A nomogram based on PI-RADS v2.1 and clinical indicators for predicting clinically significant prostate cancer in the transition zone

Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis

Background Prostate MRI for the detection of clinically significant prostate cancer (csPCa) is standardized by the Prostate Imaging Reporting and Data System (PI-RADS), currently in version 2.1. A systematic review and meta-analysis infrastructure with a 12-month update cycle was established to evaluate the diagnostic performance of PI-RADS over time. Purpose To provide estimates of diagnostic accuracy and cancer detection rates (CDRs) of PI-RADS version 2.1 categories for prostate MRI, which is required for further evidence-based patient management. Materials and Methods A systematic search of PubMed, Embase, Cochrane Library, and multiple trial registers (English-language studies published from March 1, 2019, to August 30, 2022) was performed. Studies that reported data on diagnostic accuracy or CDRs of PI-RADS version 2.1 with csPCa as the primary outcome were included. For the meta-analysis, pooled estimates for sensitivity, specificity, and CDRs were derived from extracted data at the lesion level and patient level. Sensitivity and specificity for PI-RADS greater than or equal to 3 and PI-RADS greater than or equal to 4 considered as test positive were investigated. In addition to individual PI-RADS categories 1-5, subgroup analyses of subcategories (ie, 2+1, 3+0) were performed. Results A total of 70 studies (11 686 lesions, 13 330 patients) were included. At the patient level, with PI-RADS greater than or equal to 3 considered positive, meta-analysis found a 96% summary sensitivity (95% CI: 95, 98) and 43% specificity (95% CI: 33, 54), with an area under the summary receiver operating characteristic (SROC) curve of 0.86 (95% CI: 0.75, 0.93). For PI-RADS greater than or equal to 4, meta-analysis found an 89% sensitivity (95% CI: 85, 92) and 66% specificity (95% CI: 58, 74), with an area under the SROC curve of 0.89 (95% CI: 0.85, 0.92). CDRs were as follows: PI-RADS 1, 6%; PI-RADS 2, 5%; PI-RADS 3, 19%; PI-RADS 4, 54%; and PI-RADS 5, 84%. The CDR was 12% (95% CI: 7, 19) for transition zone 2+1 lesions and 19% (95% CI: 12, 29) for 3+0 lesions (P = .12). Conclusion Estimates of diagnostic accuracy and CDRs for PI-RADS version 2.1 categories are provided for quality benchmarking and to guide further evidence-based patient management. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tammisetti and Jacobs in this issue.

A novel model incorporating quantitative contrast-enhanced ultrasound into PI-RADSv2-based nomogram detecting clinically significant prostate cancer

The diagnostic accuracy of clinically significant prostate cancer (csPCa) of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) is limited by subjectivity in result interpretation and the false positive results from certain similar anatomic structures. We aimed to establish a new model combining quantitative contrast-enhanced ultrasound, PI-RADSv2, clinical parameters to optimize the PI-RADSv2-based model. The analysis was conducted based on a data set of 151 patients from 2019 to 2022, multiple regression analysis showed that prostate specific antigen density, age, PI-RADSv2, quantitative parameters (rush time, wash-out area under the curve) were independent predictors. Based on these predictors, we established a new predictive model, the AUCs of the model were 0.910 and 0.879 in training and validation cohort, which were higher than those of PI-RADSv2-based model (0.865 and 0.821 in training and validation cohort). Net Reclassification Index analysis indicated that the new predictive model improved the classification of patients. Decision curve analysis showed that in most risk probabilities, the new predictive model improved the clinical utility of PI-RADSv2-based model. Generally, this new predictive model showed that quantitative parameters from contrast enhanced ultrasound could help to improve the diagnostic performance of PI-RADSv2 based model in detecting csPCa.

Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer: A Systematic Review and Meta-Analysis

Importance

Multiple strategies integrating magnetic resonance imaging (MRI) and clinical data have been proposed to determine the need for a prostate biopsy in men with suspected clinically significant prostate cancer (csPCa) (Gleason score ≥3 + 4). However, inconsistencies across different strategies create challenges for drawing a definitive conclusion.

Objective

To determine the optimal prostate biopsy decision-making strategy for avoiding unnecessary biopsies and minimizing the risk of missing csPCa by combining MRI Prostate Imaging Reporting & Data System (PI-RADS) and clinical data.

Data Sources

PubMed, Ovid MEDLINE, Embase, Web of Science, and Cochrane Library from inception to July 1, 2022.

Study Selection

English-language studies that evaluated men with suspected but not confirmed csPCa who underwent MRI PI-RADS followed by prostate biopsy were included. Each study had proposed a biopsy plan by combining PI-RADS and clinical data.

