax value of <sup>18</sup>F-FDG PET/CT would be associated with a higher mortality risk in patients with neuroblastoma, while its predictive performance for PFS and EFS still needs to be further validated. MTV and TLG present no predictive significance for either the PFS or OS of those patients.</p>
<p>SUVmax is the most commonly used <sup>18</sup>F-FDG PET/CT parameter for disease diagnosis and treatment response monitoring due to its high repeatability and availability. In our review, four HRs regarding SUVmax on OS were combined. SUVmax has been shown to be of predictive effect despite the thresholds of SUVmax varying among the studies. No significant heterogeneity was observed among the four studies (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B25">25</xref>), and sensitivity analysis indicated the robustness of the results. However, the predictive effect of SUVmax for PFS and EFS could not be concluded, which might be attributed to insufficient data, limited number of included studies, and varied methods for outcome measurement.</p>
<p>This study has revealed that MTV was not superior to SUVmax regarding the prediction of PFS and OS, which could be explained by several reasons. First, the three included studies (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>) regarding MTV had recruited too few patients to produce conclusive results. Then, MTV represents the size of tumor tissues that exhibit active <sup>18</sup>F-FDG uptake, which makes it unreliable and unrepeatable, especially for multiple, disseminated, and extensive lesions. Moreover, there is a lack of standardized measuring procedures for estimating MTV thresholds. Chao Li et&#xa0;al. (<xref ref-type="bibr" rid="B20">20</xref>) and Chia-Ju Liu (<xref ref-type="bibr" rid="B13">13</xref>) have estimated MTV thresholds based on 40% of the SUVmax, whereas Shuai Man et&#xa0;al. (<xref ref-type="bibr" rid="B22">22</xref>) have used 42% of the SUVmax. Using a proportion of the SUVmax as a threshold may lead to a misestimation of the calculated tumor volume in cases of heterogeneous or low uptake. There is a study reporting that an individualized threshold based on the liver background could reduce the impact of different scanning techniques on solid tumor-associated indicators (<xref ref-type="bibr" rid="B26">26</xref>). Thus, a standardized measuring method for MTV is needed for more accurate assessments in patients with neuroblastoma.</p>
<p>TLG is an ideal metabolic parameter that combines the mean SUV value and MTV to assess tumor volume and metabolism. This study has yielded results inconsistent with those of previous studies (<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B27">27</xref>), which have demonstrated the predictive value of TLG for patients with pancreatic carcinoma or extranodal natural killer/T-cell lymphoma. The possible reasons might be related to the limited number of studies included and the TLG calculations and estimation subjecting SUVmax and MTV, which are affected by MTV measurement methods.</p>
<p>This study has several limitations. First, there are insufficient data to properly assess the predictive performance of MTV and TLG for the patients&#x2019; prognosis, and some of the studies have only performed univariate analysis leading to potential confounding factors in their results. Second, most of the included studies were retrospective, with moderate methodological qualities. Finally, variances exist in study design, imaging analysis, cutoff value, and inclusion and exclusion criteria for patient recruitment among the included studies, which might lead to heterogeneity.</p>
<p>More well-designed studies with larger samples would be needed for further assessment.</p>
</sec>
<sec id="s5" sec-type="conclusion">
<title>Conclusion</title>
