Background: The Prostate Imaging Reporting and Data System, version 2.1 (PI-RADSv2.1) standardizes reporting of multiparametric MRI of the prostate. Assigned assessment categories are a risk stratification algorithm, higher categories indicate a higher probability of clinically significant cancer compared to lower categories. PI-RADSv2.1 does not define these probabilities numerically. We conduct a systematic review and meta-analysis to determine the cancer detection rates (CDR) of the PI-RADSv2.1 assessment categories on lesion level and patient level. Methods: Two independent reviewers screen a systematic PubMed and Cochrane CENTRAL search for relevant articles (primary outcome: clinically significant cancer, index test: prostate MRI reading according to PI-RADSv2.1, reference standard: histopathology). We perform meta-analyses of proportions with random-effects models for the CDR of the PI-RADSv2.1 assessment categories for clinically significant cancer. We perform subgroup analysis according to lesion localization to test for differences of CDR between peripheral zone lesions and transition zone lesions. Results: A total of 17 articles meet the inclusion criteria and data is independently extracted by two reviewers. Lesion level analysis includes 1946 lesions, patient level analysis includes 1268 patients. On lesion level analysis, CDR are 2% (95% confidence interval: 0–8%) for PI-RADS 1, 4% (1–9%) for PI-RADS 2, 20% (13–27%) for PI-RADS 3, 52% (43–61%) for PI-RADS 4, 89% (76–97%) for PI-RADS 5. On patient level analysis, CDR are 6% (0–20%) for PI-RADS 1, 9% (5–13%) for PI-RADS 2, 16% (7–27%) for PI-RADS 3, 59% (39–78%) for PI-RADS 4, 85% (73–94%) for PI-RADS 5. Higher categories are significantly associated with higher CDR (P < 0.001, univariate meta-regression), no systematic difference of CDR between peripheral zone lesions and transition zone lesions is identified in subgroup analysis. Conclusions: Our estimates of CDR demonstrate that PI-RADSv2.1 stratifies lesions and patients as intended. Our results might serve as an initial evidence base to discuss management strategies linked to assessment categories.

This systematic review and meta-analysis provides an evidence synthesis of prostate MRI diagnostic performance based on PI-RADS version 2.1 for benchmarking different diagnostic metrics and outcome thresholds.

Background: Multiparametric MRI (mpMRI) improves the detection of aggressive prostate cancer (PCa) subtypes. As cases of active surveillance (AS) increase and tumor progression triggers definitive treatment, we evaluated whether an AI‐driven algorithm can detect clinically significant PCa (csPCa) in patients under AS.Methods: Consecutive patients under AS who received mpMRI (PI‐RADSv2.1 protocol) and subsequent MR‐guided ultrasound fusion (targeted and extensive systematic) biopsy between 2017 and 2020 were retrospectively analyzed. Diagnostic performance of an automated clinically certified AI‐driven algorithm was evaluated on both lesion and patient level regarding the detection of csPCa.Results: Analysis of 56 patients resulted in 93 target lesions. Patient level sensitivity and specificity of the AI algorithm was 92.5%/31% for the detection of ISUP ≥ 1 and 96.4%/25% for the detection of ISUP ≥ 2, respectively. The only case of csPCa missed by the AI harbored only 1/47 Gleason 7a core (systematic biopsy; previous and subsequent biopsies rendered non‐csPCa).Conclusions: AI‐augmented lesion detection and PI‐RADS scoring is a robust tool to detect progression to csPCa in patients under AS. Integration in the clinical workflow can serve as reassurance for the reader and streamline reporting, hence improve efficiency and diagnostic confidence.