Data Extraction and Synthesis

Studies were independently assessed for eligibility for inclusion. Quality of studies was appraised using the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the Newcastle-Ottawa Scale. Mixed-effects meta-analyses and meta-regression models with multimodel inference were performed. Reporting of this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline.

Main Outcomes and Measures

Independent risk factors of csPCa were determined by performing meta-regression between the rate of csPCa and PI-RADS and clinical parameters. Yields of different biopsy strategies were assessed by performing diagnostic meta-analysis.

Results

The analyses included 72 studies comprising 36 366 patients. Univariable meta-regression showed that PI-RADS 4 (β-coefficient [SE], 7.82 [3.85]; P = .045) and PI-RADS 5 (β-coefficient [SE], 23.18 [4.46]; P < .001) lesions, but not PI-RADS 3 lesions (β-coefficient [SE], -4.08 [3.06]; P = .19), were significantly associated with a higher risk of csPCa. When considered jointly in a multivariable model, prostate-specific antigen density (PSAD) was the only clinical variable significantly associated with csPCa (β-coefficient [SE], 15.50 [5.14]; P < .001) besides PI-RADS 5 (β-coefficient [SE], 9.19 [3.33]; P < .001). Avoiding biopsy in patients with lesions with PI-RADS category of 3 or less and PSAD less than 0.10 (vs <0.15) ng/mL2 resulted in reducing 30% (vs 48%) of unnecessary biopsies (compared with performing biopsy in all suspected patients), with an estimated sensitivity of 97% (vs 95%) and number needed to harm of 17 (vs 15).

Conclusions and Relevance

These findings suggest that in patients with suspected csPCa, patient-tailored prostate biopsy decisions based on PI-RADS and PSAD could prevent unnecessary procedures while maintaining high sensitivity.

Nomograms for predicting clinically significant prostate cancer in men with PI-RADS-3 biparametric magnetic resonance imaging

This study aimed to construct nomograms for predicting the likelihood of clinically significant prostate cancer (csPCa) in patients with lesions rated as Prostate Imaging Reporting and Data System (PI-RADS) 3 on biparametric magnetic resonance imaging (bpMRI). We retrospectively analyzed a cohort of 457 patients from the Peking Union Medical College Hospital (January 2017-July 2021) to develop the model and externally validated it with a cohort of 238 patients from the Second Hospital of Tianjin Medical University (September 2017-September 2021). Univariate and multivariate logistic regression analyses identified significant predictors of csPCa, defined by tumor volumes ≥ 0.5 cm3, Gleason score ≥ 7, or presence of extracapsular extension. Diagnostic performance for the peripheral zone (PZ) and transitional zone (TZ) was compared using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Through univariate and multivariate logistic regression analyses, we identified age, prostate-specific antigen (PSA), and prostate volume (PV) as predictors of csPCa for the PZ, and age, serum-free to total PSA ratio (f/t PSA), and PSA density (PSAD) for the TZ. The nomograms demonstrated robust discriminative ability, with an area under the ROC curve (AUC) of 0.819 for PZ and 0.804 for TZ. The external validation corroborated the model's high predictive accuracy (AUC of 0.831 for PZ and 0.773 for TZ). Calibration curves indicated excellent agreement between predicted and observed outcomes, and DCA underscored the nomogram's clinical utility for both PZ and TZ. Overall, the nomograms offer high predictive accuracy for csPCa at initial biopsy, potentially reducing unnecessary biopsies in clinical settings.

Exploration of the diagnostic capacity of PSAMR combined with PI-RADS scoring for clinically significant prostate cancer and establishment and validation of the Nomogram prediction model

PURPOSE

The objective of this investigation was to explore the diagnostic capability of Prostate Specific Antigen Mass Ratio (PSAMR) combined with Prostate Imaging Reporting and Data System (PI-RADS) scoring for clinically significant prostate cancer (CSPC), develop and validate a Nomogram prediction model for the probability of prostate cancer occurrence in patients who have not undergone prostate biopsy.

METHODS

Initially, we retrospectively collected clinical and pathological data of patients who underwent trans-perineal prostate puncture at Yijishan Hospital of Wanan Medical College from July 2021 to January 2023. Through logistic univariate and multivariate regression analysis, independent risk factors for CSPC were determined. Receiver Operating Characteristic (ROC) curves were generated to compare the ability of different factors for diagnosis of CSPC. Then, we split the dataset into a training set and validation set, compared their heterogeneity, and developed a Nomogram prediction model based on the training set. Finally, we validated the Nomogram prediction model in terms of discrimination, calibration, and clinical usefulness.

