PI-RADS v2.1 for prostate MRI: current applications, ongoing debates, and synergies with advanced technologies—a narrative review

Introduction

Globally, the incidence of prostate cancer continues to rise, positioning it as a significant disease threatening men’s health (1,2). Early detection and precise diagnosis are crucial for formulating effective treatment plans, improving patient survival rates, and enhancing quality of life. Magnetic resonance imaging (MRI) plays a central role in the detection, localization, staging, and follow-up of prostate cancer. A representative multiparametric MRI examination is shown in Figure 1. Importantly, MRI has emerged as a key pre-biopsy triage tool that helps determine which men require biopsy and how the biopsy should be performed (3,4). Depending on local resources and clinical indication, biopsy may be carried out as systematic transrectal or transperineal biopsy, MRI-targeted biopsy (using cognitive registration, software-based fusion, or in-bore guidance), or a combination of targeted and systematic sampling. The MRI-directed pathway has been shown to increase the detection of clinically significant prostate cancer (csPCa) while reducing unnecessary biopsies and overdiagnosis of indolent disease (5). To standardize MRI reporting and improve diagnostic consistency, the European Society of Urogenital Radiology (ESUR) and the American College of Radiology (ACR) jointly released the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) in 2019. This article provides a systematic review of the current applications of PI-RADS v2.1, analyzes its advantages and limitations, and offers perspectives on its future development. We present this article in accordance with the Narrative Review reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0193/rc).

Figure 1 Representative multiparametric MRI of a clinically significant prostate cancer in the right TZ near the base, scored as PI-RADS 4. A 64 years old man with serum total PSA 5.52 ng/mL. (A) Axial T2 weighted image (non-fat suppressed) shows an ovoid, homogeneous moderately hypointense lesion with indistinct margins in the right TZ near the base (arrow), measuring approximately 1.4 cm in greatest dimension. (B) Coronal T2 weighted image (fat suppressed) confirms the lesion location and morphology (arrow). (C) Axial T1 weighted image (non-fat suppressed) demonstrates isointense signal (arrow). (D) Axial diffusion weighted image (b=1,400 s/mm²) shows marked hyperintensity (arrow). (E) Corresponding ADC map shows marked hypointensity (arrow), indicating restricted diffusion. (F) Early DCE image shows avid early enhancement (arrow), with persistent enhancement on later phases (not shown). According to PI-RADS v2.1, a TZ lesion with T2WI score 4 (moderately hypointense, indistinct margins, <1.5 cm) and DWI score 4 (marked restricted diffusion) receives an overall assessment category of 4, regardless of DCE positivity. ADC, apparent diffusion coefficient; DCE, dynamic contrast-enhanced; DWI, diffusion-weighted imaging; MRI, magnetic resonance imaging; PI-RADS, Prostate Imaging Reporting and Data System; PSA, prostate-specific antigen; TZ, transition zone.

Methods

A narrative literature search was performed in PubMed and Web of Science databases, with the search date set as December 15, 2025. The search strategy combined the key term “PI‑RADS v2.1” (including “Prostate Imaging Reporting and Data System version 2.1”) with terms related to prostate MRI, clinical applications, and emerging technologies, such as “prostate cancer”, “biparametric MRI”, “artificial intelligence”, “radiomics”, “deep learning”, “ADC”, “PSA density”, and “clinically significant prostate cancer”. The search was limited to English‑language articles, with no restriction on the starting date. Both original research and systematic reviews were included; case reports, editorials, and non‑English articles were excluded. Two authors (S.Z. and X.W.) independently screened titles and abstracts; any disagreements were resolved by consensus or by a third author (F.C.). As this is a narrative review, we did not aim to systematically retrieve all available literature; instead, we focused on key studies that illustrate the current applications, ongoing debates, and synergies with advanced technologies of PI‑RADS v2.1. The search strategy summary is presented in Table 1.

