Prostate-specific studies have quantified the risk of missing anterior tumors. In patients with low-risk prostate cancer, up to 16% of csPCa lesions are located in the anterior part and are frequently missed by standard TRUS-Bx templates (11). Importantly, this risk is context-dependent. While expanding the biopsy scope to include anterior sampling can increase the detection rate of csPCa, the net benefit varies. For instance, in the biopsy-naïve population, anterior sampling may increase the detection rate of csPCa by approximately 5.7% (p=0.09, not statistically significant in that cohort), whereas in men on active surveillance (AS) with prior negative biopsies, the incremental yield can be higher, underscoring the need for risk-adapted sampling strategies (11).

Traditional techniques rely on random sampling, which makes it arduous to effectively detect tumors that are small in volume (<0.5 cm³) or have an atypical distribution. The random sampling nature of TRUS-Bx creates a significant detection bottleneck, particularly for small or atypically located tumors. The false-negative rate is not uniform across all patient groups. For example, in patients under AS, a 12-core systematic biopsy may miss approximately 10% of csPCa (12). Notably, the reclassification risk following a negative biopsy is a key metric of this limitation. Cohort studies focused on AS populations report that patients with an initial negative biopsy harbor a 10%-25% risk of being reclassified to higher-risk disease upon subsequent surveillance biopsies, a figure directly attributable to sampling error and tumor multifocality (13). This phenomenon is closely associated with the multifocality and spatial heterogeneity of tumors (13).

The limited number of biopsy samples (usually 12 cores) may not comprehensively represent the genomic diversity of tumors. For instance, the correlation between the genomic risk score of low-risk patients and postoperative pathological upgrading and biochemical recurrence suggests that traditional biopsies may underestimate tumor aggressiveness (14). Furthermore, the Gleason scoring system’s disregard for minor Gleason 5 components can impact the accuracy of prognostic assessment (15).

The operator-dependent nature of TRUS-Bx is a well-documented limitation. Discrepancies in the definition of the ‘standard 12-core’ distribution among different institutions (e.g., inclusion of anterior or apical sampling) contribute significantly to inter-institutional variability in detection rates and limit result consistency (7). Furthermore, the reliance on ultrasound alone (resolution ~1–2 mm) for targeting is a fundamental constraint. Studies quantifying operator performance suggest that insufficient experience can reduce the detection rate of csPCa by a relative margin of up to 15-20% compared to expert operators, highlighting the critical impact of expertise on procedural efficacy (12).

Super-microvascular imaging (SMI) is capable of real-time visualization of abnormal microvessels within the prostate (such as tortuous and increased branching patterns), facilitating the identification of suspicious areas and guiding targeted biopsy. Studies have indicated that, in comparison to traditional color Doppler flow imaging (CDFI) and power Doppler ultrasound (PDUS), SMI exhibits a greater aptitude for detecting low-velocity blood flow and can more sensitively discern the neovascularization signals of tumors <mark>in the prostate (28). Prostate biopsy guided by SMI can significantly elevate the positive rate of tissue sampling (29). When integrated with other techniques like ultrasound elastography, SMI can dynamically monitor changes in blood flow signals, optimize the puncture trajectory, and mitigate accidental damage to normal blood vessels (30). SMI’s ability to display microcirculation without the need for contrast agent injection circumvents contrast-related risks, rendering it particularly suitable for patients with renal insufficiency.

Through the utilization of specific contrast agents, contrast-enhanced ultrasound (CEUS) can generate high-resolution images of tissue microvessels, enabling the observation of lesion characteristics, such as those of tumors and inflammations. Prostate cancer demonstrates rapid and high-intensity contrast enhancement attributed to neovascularization. CEUS can precisely delineate the tumor boundary and direct targeted biopsy (31, 32). Research reveals that targeted biopsy guided by CEUS has a 15%-20% higher cancer detection rate compared to systematic biopsy (31), while simultaneously reducing the number of unnecessary biopsies.

BpMRI encompasses only T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI)/apparent diffusion coefficient (ADC) sequences, excluding the dynamic contrast-enhanced (DCE) sequence. This not only substantially shortens the examination duration and reduces costs by approximately 50% but also eliminates risks associated with contrast agents (such as allergies, renal impairment, etc.) (33). Multiple meta-analyses indicate that the sensitivity (0.74-0.79 vs. 0.76-0.84) and specificity (0.88-0.90 vs. 0.89-0.89) of bpMRI and mpMRI are comparable, with no statistically significant difference (22, 23). Moreover, outside the prostate-specific antigen (PSA) range of 10–20 ng/ml, there is no marked difference in the cancer detection rates between bpMRI and mpMRI (24). Nevertheless, its sensitivity to certain lesions (such as tumors in the transition zone) may be lower than that of mpMRI, particularly when differentiating PI-RADS 3–4 lesions (33).