<p>The SUVmax of <sup>18</sup>F-FDG PET/CT is of significant predictive effect on the prognosis of neuroblastoma patients. A high SUVmax is associated with a poorer survival prognosis in neuroblastoma patients. In the future, the SUVmax of <sup>18</sup>F-FDG PET/CT could be used as a predictor for prognosis in patients with neuroblastoma.</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="SM1"><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>All authors contributed to the study&#x2019;s conception and design. RH and YZ: conceptualization, methodology, software, writing&#x2014;original draft, data curation, and visualization. SL and PL: investigation, writing&#x2014;original draft, and writing&#x2014;reviewing and editing. CL: methodology, software, and writing&#x2014;original draft. AL: conceptualization, supervision, project administration, and funding acquisition. All authors read and approved the final manuscript.</p>
</sec>
</body>
<back>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>This work was supported by the National Natural Science Foundation of China (Nos. 81874187 and 81472706).</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>
<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.2023.1208531/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fonc.2023.1208531/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
<supplementary-material xlink:href="Image_1.tif" id="SF1" mimetype="image/tiff">
<label>Supplementary Figure S1</label>
<caption>
<p>Results of sensitivity analysis in OS based on SUVmax <bold>(A)</bold>, PFS based on SUVmax <bold>(B)</bold>, MTV <bold>(C)</bold>, TLG <bold>(D)</bold>.</p>
</caption>
</supplementary-material>
</sec>
<ref-list>
<title>References</title>
<ref id="B1">
<label>1</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Whittle</surname> <given-names>SB</given-names>
</name>
<name>
<surname>Smith</surname> <given-names>V</given-names>
</name>
<name>
<surname>Doherty</surname> <given-names>E</given-names>
</name>
<name>
<surname>Zhao</surname> <given-names>S</given-names>
</name>
<name>
<surname>McCarty</surname> <given-names>S</given-names>
</name>
<name>
<surname>Zage</surname> <given-names>PE</given-names>
</name>
</person-group>. <article-title>Overview and recent advances in the treatment of neuroblastoma</article-title>. <source>Expert Rev Anticancer Ther</source> (<year>2017</year>) <volume>17</volume>(<issue>4</issue>):<page-range>369&#x2013;86</page-range>. doi: <pub-id pub-id-type="doi">10.1080/14737140.2017.1285230</pub-id>
</citation>
</ref>
<ref id="B2">
<label>2</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Ward</surname> <given-names>E</given-names>
</name>
<name>
<surname>DeSantis</surname> <given-names>C</given-names>
</name>
<name>
<surname>Robbins</surname> <given-names>A</given-names>
</name>
<name>
<surname>Kohler</surname> <given-names>B</given-names>
</name>
<name>
<surname>Jemal</surname> <given-names>A</given-names>
</name>
</person-group>. <article-title>Childhood and adolescent cancer statistics, 2014</article-title>. <source>CA Cancer J Clin</source> (<year>2014</year>) <volume>64</volume>(<issue>2</issue>):<fpage>83</fpage>&#x2013;<lpage>103</lpage>. doi: <pub-id pub-id-type="doi">10.3322/caac.21219</pub-id>
</citation>
</ref>
<ref id="B3">
<label>3</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Park</surname> <given-names>JR</given-names>
</name>
<name>
<surname>Kreissman</surname> <given-names>SG</given-names>
</name>
<name>
<surname>London</surname> <given-names>WB</given-names>
</name>
<name>
<surname>Naranjo</surname> <given-names>A</given-names>
</name>
<name>
<surname>Cohn</surname> <given-names>SL</given-names>
</name>
<name>
<surname>Hogarty</surname> <given-names>MD</given-names>
</name>
<etal/>