The standardized assessment framework of PI-RADS v2.1

The PI-RADS framework has undergone significant evolution to standardize multiparametric MRI (mpMRI) evaluation of the prostate. The initial version (v1), released by ESUR in 2012, was subsequently succeeded by version 2 (v2) in 2015. Developed by the PI-RADS Steering Committee (comprising ESUR, ACR, and the Advanced Medical Technology (AdMeTech) Foundation), PI-RADS v2 established foundational standardization by defining technical parameters, introducing the ‘dominant sequence’ principle, and establishing a simplified scoring system framework. To further enhance inter-reader agreement, clarify technical details, and address the emergence of biparametric MRI (bpMRI), the Steering Committee released the updated PI-RADS v2.1 in 2019 (6) (Figure 2).

Figure 2 Evolution of the PI-RADS standards. The PI-RADS Steering Committee is the international body responsible for developing and updating the PI-RADS standards, comprising the ESUR, the ACR, and the AdMeTech Foundation. ACR, American College of Radiology; AdMeTech, Advanced Medical Technology; bpMRI, biparametric magnetic resonance imaging; DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; DWI, diffusion-weighted imaging; ESUR, European Society of Urogenital Radiology; mpMRI, multiparametric magnetic resonance imaging; MRS, magnetic resonance spectroscopy; PI-RADS, Prostate Imaging Reporting and Data System; PZ, peripheral zone; T2WI, T2-weighted imaging; TZ, transition zone.

The key refinements in PI-RADS v2.1 are threefold (Figure 3). First, technical specifications were updated: T2-weighted imaging (T2WI) must include axial and at least one orthogonal plane; the acquisition scheme for diffusion-weighted imaging (DWI), including b-values for apparent diffusion coefficient (ADC) map calculation, was refined; and the recommended temporal resolution for dynamic contrast-enhanced (DCE) MRI was adjusted to ≤15 seconds, with a preference for 3D over 2D sequences (6).

Figure 3 Schematic overview of key updates in PI-RADS v2.1, including refinements to technical specifications, interpretive criteria, and clinical guidance. 3D T1W GRE, three-dimensional T1-weighted gradient recalled echo; ADC, apparent diffusion coefficient; AP, anteroposterior; BPH, benign prostatic hyperplasia; bpMRI, biparametric magnetic resonance imaging; DCE, dynamic contrast-enhanced; DWI, diffusion-weighted imaging; mpMRI, multiparametric magnetic resonance imaging; PI-RADS v2.1, Prostate Imaging Reporting and Data System version 2.1; PZ, peripheral zone; T2WI, T2-weighted imaging.

Second, interpretive criteria were clarified and refined: Typical, completely encapsulated benign prostatic hyperplasia (BPH) nodules in the transition zone (TZ) were reclassified as PI-RADS category 1. The distinction between DWI scores 2 and 3 was optimized, and the definitions for “positive” versus “negative” enhancement on DCE were explicitly stated. Furthermore, assessment criteria for lesions involving the anterior fibromuscular stroma (AFMS) were clarified, specifying that such lesions should be scored using criteria for the zone [peripheral zone (PZ) or TZ] from which they most likely originate (6,7).

Third, guidance for clinical application was enhanced: Scenarios appropriate for bpMRI were defined. An updated prostate sector map featuring 41 regions was introduced to facilitate precise lesion localization. Finally, a standardized method for measuring prostate volume on MRI was provided to ensure consistency in calculating metrics like prostate-specific antigen density (PSAD). Collectively, these revisions in PI-RADS v2.1 systematically improve the reproducibility and clinical utility of prostate MRI assessment (6).

Despite these improvements, PI-RADS v2.1 offers limited and ambiguous guidance on patient preparation. Factors such as bowel cleansing (enema, dietary restrictions), administration of anti-spasmodic agents (e.g., hyoscine butylbromide), and management of rectal air or stool are not standardised in the framework (8). These pre-examination variables can significantly affect image quality—particularly on diffusion-weighted and T2-weighted sequences—yet no consensus recommendations are provided. Consequently, this represents an important unresolved limitation of the current guidelines that may contribute to inter-institutional variability in diagnostic performance.