By integrating T2WI, DWI, DCE, and magnetic resonance spectroscopy (MRS), MpMRI can more comprehensively assess prostate lesions, including complex manifestations such as seminal vesicle invasion and pelvic lymph node metastasis. Studies demonstrate that the predictive accuracy of mpMRI for prostate extracapsular extension reaches 89%, significantly superior to traditional imaging (34). Its targeted biopsy (TB) in combination with ultrasound fusion exhibits higher sensitivity (81%-86%) and specificity (69%-84%) in the detection of csPCa, especially in lesions with PI-RADS ≥ 4, where its diagnostic efficacy is markedly better than that of traditional 12-core systematic biopsy (SB) (35–37). Combining TB and SB can further optimize the detection rate, particularly for multifocal lesions or in the anterior prostate region, with an incremental csPCa detection rate of 10%-15% (37–39). It is noteworthy that approximately 19%-31% of csPCa are detected solely in the second or third targeted biopsy, suggesting the necessity of a multi-core sampling strategy (36). Although several studies have shown that the sensitivity and specificity of bpMRI and mpMRI in detecting prostate cancer are marginally different, mpMRI demonstrates higher diagnostic accuracy in certain cases, especially in the detection of csPCa, where its sensitivity is significantly greater than that of bpMRI (22). Additionally, the enhanced imaging capabilities of mpMRI facilitate more precise prostate cancer localization, thereby reducing the false-positive rate (33). When the mpMRI result is negative, the negative predictive value for significant prostate cancer is as high as 95% (40). In AS, mpMRI can detect tumor progression (such as a volume change of ≥ 50%) at an earlier stage and guide the appropriate timing of treatment (41).

PI-RADS, through the interpretation of standard mpMRI images, has significantly enhanced the detection efficiency of csPCa. Research has validated that PI-RADS 4–5 lesions are strongly correlated with csPCa, with positive predictive values (PPV) of up to 48.1% and 68.3% respectively (42, 43). However, the clinical management of PI-RADS 3 lesions remains a subject of controversy, with a csPCa detection rate of only 12.5%-20.8%. Individualized decisions should be made by incorporating high-risk factors such as prostate-specific antigen density (PSAD > 0.15 ng/ml/cm³) or abnormal digital rectal examination (44, 45). It is important to note that even though PI-RADS 5 lesions have a high predictive value, 18% of cases yield benign pathological results following targeted biopsy, suggesting that integration of other imaging or molecular markers is necessary to optimize diagnostic efficacy (46, 47). In response to the clinical challenges posed by PI-RADS 3 lesions, the latest guidelines advocate a dynamic risk-stratification strategy: for single-focus lesions with a low PSAD (PSAD < 0.12 ng/ml/cm³), short-term imaging follow-up can be implemented, while for multiple-focus lesions or those with high-risk factors, MRI-ultrasound fusion-guided targeted biopsy is recommended (44, 45, 48). Moreover, elastography technology, by quantifying tissue hardness disparities, can assist in locating sclerotic areas not visualized by MRI. When combined with mpMRI, it can increase the csPCa detection rate by 8%-12%, particularly providing supplementary value in transition-zone lesions (45, 46).

PSMA PET/CT enables highly sensitive detection (sensitivity 90-98%, specificity 82%-99%) of prostate cancer lesions by targeting the expression of the PSMA protein (49, 50). It can also furnish information regarding the body-wide distribution of tumors, assisting doctors in comprehensively evaluating disease spread and thereby formulating more precise treatment plans (51, 52). Studies have indicated that PSMA PET/CT demonstrates high sensitivity and specificity in identifying local and distant metastases (49, 53), particularly in high-risk prostate cancer patients, where its diagnostic accuracy surpasses that of traditional imaging methods (CT and MRI) (54). The maximum standardized uptake value (SUVmax) of PSMA PET/CT is significantly positively correlated with tumor aggressiveness (such as Gleason score, pathological stage) (p=0.007) (55, 56). In instances where mpMRI results are negative or equivocal, PSMA PET/CT can function as an effective supplementary tool.

Approximately 5%-10% of prostate cancers (notably those with neuroendocrine differentiation) display low PSMA expression (SUVmax < 10), and a missed diagnosis could potentially delay treatment (57, 58). For such cases, alternative biomarkers like KLF8, CHST11, or functional imaging (such as FDG-PET) must be incorporated (56, 57). Research reveals that the combined use of mpMRI and PSMA PET can elevate the negative predictive value (NPV) of biopsy to 96%, diminishing the necessity for systematic biopsy (58). The detection rate of PSMA-PET for tumors with a high Gleason score (≥8) is markedly higher than that for tumors with a low score (90% vs 60%), yet its sensitivity to Gleason 6 tumors is inadequate (<50%) (57, 58). Additionally, antibody probes targeting KLF8 have manifested potential for specific binding to poorly differentiated tumors in preclinical studies, and future endeavors are required to facilitate their clinical translation (57).