RESULTS

Logistic multivariate regression analysis illustrated that age [64-69 (OR = 2.736, P = 0.029); 69-75 (OR = 4.728, P = 0.001); > 75 (OR = 11.344, P < 0.001)], PSAMR [0.44-0.73 (OR = 4.144, P = 0.028); 0.73-1.64(OR = 13.022, P < 0.001); > 1.64(OR = 50.541, P < 0.001)], and PI-RADS score [4 points (OR = 7.780, P < 0.001); 5 points (OR = 24.533, P < 0.001)] were independent risk factors for CSPC. The Area Under the Curve (AUC) of the ROC curves of PSA, PSAMR, PI-RADS score, and PSAMR combined with PI-RADS score were respectively 0.797, 0.874, 0.889, and 0.928. The performance of PSAMR and PI-RADS score for diagnosis of CSPC was superior to PSA, but inferior to PSAMR combined with PI-RADS. Age, PSAMR, and PI-RADS were included in the Nomogram prediction model. The AUCs of the training set ROC curve and the validation set ROC curve were 0.943 (95% CI 0.917-0.970) and 0.878 (95% CI 0.816-0.940), respectively, in the discrimination validation. The calibration curve showed good consistency, and the decision analysis curve suggested the model had good clinical efficacy.

CONCLUSIONS

We found that PSAMR combined with PI-RADS scoring had a strong diagnostic capability for CSPC, and provided a Nomogram prediction model to predict the probability of prostate cancer occurrence combined with clinical data.

The Impact of Prostate Imaging Reporting and Data System Version 2.1 and Prostate-Specific Antigen Density in the Prediction of Clinically Significant Prostate Cancer

OBJECTIVE

The aim of this study was to evaluate the diagnostic performance of multiparametric magnetic resonance imaging for clinically significant prostate cancer and to determine whether applying Prostate Imaging Reporting and Data Systems version 2.1 score could improve the diagnostic pathway besides the biochemical characteristics.

MATERIALS AND METHODS

In this study, 199 patients with clinically suspected prostate cancer who underwent multiparametric magnetic resonance imaging were included. Logistic regression analyses and receiver operating characteristic curve were performed to determine independent predictors and to compare diagnostic performance of indicators for clinically significant prostate cancer. Two models were established. In model 1, the diagnostic performance of prostate-specific antigen- and prostatespecific antigen density-derived parameters were evaluated. In model 2, the prediction potential of model 1 plus Prostate Imaging Reporting and Data Systems version 2.1 score was analyzed.

RESULTS

Sixty-four patients were positive for clinically significant prostate cancer by histopathological analysis (32.1%). In model 1, a prostate-specific antigen density >0.15 was labeled as the strongest predictor of malignancy. In model 2, a prostatespecific antigen density >0.15, a Prostate Imaging Reporting and Data Systems score ≥3, and a Prostate Imaging Reporting and Data Systems score ≥4 demonstrated the strongest association with malignancy. Among these parameters, a Prostate Imaging Reporting and Data Systems score ≥4 (P=.003) was found to be the most robust predictor for malignancy, followed by a Prostate Imaging Reporting and Data Systems score ≥3 (P=.012). The multivariate analysis revealed higher accuracy in model 2 (76.9%) than in model 1 (67.8%). The area under curve values with respect to prostatespecific antigen, prostate-specific antigen density, model 1, and model 2 were 0.632, 0.741, 0.656, and 0.798, respectively.

CONCLUSION

These results indicated that Prostate Imaging Reporting and Data Systems version 2.1 score and prostate-specific antigen density are independent predictors for the presence of clinically significant prostate cancer. Both prostate-specific antigen density and Prostate Imaging Reporting and Data Systems version 2.1 score should be risen to prominence in the decision of biopsy instead of PSA.

Detection of grey zones in inter-rater agreement studies

BACKGROUND

In inter-rater agreement studies, the assessment behaviour of raters can be influenced by their experience, training levels, the degree of willingness to take risks, and the availability of clear guidelines for the assessment. When the assessment behaviour of raters differentiates for some levels of an ordinal classification, a grey zone occurs between the corresponding adjacent cells to these levels around the main diagonal of the table. A grey zone introduces a negative bias to the estimate of the agreement level between the raters. In that sense, it is crucial to detect the existence of a grey zone in an agreement table.

METHODS

In this study, a framework composed of a metric and the corresponding threshold is developed to identify grey zones in an agreement table. The symmetry model and Cohen's kappa are used to define the metric, and the threshold is based on a nonlinear regression model. A numerical study is conducted to assess the accuracy of the developed framework. Real data examples are provided to illustrate the use of the metric and the impact of identifying a grey zone.

RESULTS

The sensitivity and specificity of the proposed framework are shown to be very high under moderate, substantial, and near-perfect agreement levels for [Formula: see text] and [Formula: see text] tables and sample sizes greater than or equal to 100 and 50, respectively. Real data examples demonstrate that when a grey zone is detected in the table, it is possible to report a notably higher level of agreement in the studies.