</person-group>. <article-title>Effect of tandem autologous stem cell transplant vs single transplant on event-free survival in patients with high-risk neuroblastoma: a randomized clinical trial</article-title>. <source>JAMA</source> (<year>2019</year>) <volume>322</volume>(<issue>8</issue>):<page-range>746&#x2013;55</page-range>. doi: <pub-id pub-id-type="doi">10.1001/jama.2019.11642</pub-id>
</citation>
</ref>
<ref id="B4">
<label>4</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Swift</surname> <given-names>CC</given-names>
</name>
<name>
<surname>Eklund</surname> <given-names>MJ</given-names>
</name>
<name>
<surname>Kraveka</surname> <given-names>JM</given-names>
</name>
<name>
<surname>Alazraki</surname> <given-names>AL</given-names>
</name>
</person-group>. <article-title>Updates in diagnosis, management, and treatment of neuroblastoma</article-title>. <source>Radiographics</source> (<year>2018</year>) <volume>38</volume>(<issue>2</issue>):<page-range>566&#x2013;80</page-range>. doi: <pub-id pub-id-type="doi">10.1148/rg.2018170132</pub-id>
</citation>
</ref>
<ref id="B5">
<label>5</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Cohade</surname> <given-names>C</given-names>
</name>
<name>
<surname>Wahl</surname> <given-names>RL</given-names>
</name>
</person-group>. <article-title>Applications of positron emission tomography/computed tomography image fusion in clinical positron emission tomography&#x2013;clinical use, interpretation methods, diagnostic improvements</article-title>. <source>Semin Nucl Med</source> (<year>2003</year>) <volume>33</volume>(<issue>3</issue>):<page-range>228&#x2013;37</page-range>. doi: <pub-id pub-id-type="doi">10.1053/snuc.2003.127312</pub-id>
</citation>
</ref>
<ref id="B6">
<label>6</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zer</surname> <given-names>A</given-names>
</name>
<name>
<surname>Domachevsky</surname> <given-names>L</given-names>
</name>
<name>
<surname>Rapson</surname> <given-names>Y</given-names>
</name>
<name>
<surname>Nidam</surname> <given-names>M</given-names>
</name>
<name>
<surname>Flex</surname> <given-names>D</given-names>
</name>
<name>
<surname>Allen</surname> <given-names>AM</given-names>
</name>
<etal/>
</person-group>. <article-title>The role of 18F-FDG PET/CT on staging and prognosis in patients with small cell lung cancer</article-title>. <source>Eur Radiol</source> (<year>2016</year>) <volume>26</volume>(<issue>9</issue>):<page-range>3155&#x2013;61</page-range>. doi: <pub-id pub-id-type="doi">10.1007/s00330-015-4132-2</pub-id>
</citation>
</ref>
<ref id="B7">
<label>7</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Yue</surname> <given-names>Y</given-names>
</name>
<name>
<surname>Cui</surname> <given-names>X</given-names>
</name>
<name>
<surname>Bose</surname> <given-names>S</given-names>
</name>
<name>
<surname>Audeh</surname> <given-names>W</given-names>
</name>
<name>
<surname>Zhang</surname> <given-names>X</given-names>
</name>
<name>
<surname>Fraass</surname> <given-names>B</given-names>
</name>
</person-group>. <article-title>Stratifying triple-negative breast cancer prognosis using 18F-FDG-PET/CT imaging</article-title>. <source>Breast Cancer Res Treat</source> (<year>2015</year>) <volume>153</volume>(<issue>3</issue>):<page-range>607&#x2013;16</page-range>. doi: <pub-id pub-id-type="doi">10.1007/s10549-015-3558-1</pub-id>
</citation>
</ref>
<ref id="B8">
<label>8</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Xie</surname> <given-names>P</given-names>
</name>
<name>
<surname>Li</surname> <given-names>M</given-names>
</name>
<name>
<surname>Zhao</surname> <given-names>H</given-names>
</name>
<name>
<surname>Sun</surname> <given-names>X</given-names>
</name>
<name>
<surname>Fu</surname> <given-names>Z</given-names>
</name>
<name>
<surname>Yu</surname> <given-names>J</given-names>
</name>
</person-group>. <article-title>18F-FDG PET or PET-CT to evaluate prognosis for head and neck cancer: a meta-analysis</article-title>. <source>J Cancer Res Clin Oncol</source> (<year>2011</year>) <volume>137</volume>(<issue>7</issue>):<page-range>1085&#x2013;93</page-range>. doi: <pub-id pub-id-type="doi">10.1007/s00432-010-0972-y</pub-id>
