The stanford prostate cancer calculator: Development and external validation of online nomograms incorporating PIRADS scores to predict clinically significant prostate cancer

Utility of tumor diameter-to-prostate volume ratio for predicting the outcome of magnetic resonance imaging/transrectal ultrasound fusion-targeted biopsy

OBJECTIVES

To assess the impact of the tumor diameter-to-prostate volume ratio (TD/PV) on predicting prostate cancer (PCa) and clinically significant (cs) -PCa in magnetic resonance imaging (MRI) /transrectal ultrasound (TRUS) fusion-targeted biopsy based on prostate imaging-reporting and data system (PI-RADS) findings in MRI.

MATERIALS AND METHODS

Patients who underwent MRI/TRUS fusion-targeted biopsy for PI-RADS version 2.1 categories 3-5 lesions between 2017 and 2023 were retrospectively reviewed. TD/PV was calculated by dividing the tumor diameter by the total prostate volume. csPCa was defined as a Gleason score of ≥ 3 + 4. Univariable and multivariable logistic regression analyses were used to develop predictive nomograms for PCa and csPCa. A receiver operating characteristic curve was constructed to evaluate the predictive ability of the factors using the area under the curve (AUC).

RESULTS

A total of 565 patients were analyzed; the AUC of TD/PV was significantly superior to those of the prostate-specific antigen (PSA), tumor diameter, PSA density, and PI-RADS for predicting PCa (AUC: 0.840, P < 0.05) and csPCa (AUC: 0.819, P < 0.05). Multivariable analyses showed that TD/PV is a significant predictive factor for PCa and csPCa in MRI/TRUS fusion-targeted biopsy (P < 0.05). The predictive nomograms combining TD/PV and PI-RADS category were constructed and their AUCs for predicting PCa and csPCa were 0.861 and 0.845, respectively.

CONCLUSIONS

In this retrospective analysis, prediction of PCa and csPCa on MRI/TRUS fusion-targeted biopsy was improved when TD/PV was combined with PI-RADS category.

Variation in Prostate Magnetic Resonance Imaging Performance: Data from the Prostate Biopsy Collaborative Group

BACKGROUND AND OBJECTIVE

The quality and reporting of prostate magnetic resonance imaging (MRI) are operator dependent, leading to variations in estimates such as positive predictive value across sites. This impacts patient counseling, risk modeling, and risk calculators. This study assessed variation in Prostate Imaging Reporting and Data System (PI-RADS) score classification and subsequent probability of grade group (GG) ≥2 + prostate cancer.

METHODS

Data from the Prostate Biopsy Collaborative Group, including multiple sites in North America, Europe, and Asia Pacific, were analyzed. Patients underwent multiparametric MRI (mpMRI) of the prostate followed by prostate biopsy during the years 2010-2023. Only those with MRI-targeted biopsy and PI-RADS score ≥3 were included. The risk of being assigned PI-RADS 4 or 5 and risk of GG ≥2 disease for these scores were estimated using logistic regression.

KEY FINDINGS AND LIMITATIONS

The cohort included 7325 biopsies from 7320 unique patients from 13 sites. A two-fold variation in the probability of PI-RADS 4 or 5 assignment across sites persisted even after adjustment for patient risk (heterogeneity p < 0.001 for both). There were significant differences in the absolute risk of GG ≥2 disease for PI-RADS 4 and 5 (heterogeneity p < 0.001 for both), varying between 23% and 68% and between 49% and 87%, respectively. The use of prostate biopsy as a reference standard has limitations but reflects typical usage of mpMRI in clinical practice.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The probability of being assigned PI-RADS 4 or 5 and subsequent detection of GG ≥2 disease varies widely between institutions. This impacts counseling, risk stratification, and clinical practice, necessitating better standardization in the performance and interpretation of mpMRI.

Repeat Prostate-specific Antigen Testing Improves Risk-based Selection of Men for Prostate Biopsy After Magnetic Resonance Imaging

Background and objective

The aim of our study was to investigate whether repeat prostate-specific antigen (PSA) testing as currently recommended improves risk stratification for men undergoing magnetic resonance imaging (MRI) and targeted biopsy for suspected prostate cancer (PCa).

Methods

Consecutive men undergoing MRI and prostate biopsy who had at least two PSA tests before prostate biopsy were retrospectively registered and assigned to a development cohort (n = 427) or a validation (n = 174) cohort. Change in PSA level was assessed as a predictor of clinically significant PCa (csPCa; Gleason score ≥3 + 4, grade group ≥2) by multivariable logistic regression analysis. We developed a multivariable prediction model (MRI-RC) and a dichotomous biopsy decision strategy incorporating the PSA change. The performance of the MRI-RC model and dichotomous decision strategy was assessed in the validation cohort and compared to prediction models and decision strategies not including PSA change in terms of discriminative ability and decision curve analysis.

Results

Men who had a decrease on repeat PSA testing had significantly lower risk of csPCa than men without a decrease (odds ratio [OR] 0.3, 95% confidence interval [CI] 0.16-0.54; p < 0.001). Men with an increased repeat PSA had a significantly higher risk of csPCa than men without an increase (OR 2.97, 95% CI 1.62-5.45; p < 0.001). Risk stratification using both the MRI-RC model and the dichotomous decision strategy was improved by incorporating change in PSA as a parameter.