Diagnostic performance, interobserver agreement, and clinical validation of PI-RADS v2.1: a landscape of both advantages and controversies

The introduction of PI-RADS v2.1 aimed to refine the mpMRI protocol, address ambiguities present in earlier versions, and enhance scoring consistency and diagnostic accuracy. Extensive research has validated its diagnostic performance, interobserver agreement, and clinical application strategies, revealing a landscape characterized by both strengths and ongoing debates.

Diagnostic performance: comparable overall but with zonal variations

Multiple comparative studies and meta-analyses indicate that the overall diagnostic performance of PI-RADS v2.1 for detecting csPCa is comparable to that of version 2.0 (Table 2). A meta-analysis by Lee et al. found no significant differences in pooled sensitivity, positive predictive value, or negative predictive value between the two versions (14). This conclusion is supported by studies from Hötker and Oerther et al. (11,19).

However, the assessment of specificity remains controversial. While one meta-analysis reported a significantly lower pooled specificity for v2.1 compared to v2.0 (14), some single-center studies have shown improved specificity within the TZ for v2.1 (9,12,13). This discrepancy underscores the critical influence of study design, reader experience, and reference standards (e.g., biopsy technique) on performance evaluation.

Furthermore, diagnostic performance varies across anatomical zones:

TZ: by refining criteria (e.g., downgrading typical BPH nodules from category 2 to category 1), v2.1 has improved sensitivity and interobserver agreement for cancer detection in the TZ (12,13,15), though potentially at the cost of decreased specificity (10,20) (Table 2). Although v2.1 provides clearer imaging criteria for the “nodule-in-nodule” sign, the actual proportion of prostate cancers detected specifically through this sign remains relatively low (approximately 5–8%) (21). Notably, TZ nodules upgraded to category 3 based on DWI findings show a lower cancer detection rate (~28%) compared to those scored as 3 based on T2WI alone (22).

PZ: improvements in diagnostic performance are more consistently reported for the PZ in v2.1 (17). The main controversy centers on PI-RADS category 3 lesions. Evidence suggests that upgrading a DWI score 3 lesion to category 4 based solely on positive DCE findings significantly increases the false-positive rate (23). Consequently, biopsy decisions for such lesions should incorporate clinical indicators like PSAD rather than relying exclusively on DCE results (23,24).

Interobserver agreement: modest improvement with persistent challenges

Current literature suggests that the overall interobserver agreement of PI-RADS v2.1 is comparable to, or slightly improved over, version 2.0 (25) (Table 2). Specifically, the agreement in assessing the PZ appears to be more clearly enhanced (16,18). However, significant inconsistencies remain, particularly in distinguishing lower score categories (e.g., 1 vs. 2), with the TZ posing the greatest challenge (18). Despite the use of structured terminology, subjective interpretation of certain imaging features, such as lesion shape and signal intensity, continues to be a major source of variability (26). It has been suggested that merging categories 1 and 2 might help improve agreement in clinical decision-making (18).

The critical influence of reader experience and optimization strategies

Reader experience is a key variable influencing both the diagnostic accuracy and consistency of PI-RADS v2.1 assessments. Experienced radiologists achieve reliable diagnostic performance, while less experienced readers are a primary source of inconsistency and face a significant learning curve (27,28). To bridge this experience gap, several strategies can be employed: artificial intelligence (AI)-assisted diagnostic tools have been shown to improve the agreement and diagnostic performance of less experienced readers relative to experts (29,30); structured reporting and online scoring tools aid in standardizing the workflow and improving efficiency (31). Furthermore, exploring the combination of the standardized PI-RADS framework with experience-based Likert scoring offers a potential direction for developing more flexible and personalized precision assessment schemes in the future (32).

Multimodal integration and model construction: from precise measurement to clinical prediction

Integrating PI-RADS v2.1 with clinical and molecular biomarkers constitutes a critical pathway towards personalized diagnosis and prognostic assessment.