</citation>
</ref>
<ref id="B9">
<label>9</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zhu</surname> <given-names>D</given-names>
</name>
<name>
<surname>Wang</surname> <given-names>L</given-names>
</name>
<name>
<surname>Zhang</surname> <given-names>H</given-names>
</name>
<name>
<surname>Chen</surname> <given-names>J</given-names>
</name>
<name>
<surname>Wang</surname> <given-names>Y</given-names>
</name>
<name>
<surname>Byanju</surname> <given-names>S</given-names>
</name>
<etal/>
</person-group>. <article-title>Prognostic value of 18F-FDG-PET/CT parameters in patients with pancreatic carcinoma: a systematic review and meta-analysis</article-title>. <source>Med (Baltimore)</source> (<year>2017</year>) <volume>96</volume>(<issue>33</issue>):<elocation-id>e7813</elocation-id>. doi: <pub-id pub-id-type="doi">10.1097/MD.0000000000007813</pub-id>
</citation>
</ref>
<ref id="B10">
<label>10</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Liu</surname> <given-names>J</given-names>
</name>
<name>
<surname>Dong</surname> <given-names>M</given-names>
</name>
<name>
<surname>Sun</surname> <given-names>X</given-names>
</name>
<name>
<surname>Li</surname> <given-names>W</given-names>
</name>
<name>
<surname>Xing</surname> <given-names>L</given-names>
</name>
<name>
<surname>Yu</surname> <given-names>J</given-names>
</name>
</person-group>. <article-title>Prognostic value of 18F-FDG PET/CT in surgical non-small cell lung cancer: a meta-analysis</article-title>. <source>PloS One</source> (<year>2016</year>) <volume>11</volume>(<issue>1</issue>):<elocation-id>e0146195</elocation-id>. doi: <pub-id pub-id-type="doi">10.1371/journal.pone.0146195</pub-id>
</citation>
</ref>
<ref id="B11">
<label>11</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sharp</surname> <given-names>SE</given-names>
</name>
<name>
<surname>Shulkin</surname> <given-names>BL</given-names>
</name>
<name>
<surname>Gelfand</surname> <given-names>MJ</given-names>
</name>
<name>
<surname>Salisbury</surname> <given-names>S</given-names>
</name>
<name>
<surname>Furman</surname> <given-names>WL</given-names>
</name>
</person-group>. <article-title>I-123-MIBG scintigraphy and f-18-FDG PET in neuroblastoma</article-title>. <source>J Nucl Med</source> (<year>2009</year>) <volume>50</volume>(<issue>8</issue>):<page-range>1237&#x2013;43</page-range>. doi: <pub-id pub-id-type="doi">10.2967/jnumed.108.060467</pub-id>
</citation>
</ref>
<ref id="B12">
<label>12</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Bleeker</surname> <given-names>G</given-names>
</name>
<name>
<surname>Tytgat</surname> <given-names>GA</given-names>
</name>
<name>
<surname>Adam</surname> <given-names>JA</given-names>
</name>
<name>
<surname>Caron</surname> <given-names>HN</given-names>
</name>
<name>
<surname>Kremer</surname> <given-names>LC</given-names>
</name>
<name>
<surname>Hooft</surname> <given-names>L</given-names>
</name>
<etal/>
</person-group>. <article-title>123I-MIBG scintigraphy and 18F-FDG-PET imaging for diagnosing neuroblastoma</article-title>. <source>Cochrane Database Systematic Rev</source> (<year>2015</year>) <volume>2015</volume>(<issue>9</issue>):<fpage>Cd009263</fpage>. doi: <pub-id pub-id-type="doi">10.1002/14651858.CD009263.pub2</pub-id>
</citation>
</ref>
<ref id="B13">
<label>13</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Liu</surname> <given-names>CJ</given-names>
</name>
<name>
<surname>Lu</surname> <given-names>MY</given-names>
</name>
<name>
<surname>Liu</surname> <given-names>YL</given-names>
</name>
<name>
<surname>Ko</surname> <given-names>CL</given-names>
</name>
<name>
<surname>Ko</surname> <given-names>KY</given-names>
</name>
<name>
<surname>Tzen</surname> <given-names>KY</given-names>