CONCLUSIONS

The accuracy of the proposed framework is sufficiently high; hence, it provides practitioners with a precise way to detect the grey zones in agreement tables.

PI-RADS v2.1 Combined With Prostate-Specific Antigen Density for Detection of Prostate Cancer in Peripheral Zone

Purpose

To evaluate the diagnostic performance of combining the Prostate Imaging Reporting and Data System (PI-RADS) scoring system v2.1 with prostate-specific antigen density (PSAD) to detect prostate cancer (PCa).

Methods

A total of 266 participants with suspicion of PCa underwent multiparametric magnetic resonance imaging (mpMRI) in our hospital, after at least 4 weeks all patients underwent subsequent systematic transrectal ultrasound (TRUS)-guided biopsy or MRI-TRUS fusion targeted biopsy. All mpMRI images were scored in accordance with the PI-RADS v2.1, and univariate and multivariate logistic regression analyses were performed to determine significant predictors of PCa.

Results

A total of 119 patients were diagnosed with PCa in the biopsy, of them 101 patients were diagnosed with clinically significant PCa. The multivariate analysis revealed that PI-RADS v2.1 and PSAD were independent predictors for PCa. For peripheral zone (PZ), the area under the ROC curve (AUC) for the combination of PI-RADS score and PSAD was 0.90 (95% CI 0.83-0.96), which is significantly superior to using PI-RADS score (0.85, 95% CI 0.78-0.93, P=0.031) and PSAD alone (0.83, 95% CI 0.75-0.90, P=0.037). For transition zone (TZ), however, the combination model was not significantly superior to PI-RADS alone, with AUC of 0.94 (95% CI 0.89-0.99) vs. 0.93 (95% CI 0.88-0.97, P=0.186).

Conclusion

The combination of PI-RADS v2.1 with PSAD could significantly improve the diagnostic performance of PCa in PZ. Nevertheless, no significant improvement was observed regarding PCa in TZ.

The Role of PSA Density among PI-RADS v2.1 Categories to Avoid an Unnecessary Transition Zone Biopsy in Patients with PSA 4-20 ng/mL

Objective

To evaluate the role of prostate-specific antigen density (PSAD) in different Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) categories to avoid an unnecessary biopsy in transition zone (TZ) patients with PSA ranging from 4 to 20 ng/mL.

Materials and Methods

In this retrospective and single-center study, 333 biopsy-naïve patients with TZ lesions who underwent biparametric magnetic resonance imaging (bp-MRI) were analyzed from January 2016 to March 2020. Multivariate logistic regression analyses were performed to determine independent predictors of clinically significant prostate cancer (cs-PCa). The receiver operating characteristic (ROC) curve was used to compare diagnostic performance.

Results

PI-RADS v2.1 and PSAD were the independent predictors for TZ cs-PCa in patients with PSA 4-20 ng/mL. 0.9% (2/213), 10.0% (7/70), and 48.0% (24/50) of PI-RADS v2.1 score 1-2, 3, and 4-5 had TZ cs-PCa. However, for patients with PI-RADS v2.1 score 1-2, there were no obvious changes in the detection of TZ cs-PCa (0.8% (1/129), 1.3% (1/75), and 0.0% (0/9)) combining with different PSAD stratification (PSAD < 0.15, 0.15-0.29, and ≥0.30 ng/mL/mL). For patients with PI-RADS v2.1 score ≥ 3, the TZ cs-PCa detection rate significantly varied according to different PSAD stratification. A PI-RADS v2.1 score 3 and PSAD < 0.15 and 0.15-0.29 ng/mL/mL had 8.6% (3/35) and 3.7% (1/27) of TZ cs-PCa, while a PI-RADS v2.1 score 3 and PSAD ≥ 0.30 ng/mL/mL had a higher TZ cs-PCa detection rate (37.5% (3/8)). A PI-RADS v2.1 score 4-5 and PSAD <0.15 ng/mL/mL had no cs-PCa (0.0% (0/9)). In contrast, a PI-RADS v2.1 score 4-5 and PSAD 0.15-0.29 and ≥0.30 ng/mL/mL had the highest cs-PCa detection rate (50.0% (10/20), 66.7% (14/21)). It showed the highest AUC in the combination of PI-RADS v2.1 and PSAD (0.910), which was significantly higher than PI-RADS v2.1 (0.889, P = 0.039) or PSAD (0.803, P < 0.001).

Conclusions

For TZ patients with PSA 4-20 ng/mL, PI-RADS v2.1 score ≤ 2 can avoid an unnecessary biopsy regardless of PSAD. PI-RADS v2.1 score ≥ 3 may avoid an unnecessary biopsy after combining with PSAD. PI-RADS v2.1 combined with PSAD could significantly improve diagnostic performance.