Foundational role and methodological consensus of precise volume measurement

Accurate assessment of prostate volume is fundamental for calculating PSAD, formulating treatment strategies, and selecting patients for active surveillance. PI-RADS v2.1 recommends routinely reporting MRI-based prostate volume, using either the ellipsoid formula or segmentation methods, with unified measurement planes to enhance consistency (6). The ellipsoid formula, favored for routine clinical practice due to its speed, reliability, and good reproducibility (33-36), is widely adopted. In contrast, segmentation, while more accurate, is less frequently used in daily practice due to its time-consuming nature (33). PI-RADS v2.1 specifically recommends measuring the anteroposterior (AP) diameter on the midsagittal plane to mitigate the “salami slicing effect” (34). It is noteworthy, however, that some studies have found measurements on the axial plane to correlate better with segmented volumes (35). Volume measurement not only affects lesion assessment but is also linked to tumor biology: 3D modeling reveals that the actual diameter and volume of lesions are underestimated in approximately 40–61% of patients, potentially affecting the final PI-RADS score (37). Moreover, a larger peripheral zone volume is often associated with higher Gleason scores, whereas a larger transition zone volume correlates with lower-grade cancer, supporting the theory of mechanical pressure within the gland inhibiting tumorigenesis (38).

Integration of core biomarkers and optimization of diagnostic performance

Volume-based core biomarkers are crucial for clinical decision-making. PSAD, by correcting for the influence of benign hyperplasia on serum prostate-specific antigen (PSA), has become one of the most cost-effective tools for optimizing the diagnostic performance of PI-RADS (24,39,40). Its core value lies in guiding the management of PI-RADS category 3 lesions: immediate biopsy may be deferred when PSAD is ≤0.15 ng/mL² (40,41), and PSAD outperforms lesion-volume-based strategies in ruling out csPCa (41). PSAD also helps identify false positives among PI-RADS 4–5 lesions, thereby improving diagnostic specificity (42,43).

The Prostate Health Index (PHI) and its derivative, PHI density (PHID), integrate molecular and volumetric information, enhancing diagnostic specificity and offering unique value in risk stratification for patients with PI-RADS 3 lesions (44). Quantitative imaging markers, such as ADC values derived from DWI, perform excellently in discriminating csPCa (45,46). Their combination with PI-RADS v2.1 scores can significantly improve diagnostic accuracy and reduce unnecessary biopsies (47,48). ADC heterogeneity parameters (e.g., coefficient of variation) are also considered potential markers for assessing tumor aggressiveness (49). However, the application of ADC values has limitations; for instance, their predictive value for high-grade lesions may not surpass that of morphological features (50), and widespread adoption requires further standardization and prospective validation (51).

Construction and clinical application of multiparameter predictive and prognostic models

The current research trend focuses on constructing multivariable models that integrate imaging, clinical, and molecular data to comprehensively enhance the precision of diagnosis and prognosis.

For diagnostic prediction, models integrating PI-RADS v2.1 scores, PSAD, and ADC values demonstrate robust performance in predicting csPCa and reducing unnecessary biopsies (52,53). Incorporating PHI can further enhance predictive capability (54,55). More advanced models that fuse deep learning (DL)-extracted imaging features with clinical variables have shown diagnostic efficacy superior to using PI-RADS scores alone (56,57). Dedicated nomograms for specific clinical scenarios (e.g., “double-negative” patients) can more precisely guide biopsy decisions (58,59). Some models have even begun integrating genomic information, showing promise in risk stratification for active surveillance patients (60).

For prognostic prediction, preoperative PI-RADS scores and related MRI features are significant for predicting pathological outcomes after radical prostatectomy. Scoring systems combining MRI features with clinical indicators can effectively predict extraprostatic extension, aiding surgical planning (61,62). Imaging factors like a PI-RADS score of 5 are independent predictors of postoperative biochemical recurrence (63). Comprehensive models integrating mpMRI features with biopsy-derived molecular markers can significantly improve the predictive efficacy for postoperative recurrence risk (64).