</name>
<etal/>
</person-group>. <article-title>Risk stratification of pediatric patients with neuroblastoma using volumetric parameters of 18F-FDG and 18F-DOPA PET/CT</article-title>. <source>Clin Nucl Med</source> (<year>2017</year>) <volume>42</volume>(<issue>3</issue>):<page-range>e142&#x2013;8</page-range>. doi: <pub-id pub-id-type="doi">10.1097/RLU.0000000000001529</pub-id>
</citation>
</ref>
<ref id="B14">
<label>14</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Papathanasiou</surname> <given-names>ND</given-names>
</name>
<name>
<surname>Gaze</surname> <given-names>MN</given-names>
</name>
<name>
<surname>Sullivan</surname> <given-names>K</given-names>
</name>
<name>
<surname>Aldridge</surname> <given-names>M</given-names>
</name>
<name>
<surname>Waddington</surname> <given-names>W</given-names>
</name>
<name>
<surname>Almuhaideb</surname> <given-names>A</given-names>
</name>
<etal/>
</person-group>. <article-title>F-18-FDG PET/CT and I-123-Metaiodobenzylguanidine imaging in high-risk neuroblastoma: diagnostic comparison and survival analysis</article-title>. <source>J Nucl Med</source> (<year>2011</year>) <volume>52</volume>(<issue>4</issue>):<page-range>519&#x2013;25</page-range>. doi: <pub-id pub-id-type="doi">10.2967/jnumed.110.083303</pub-id>
</citation>
</ref>
<ref id="B15">
<label>15</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Page</surname> <given-names>MJ</given-names>
</name>
<name>
<surname>McKenzie</surname> <given-names>JE</given-names>
</name>
<name>
<surname>Bossuyt</surname> <given-names>PM</given-names>
</name>
<name>
<surname>Boutron</surname> <given-names>I</given-names>
</name>
<name>
<surname>Hoffmann</surname> <given-names>TC</given-names>
</name>
<name>
<surname>Mulrow</surname> <given-names>CD</given-names>
</name>
<etal/>
</person-group>. <article-title>The PRISMA 2020 statement: an updated guideline for reporting systematic reviews</article-title>. <source>BMJ</source> (<year>2021</year>) <volume>372</volume>:<fpage>n71</fpage>. doi: <pub-id pub-id-type="doi">10.1136/bmj.n71</pub-id>
</citation>
</ref>
<ref id="B16">
<label>16</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Hayden</surname> <given-names>JA</given-names>
</name>
<name>
<surname>van der Windt</surname> <given-names>DA</given-names>
</name>
<name>
<surname>Cartwright</surname> <given-names>JL</given-names>
</name>
<name>
<surname>Cote</surname> <given-names>P</given-names>
</name>
<name>
<surname>Bombardier</surname> <given-names>C</given-names>
</name>
</person-group>. <article-title>Assessing bias in studies of prognostic factors</article-title>. <source>Ann Intern Med</source> (<year>2013</year>) <volume>158</volume>(<issue>4</issue>):<page-range>280&#x2013;6</page-range>. doi: <pub-id pub-id-type="doi">10.7326/0003-4819-158-4-201302190-00009</pub-id>
</citation>
</ref>
<ref id="B17">
<label>17</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Parmar</surname> <given-names>MK</given-names>
</name>
<name>
<surname>Torri</surname> <given-names>V</given-names>
</name>
<name>
<surname>Stewart</surname> <given-names>L</given-names>
</name>
</person-group>. <article-title>Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints</article-title>. <source>Stat Med</source> (<year>1998</year>) <volume>17</volume>(<issue>24</issue>):<page-range>2815&#x2013;34</page-range>. doi: <pub-id pub-id-type="doi">10.1002/(SICI)1097-0258(19981230)17:24&lt;2815::AID-SIM110&gt;3.0.CO;2-8</pub-id>
</citation>
</ref>
<ref id="B18">
<label>18</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Higgins</surname> <given-names>JP</given-names>
</name>
<name>
<surname>Thompson</surname> <given-names>SG</given-names>
</name>
<name>
<surname>Deeks</surname> <given-names>JJ</given-names>
</name>
<name>
<surname>Altman</surname> <given-names>DG</given-names>
</name>