Background: The increase in multiparametric magnetic resonance imaging (mpMRI) examinations as a fundamental tool in prostate cancer (PCa) diagnostics raises the need for supportive computer-aided imaging analysis. Therefore, we evaluated the performance of a commercially available AI-based algorithm for prostate cancer detection and classification in a multi-center setting. Methods: Representative patients with 3T mpMRI between 2017 and 2022 at three different university hospitals were selected. Exams were read according to the PI-RADSv2.1 protocol and then assessed by an AI algorithm. Diagnostic accuracy for PCa of both human and AI readings were calculated using MR-guided ultrasound fusion biopsy as the gold standard. Results: Analysis of 91 patients resulted in 138 target lesions. Median patient age was 67 years (range: 49–82), median PSA at the time of the MRI exam was 8.4 ng/mL (range: 1.47–73.7). Sensitivity and specificity for clinically significant prostate cancer (csPCa, defined as ISUP ≥ 2) were 92%/64% for radiologists vs. 91%/57% for AI detection on patient level and 90%/70% vs. 81%/78% on lesion level, respectively (cut-off PI-RADS ≥ 4). Two cases of csPCa were missed by the AI on patient-level, resulting in a negative predictive value (NPV) of 0.88 at a cut-off of PI-RADS ≥ 3. Conclusions: AI-augmented lesion detection and scoring proved to be a robust tool in a multi-center setting with sensitivity comparable to the radiologists, even outperforming human reader specificity on both patient and lesion levels at a threshold of PI-RADS ≥3 and a threshold of PI-RADS ≥ 4 on lesion level. In anticipation of refinements of the algorithm and upon further validation, AI-detection could be implemented in the clinical workflow prior to human reading to exclude PCa, thereby drastically improving reading efficiency.

Background/Aim: To investigate whether quantitative analysis of diffusion weighted images allows for improved risk stratification of transition zone lesions in prostate magnetic resonance imaging (MRI) evaluated according to PI-RADSv2.1 [Prostate Imaging Reporting and Data System, target variable: clinically significant prostate cancer (csPCa)]. Patients and Methods: Consecutive patients with transition zone lesions in 3T prostate MRI were enrolled in the study. All lesions on MRI were histopathologically verified by transperineal MRI-TRUS fusion biopsy. Two blinded radiologists re-evaluated all lesions according to PI-RADSv2.1. A consensus reading was performed after reading of all cases. Additionally, mean apparent diffusion coefficient values (mADC) were derived from blinded lesion segmentation. ROC analysis was performed for PI-RADS categories and PI-RADS categories with separate subcategories and diffusion coefficient values (ADC). Data were examined for optimal mADC cut-off values that improve stratification of csPCa and benign lesions. Results: Among 85 patients (mean age=66.2 years), 98 transition zone lesions were detected. Biopsy confirmed csPCa in 24/98 cases. Area under the curve (AUC) was 0.89/0.90 for reader 1, 0.92/0.91 for reader 2 and 0.92/0.91 for the consensus reading (5 category analysis/analysis with subcategories separately). Inter-reader agreement was substantial, with lower PI-RADS categories assigned by the more experienced reader (p<0.05). AUC for mADC alone was 0.81. When a cut-off threshold of 950 μm²/s mADC is used to downgrade PI-RADS 3 lesions to PI-RADS 2, biopsy could be avoided in all benign PI-RADS 3 cases. Conclusion: Quantitative analysis of diffusion weighted images may help avoid unnecessary biopsies of transition zone PI-RADS 3 lesions.

Purpose: To evaluate the diagnostic performance of a fully automated, commercially available AI algorithm for detecting prostate cancer and classifying lesions according to PI-RADS. Material and methods: In this retrospective single-center cohort study, we included consecutive patients with suspected prostate cancer who underwent 3T MRI between May 2017 and May 2020. Histopathological ground truth was targeted transperineal ultrasound-fusion guided biopsy and extensive systematic biopsy. We compared the results of the AI algorithm to those of human readers on both the lesion and patient level and determined the diagnostic performance. Results: A total of 272 patients with 436 target lesions were evaluated. Of these patients, 135 (49.6%) had clinically significant prostate cancer (sPCa), 35 (12.9%) had clinically insignificant prostate cancer (ISUP = 1), and 102 (37.5%) were benign. On patient level, the cancer detection rates of sPCa for AI versus human readers were 11% versus 18% for PI-RADS ≤ 2, 27% versus 11% for PI-RADS 3, 54% versus 41% for PI-RADS 4, and 74% versus 92% for PI-RADS 5. The AI showed significantly higher accuracy: 74% versus 63% for PI-RADS ≥ 4 (p < 0.01) and 70% versus 52% for PI-RADS ≥ 3 (p < 0.01). Additionally, the AI correctly classified 62 patients with human reading PI-RADS ≥ 3 as true negatives. Conclusion: The AI algorithm proved to be a reliable and robust tool for lesion detection and classification. Its cancer detection rates and PI-RADS category distribution align with the results of recent meta-analyses, indicating precise risk stratification.