Technical pathway consensus and choice: biparametric versus multiparametric MRI

Although the PI-RADS v2.1 standard is predicated on mpMRI, the bpMRI pathway, which omits the DCE sequence, has garnered substantial clinical acceptance owing to its efficiency, safety, and cost-effectiveness (Table 3).

Re-evaluation of DCE and the evidence-based ascendancy of bpMRI

In PI-RADS v2.1, the role of DCE is precisely circumscribed: it is primarily utilized to upgrade a DWI score 3 lesion in the PZ to assessment category 4, while it holds no formal role in the evaluation of the TZ (65). Evidence indicates that DCE provides moderate sensitivity (up to 82.6%) for prostate cancer detection (72), can enhance the detection of small TZ cancers (73), and serves as a critical adjunct in cases of suboptimal DWI image quality (66,67).

However, the routine application of DCE is debated. A central contention is that upgrading a lesion based solely on DCE positivity may increase false-positive rates, thereby diluting the proportion of csPCa within category 4 and potentially leading to unnecessary biopsies (23). A large-scale review found that DCE led to score upgrades in only 3.2% of patients, and among these upgrades, merely ~27.7% were pathologically confirmed as csPCa (77). When combined with its invasive nature, potential adverse effects, and higher cost, the necessity for routine DCE is challenged (74,75).

Consequently, the contrast-free bpMRI pathway has rapidly evolved. Multiple studies affirm that the diagnostic performance of bpMRI for csPCa detection (68-70) and its inter-reader agreement (70) are comparable to those of mpMRI. While mpMRI may offer marginally higher sensitivity, bpMRI demonstrates superior specificity in some analyses (71). For patients in the diagnostic PSA “gray zone” (4–10 ng/mL), bpMRI has shown a significantly higher area under the curve (AUC) and better specificity compared to mpMRI in certain studies (80). Additional advantages of bpMRI include the absence of contrast-related risks, shorter acquisition times, lower cost, and improved accessibility (74,76) (Table 3).

Efficacy equilibrium and contextual complementarity of bpMRI and mpMRI

Head-to-head comparative studies reinforce the diagnostic reliability of bpMRI. For detecting PZ prostate cancer and csPCa within the PZ, bpMRI and mpMRI demonstrate no statistically significant differences in sensitivity, specificity, or AUC (69). Their csPCa detection rates are also highly concordant when using various PI-RADS score thresholds (68). This body of evidence robustly supports bpMRI as a clinically effective, efficient, and economical alternative that does not increase the risk of unnecessary biopsy, with key comparative data summarized in Table 3.

Notwithstanding, conventional mpMRI retains irreplaceable value in specific contexts. Evidence suggests mpMRI may preserve advantages in overall lesion detection and the identification of minute cancers (73). More critically, DCE is indispensable for post-treatment evaluations, such as assessing local recurrence—a clinical scenario outside the purview of standard PI-RADS assessment and not adequately addressed by bpMRI (6).

AI presents a transformative opportunity. DL techniques have been employed to synthesize simulated DCE images from non-contrast sequences. These synthetic images exhibit high concordance with genuine DCE-MRI under the PI-RADS v2.1 framework, suggesting a promising avenue for non-invasive, precise evaluation (78).

Risk-stratified imaging pathway selection and emerging clinical consensus

While the PI-RADS Steering Committee acknowledges the merits of bpMRI and endorses further investigation, it cautions that current supporting evidence is largely derived from single-center studies and notes that DCE may augment the sensitivity of mpMRI. Therefore, the committee continues to advocate for mpMRI as the preferred modality for high-risk individuals, including those with prior negative biopsies and persistently elevated PSA, patients on active surveillance with suspected progression, individuals with a strong family history or genetic predisposition, and cases anticipated to have suboptimal image quality (6).