</person-group>. <article-title>Measuring inconsistency in meta-analyses</article-title>. <source>Bmj</source> (<year>2003</year>) <volume>327</volume>(<issue>7414</issue>):<page-range>557&#x2013;60</page-range>. doi: <pub-id pub-id-type="doi">10.1136/bmj.327.7414.557</pub-id>
</citation>
</ref>
<ref id="B19">
<label>19</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Lee</surname> <given-names>JW</given-names>
</name>
<name>
<surname>Cho</surname> <given-names>A</given-names>
</name>
<name>
<surname>Yun</surname> <given-names>M</given-names>
</name>
<name>
<surname>Lee</surname> <given-names>JD</given-names>
</name>
<name>
<surname>Lyu</surname> <given-names>CJ</given-names>
</name>
<name>
<surname>Kang</surname> <given-names>WJ</given-names>
</name>
</person-group>. <article-title>Prognostic value of pretreatment FDG PET in pediatric neuroblastoma</article-title>. <source>Eur J Radiol</source> (<year>2015</year>) <volume>84</volume>(<issue>12</issue>):<page-range>2633&#x2013;9</page-range>. doi: <pub-id pub-id-type="doi">10.1016/j.ejrad.2015.09.027</pub-id>
</citation>
</ref>
<ref id="B20">
<label>20</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Li</surname> <given-names>C</given-names>
</name>
<name>
<surname>Zhang</surname> <given-names>J</given-names>
</name>
<name>
<surname>Chen</surname> <given-names>S</given-names>
</name>
<name>
<surname>Huang</surname> <given-names>S</given-names>
</name>
<name>
<surname>Wu</surname> <given-names>S</given-names>
</name>
<name>
<surname>Zhang</surname> <given-names>L</given-names>
</name>
<etal/>
</person-group>. <article-title>Prognostic value of metabolic indices and bone marrow uptake pattern on preoperative 18F&#x2013;FDG PET/CT in pediatric patients with neuroblastoma</article-title>. <source>Eur J Nucl Med Mol Imag</source> (<year>2018</year>) <volume>45</volume>(<issue>2</issue>):<page-range>306&#x2013;15</page-range>. doi: <pub-id pub-id-type="doi">10.1007/s00259-017-3851-9</pub-id>
</citation>
</ref>
<ref id="B21">
<label>21</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Liu</surname> <given-names>YL</given-names>
</name>
<name>
<surname>Lu</surname> <given-names>MY</given-names>
</name>
<name>
<surname>Chang</surname> <given-names>HH</given-names>
</name>
<name>
<surname>Lu</surname> <given-names>CC</given-names>
</name>
<name>
<surname>Lin</surname> <given-names>DT</given-names>
</name>
<name>
<surname>Jou</surname> <given-names>ST</given-names>
</name>
<etal/>
</person-group>. <article-title>Diagnostic FDG and FDOPA positron emission tomography scans distinguish the genomic type and treatment outcome of neuroblastoma</article-title>. <source>Oncotarget</source> (<year>2016</year>) <volume>7</volume>(<issue>14</issue>):<page-range>18774&#x2013;86</page-range>. doi: <pub-id pub-id-type="doi">10.18632/oncotarget.7933</pub-id>
</citation>
</ref>
<ref id="B22">
<label>22</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Man</surname> <given-names>S</given-names>
</name>
<name>
<surname>Yan</surname> <given-names>J</given-names>
</name>
<name>
<surname>Li</surname> <given-names>J</given-names>
</name>
<name>
<surname>Cao</surname> <given-names>Y</given-names>
</name>
<name>
<surname>Hu</surname> <given-names>J</given-names>
</name>
<name>
<surname>Ma</surname> <given-names>W</given-names>
</name>
<etal/>
</person-group>. <article-title>Value of pretreatment 18f-fdg pet/ct in prognosis and the reflection of tumor burden: a study in pediatric patients with newly diagnosed neuroblastoma</article-title>. <source>Int J Med Sci</source> (<year>2021</year>) <volume>18</volume>(<issue>8</issue>):<page-range>1857&#x2013;65</page-range>. doi: <pub-id pub-id-type="doi">10.7150/ijms.58263</pub-id>
</citation>
</ref>
<ref id="B23">
<label>23</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sung</surname> <given-names>AJ</given-names>
</name>
<name>
<surname>Weiss</surname> <given-names>BD</given-names>
</name>