AI, leveraging deep-learning algorithms, has empowered automated analysis of prostate imaging data and extraction of quantitative features, thereby significantly enhancing the accuracy and efficiency of lesion localization. AI models, such as U-Net and nnUNet, are capable of automatically segmenting prostate anatomical structures and suspicious lesions by analyzing mpMRI and PET images (59). For instance, FocalNet, through the integration of convolutional neural network and Gleason score data, has accomplished joint detection of prostate cancer and prediction of its aggressiveness. Its sensitivity and specificity in the detection of clinically significant prostate cancer attain 89.7% and 87.9% respectively (60). In contrast, traditional image segmentation hinges on manual delineation, which is not only time-consuming but also prone to subjective bias. Deep-learning models can mitigate the subjective discrepancies among radiologists and enhance the consistency of segmentation results. For example, a study encompassing 976 cases demonstrated that the consistency between the AI segmentation model based on ADC maps and the manual annotations of multiple radiologists reached 0.96 (95% CI 0.95-0.97) (61).

Subsequent to lesion segmentation, the technique of image texture feature quantification can be further employed to conduct a more in-depth analysis of the segmented lesion area. Texture feature quantification has the capacity to capture subtle alterations in the lesion area, such as cell arrangement and blood vessel distribution. This information aids in the evaluation of the malignancy and aggressiveness of the lesion. For example, regarding PI-RADS 3 lesions, the AI classification model founded on the texture features of T2-weighted images exhibits a sensitivity and specificity of 83% and 96% respectively for csPCa, markedly outperforming traditional visual assessment (Area Under Curve [AUC] 0.89 vs 0.72, p < 0.001) (62). Three-dimensional morphological analysis techniques, like light-sheet microscopy, further augment the model’s ability to capture heterogeneous structures and enhance diagnostic robustness (63).

By integrating the high-resolution anatomical information of MRI with the real-time navigation functionality of ultrasound, a three-dimensional model is constructed to facilitate puncture positioning. Targeted biopsy using MRI-TRUS fusion in conjunction with systematic biopsy (TB + SB) can significantly boost the detection rate of csPCa. Compared to traditional 12-core TRUS-Bx, fusion biopsy has demonstrated superiority in large-scale clinical trials such as PRECISION and PRECISE, particularly in the detection of high-risk tumors with an International Society of Urological Pathology (ISUP) grade of ≥ 2 (16–18). Prospective multicenter trials have revealed that mpMRI combined with TRUS fusion biopsy yields a significantly higher csPCa detection rate than simple systematic biopsy. The biopsy positive rate is increased by 20%-30%, while the overdiagnosis of low-risk tumors is reduced (64–66). In comparison to the transrectal approach, transperineal MRI-TRUS fusion biopsy exhibits a lower infection complication rate (0.8% vs. 3.5%) (2, 67), and antibiotic use is more standardized. Although pain and anxiety persist in fusion biopsy, advancements in electromagnetic tracking and local anesthesia techniques (such as the Vector electromagnetic needle tracking system) have improved patient tolerance (68, 69). A comparative analysis of different fusion methods of MRI - TRUS is presented in Table 2 .

PSMA-PET/CT and Ultrasound Fusion: PSMA-PET/CT can specifically identify the molecularly metabolically active regions of prostate cancer lesions, while ultrasound offers real-time anatomical guidance. The integration of these two modalities combines highly sensitive molecular imaging with real-time puncture navigation, significantly increasing the detection rate of csPCa (70–72). PSMA-PET/CT can also pinpoint multifocal or occult lesions, thereby avoiding blind punctures in non-metabolically active areas. For instance, in PSMA-PET-negative areas, the csPCa missed diagnosis rate is merely 5%-8% (25, 73). whereas the missed diagnosis rate of TRUS-Bx is as high as 20%-30% (71).

PET and MRI Fusion: The PET-MRI fusion technology combines the functional metabolic information of PET with the anatomical structure information of MRI, surmounting the limitations of single-technology approaches. By simultaneously acquiring metabolic and anatomical data, doctors can more comprehensively assess the location, size, and activity of lesions. For example, PSMA-PET/MRI can identify metabolically active lesions that MRI might overlook, and MRI can supply anatomical details to aid in targeted biopsy (74, 75).