Implementing a risk-stratified imaging strategy is pivotal for optimizing resource utilization. A proposed “recall system”, wherein all patients initially undergo bpMRI with only those exhibiting PZ PI-RADS category 3 lesions or technically limited examinations recalled for supplemental DCE, has been shown to maintain diagnostic accuracy while significantly reducing cost and examination time (76). Furthermore, a precision biopsy strategy integrating mpMRI findings with PSAD can safely avert approximately 40% of unnecessary biopsies while preserving a high csPCa detection rate (79). It is crucial to recognize that within a strict bpMRI pathway, DWI score 3 lesions in the PZ cannot be upgraded based on DCE, necessitating adjustments in clinical diagnostic algorithms and decision-making protocols to ensure accurate risk stratification.

AI: reshaping the assessment paradigm of PI-RADS v2.1

AI technology is being integrated into the clinical workflow of prostate MRI with unprecedented depth and breadth, comprehensively reshaping the PI-RADS v2.1-centric diagnostic paradigm from image acquisition and lesion analysis to report generation (Figure 4).

Figure 4 Framework of an AI-augmented diagnostic workflow for prostate MRI, depicting the integration of AI technologies across key stages from image acquisition to reporting (78,80-87). AI, artificial intelligence; bpMRI, biparametric magnetic resonance imaging; csPCa, clinically significant prostate cancer; DCE, dynamic contrast-enhanced; DWI, diffusion-weighted imaging; GPT-4, Generative Pre-trained Transformer 4; MRI, magnetic resonance imaging;PI-CAI, Prostate Imaging Cancer Artificial Intelligence; PI-RADS, Prostate Imaging Reporting and Data System; PSA, prostate-specific antigen.

DL-based image analysis and diagnostic assistance

DL algorithms have been systematically applied to key tasks in prostate MRI analysis, including prostate segmentation, cancer detection, lesion segmentation, and PI-RADS-based lesion classification (81,82,88). The international, multi-center Prostate Imaging Cancer Artificial Intelligence (PI-CAI) study demonstrated that AI systems, in aggregate, outperformed the average performance of radiologists using PI-RADS v2.1 for detecting csPCa and achieved diagnostic efficacy comparable to contemporary multidisciplinary clinical practice, highlighting its potential as a mainstream diagnostic aid (83). In this context, fully automated AI diagnostic software (e.g., Quantib^® Prostate) has emerged, serving as an effective adjunct to PACS systems that significantly narrows the assessment gap between physicians of varying experience and improves diagnostic consistency (29,66). At an innovative level, DL demonstrates potential to disrupt traditional workflows, such as by generating high-quality synthetic DCE images from non-contrast sequences, offering a viable pathway to reduce contrast agent use (78).

Radiomics: mining quantitative features beyond visual perception

In contrast to DL’s focus on image interpretation, radiomics is dedicated to extracting a vast number of quantitative features, invisible to the human eye, from medical images, providing objective evidence beyond subjective visual assessment for the precise management of prostate cancer. In enhancing diagnostic performance, MRI-based radiomics models show significant advantages, particularly in patients within the PSA 4–10 ng/mL diagnostic “gray zone”, where their diagnostic efficacy is significantly superior to the PI-RADS v2.1 score (84). For clinically challenging PI-RADS category 3 lesions, studies confirm that machine learning-based radiomics models can effectively differentiate between benign and malignant lesions, thereby optimizing biopsy decisions (85,89). Furthermore, a multimodal approach fusing AI with PSA density has demonstrated considerable potential in reducing unnecessary biopsies (65).

Regarding the prediction of tumor aggressiveness, radiomic features extracted from T2WI and ADC maps correlate closely with Gleason scores. Machine learning models that integrate radiomic features with clinical indicators (e.g., prostate volume and PSA density) outperform PI-RADS v2.1 and PSAD alone in distinguishing csPCa (90), and can even predict high-grade tumors in radical prostatectomy specimens with diagnostic capability approaching that of preoperative biopsy (86). Although a 2024 systematic review affirmed this potential, the clinical translation of radiomics remains hampered by study heterogeneity, necessitating validation of its true value through prospective studies and standardized comparisons with PI-RADS (87).