<name>
<surname>Sharp</surname> <given-names>SE</given-names>
</name>
<name>
<surname>Zhang</surname> <given-names>B</given-names>
</name>
<name>
<surname>Trout</surname> <given-names>AT</given-names>
</name>
</person-group>. <article-title>Prognostic significance of pretreatment 18F-FDG positron emission tomography/computed tomography in pediatric neuroblastoma</article-title>. <source>Pediatr Radiol</source> (<year>2021</year>) <volume>51</volume>(<issue>8</issue>):<page-range>1400&#x2013;5</page-range>. doi: <pub-id pub-id-type="doi">10.1007/s00247-021-05005-y</pub-id>
</citation>
</ref>
<ref id="B24">
<label>24</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Liu</surname> <given-names>J</given-names>
</name>
<name>
<surname>Si</surname> <given-names>Y</given-names>
</name>
<name>
<surname>Zhou</surname> <given-names>Z</given-names>
</name>
<name>
<surname>Yang</surname> <given-names>X</given-names>
</name>
<name>
<surname>Li</surname> <given-names>C</given-names>
</name>
<name>
<surname>Qian</surname> <given-names>L</given-names>
</name>
<etal/>
</person-group>. <article-title>The prognostic value of (18)F-FDG PET/CT intra-tumoural metabolic heterogeneity in pretreatment neuroblastoma patients</article-title>. <source>Cancer Imag</source> (<year>2022</year>) <volume>22</volume>(<issue>1</issue>):<fpage>32</fpage>. doi: <pub-id pub-id-type="doi">10.1186/s40644-022-00472-4</pub-id>
</citation>
</ref>
<ref id="B25">
<label>25</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Nikolaos</surname> <given-names>DP</given-names>
</name>
<name>
<surname>Gaze</surname> <given-names>MN</given-names>
</name>
<name>
<surname>Sullivan</surname> <given-names>K</given-names>
</name>
<name>
<surname>Aldridge</surname> <given-names>M</given-names>
</name>
<name>
<surname>Waddington</surname> <given-names>W</given-names>
</name>
<name>
<surname>Almuhaideb</surname> <given-names>A</given-names>
</name>
<etal/>
</person-group>. <article-title>18F-FDG PET/CT and 123I-metaiodobenzylguanidine imaging in high-risk neuroblastoma: diagnostic comparison and survival analysis</article-title>. <source>J Nucl Med</source> (<year>2011</year>) <volume>52</volume>(<issue>4</issue>):<page-range>519&#x2013;25</page-range>. doi: <pub-id pub-id-type="doi">10.2967/jnumed.110.083303</pub-id>
</citation>
</ref>
<ref id="B26">
<label>26</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Wahl</surname> <given-names>RL</given-names>
</name>
<name>
<surname>Jacene</surname> <given-names>H</given-names>
</name>
<name>
<surname>Kasamon</surname> <given-names>Y</given-names>
</name>
<name>
<surname>Lodge</surname> <given-names>MA</given-names>
</name>
</person-group>. <article-title>From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors</article-title>. <source>J Nucl Med</source> (<year>2009</year>) <volume>50 Suppl 1</volume>:<page-range>122S&#x2013;50S</page-range>. doi: <pub-id pub-id-type="doi">10.2967/jnumed.108.057307</pub-id>
</citation>
</ref>
<ref id="B27">
<label>27</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Wang</surname> <given-names>H</given-names>
</name>
<name>
<surname>Shen</surname> <given-names>G</given-names>
</name>
<name>
<surname>Jiang</surname> <given-names>C</given-names>
</name>
<name>
<surname>Li</surname> <given-names>L</given-names>
</name>
<name>
<surname>Cui</surname> <given-names>F</given-names>
</name>
<name>
<surname>Tian</surname> <given-names>R</given-names>
</name>
</person-group>. <article-title>Prognostic value of baseline, interim and end-of-treatment 18F-FDG PET/CT parameters in extranodal natural killer/T-cell lymphoma: a meta-analysis</article-title>. <source>PloS One</source> (<year>2018</year>) <volume>13</volume>(<issue>3</issue>):<elocation-id>e0194435</elocation-id>. doi: <pub-id pub-id-type="doi">10.1371/journal.pone.0194435</pub-id>
</citation>
</ref>
</ref-list>
</back>
</article>