Preliminary exploration of large language models in report processing

The influence of AI has extended from image analysis to the field of natural language processing. Preliminary studies have evaluated the capability of large language models (LLMs), such as ChatGPT and Gemini, to automatically assign PI-RADS categories based on MRI text reports (91). Results indicate that while the accuracy of newer models (e.g., GPT-4, Gemini: 83% and 79%, respectively) has improved significantly compared to their predecessors, it remains lower than that of senior radiologists (95%). These models exhibit inconsistent performance across high- and low-risk cases and occasionally demonstrate “hallucination” errors, such as fabricating a non-existent “PI-RADS 6” category. Therefore, despite their promise, LLMs currently require cautious, supervised use in clinical PI-RADS classification, and their reliability warrants further validation.

DL for accelerated MRI acquisition

DL technology is fundamentally optimizing the MRI acquisition process, bringing breakthroughs in shortening scan times and improving accessibility. For accelerated acquisition, DL reconstruction techniques applied to DWI sequences can reduce acquisition time by approximately 23% while maintaining or even enhancing image quality, a benefit particularly significant for improving high b-value DWI crucial for PI-RADS assessment (92). In the context of rapid screening, integrated DL-accelerated T2WI and DWI protocols enable diagnostic-quality bpMRI examinations to be completed within 4 minutes without compromising lesion detection or PI-RADS scoring (93,94). This breakthrough lays a solid technical foundation for the future implementation of large-scale prostate cancer screening.

Beyond PI-RADS v2.1: diagnostic potential of emerging MRI techniques

Several emerging MRI techniques demonstrate the potential to enhance the diagnostic performance of PI-RADS v2.1 by providing complementary quantitative information (Figure 5). Amide Proton Transfer-Weighted Imaging (APTWI), which probes protein metabolism, can significantly increase the AUC for diagnosing csPCa from 0.813 to 0.875 when its quantitative parameters are combined with ADC values (48). The quantitative index RSIrs from Restriction Spectrum Imaging (RSI) demonstrates diagnostic performance comparable to PI-RADS alone; their combination elevates the AUC to 0.85, indicating promising prospects for clinical translation (95). Magnetic resonance elastography (MRE), which quantifies tissue stiffness, can improve the diagnostic AUC from 0.79 to 0.86 when integrated with PI-RADS. The more advanced technique of tomoelastography, capable of assessing both stiffness and viscosity, can substantially increase the diagnostic AUC to 0.95 when used in conjunction with PI-RADS (96,97). Ultrahigh-performance MRI significantly enhances image quality through hardware optimization, laying the foundation for more precise PI-RADS scoring (98). Hybrid multidimensional MRI technology enables the simultaneous acquisition of multi-parametric information, significantly improving interpretation efficiency and inter-reader agreement while maintaining diagnostic performance (99). Collectively, these technologies provide feasible pathways for constructing a more accurate and efficient next-generation imaging diagnostic paradigm for prostate cancer.

Figure 5 Emerging MRI techniques that provide complementary quantitative information for the PI-RADS v2.1 assessment. APTWI, amide proton transfer-weighted imaging; AUC, area under the curve; MRE, magnetic resonance elastography; MRI, magnetic resonance imaging; PI-RADS v2.1, Prostate Imaging Reporting and Data System version 2.1; RSI, restriction spectrum imaging; SNR, signal‑to‑noise ratio.

Conclusions

PI-RADS v2.1 has markedly advanced the standardization of prostate MRI, leading to improved consistency in diagnosing csPCa. Its diagnostic utility is most effectively realized through the integration of imaging findings with clinical biomarkers—especially for stratifying risk in equivocal lesions. The bpMRI pathway now stands as a validated, efficient option for initial evaluation, and AI holds strong potential to further enhance interpretive accuracy and streamline workflows.

Moving forward, the evolution of prostate cancer diagnosis will be driven by the convergence of these approaches. Emerging quantitative MRI techniques supply valuable complementary data, and their incorporation into multiparametric predictive models—together with genomic and clinical profiles—will be essential to enable truly personalized management. Sustained research efforts aimed at prospective validation and clinical integration will be crucial to translate these innovations into broadly improved patient outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0193/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0193/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0193/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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