Conclusions and clinical implications

Repeat PSA testing gives predictive information regarding men undergoing MRI and targeted prostate biopsy. Inclusion of PSA change as a parameter in an MRI-RC model and a dichotomous biopsy decision strategy improves their predictive performance and clinical utility without requiring additional investigations.

Patient summary

For men with a suspicion of prostate cancer, repeat PSA (prostate-specific antigen) testing after an MRI (magnetic resonance imaging) scan can help in identifying patients who can safely avoid prostate biopsy.

Risk calculators for the detection of prostate cancer: a systematic review

BACKGROUND

Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm.

METHODS

We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men.

RESULTS

We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93.

CONCLUSIONS

This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.

Predicting clinically significant prostate cancer following suspicious mpMRI: analyses from a high-volume center

PURPOSE

mpMRI is routinely used to stratify the risk of clinically significant prostate cancer (csPCa) in men with elevated PSA values before biopsy. This study aimed to calculate a multivariable risk model incorporating standard risk factors and mpMRI findings for predicting csPCa on subsequent prostate biopsy.

METHODS

Data from 677 patients undergoing mpMRI ultrasound fusion biopsy of the prostate at the TUM University Hospital tertiary urological center between 2019 and 2023 were analyzed. Patient age at biopsy (67 (median); 33-88 (range) (years)), PSA (7.2; 0.3-439 (ng/ml)), prostate volume (45; 10-300 (ml)), PSA density (0.15; 0.01-8.4), PI-RADS (V.2.0 protocol) score of index lesion (92.2% ≥3), prior negative biopsy (12.9%), suspicious digital rectal examination (31.2%), biopsy cores taken (12; 2-22), and pathological biopsy outcome were analyzed with multivariable logistic regression for independent associations with the detection of csPCa defined as ISUP ≥ 3 (n = 212 (35.2%)) and ISUP ≥ 2 (n = 459 (67.8%) performed on 603 patients with complete information.

RESULTS

Older age (OR: 1.64 for a 10-year increase; p < 0.001), higher PSA density (OR: 1.60 for a doubling; p < 0.001), higher PI-RADS score of the index lesion (OR: 2.35 for an increase of 1; p < 0.001), and a prior negative biopsy (OR: 0.43; p = 0.01) were associated with csPCa.

CONCLUSION

mpMRI findings are the dominant predictor for csPCa on follow-up prostate biopsy. However, PSA density, age, and prior negative biopsy history are independent predictors. They must be considered when discussing the individual risk for csPCa following suspicious mpMRI and may help facilitate the further diagnostical approach.

Comparison of Magnetic Resonance Imaging-Based Risk Calculators to Predict Prostate Cancer Risk

Importance

Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms.

Objective

To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America.

Design, Setting, and Participants

This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy.

Interventions

Prostate MRI followed by prostate biopsy.

Main Outcomes and Measures

The primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated.

Results

A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort.

Conclusions and Relevance

In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.

Performance of magnetic resonance imaging-based prostate cancer risk calculators and decision strategies in two large European medical centres

OBJECTIVES

To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa).

PATIENTS AND METHODS

We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%-20%.

RESULTS

A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63-72) years, 9 (7-15) ng/mL, and 0.20 (0.13-0.32) ng/mL2 , respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD.

CONCLUSIONS

Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.

Prostate cancer risk stratification using magnetic resonance imaging-ultrasound fusion vs systematic prostate biopsy

BACKGROUND

Image-guided approaches improve the diagnostic yield of prostate biopsy and frequently modify estimates of clinical risk. To better understand the impact of magnetic resonance imaging-ultrasound fusion targeted biopsy (MRF-TB) on risk assessment, we compared the distribution of National Comprehensive Cancer Network (NCCN) risk groupings, as calculated from MRF-TB vs systematic biopsy alone.

METHODS

We performed a retrospective analysis of 713 patients who underwent MRF-TB from January 2017 to July 2021. The primary study objective was to compare the distribution of National Comprehensive Cancer Network risk groupings obtained using MRF-TB (systematic + targeted) vs systematic biopsy.

RESULTS

Systematic biopsy alone classified 10% of samples as very low risk and 18.7% of samples as low risk, while MRF-TB classified 10.5% of samples as very low risk and 16.1% of samples as low risk. Among patients with benign findings, low-risk disease, and favorable/intermediate-risk disease on systematic biopsy alone, 4.6% of biopsies were reclassified as high risk or very high risk on MRF-TB. Of 207 patients choosing active surveillance, 64 (31%), 91 (44%), 42 (20.2%), and 10 (4.8%) patients were classified as having very low-risk, low-risk, and favorable/intermediate-risk and unfavorable/intermediate-risk criteria, respectively. When using systematic biopsy alone, 204 patients (28.7%) were classified as having either very low-risk and low-risk disease per NCCN guidelines, while 190 men (26.6%) received this classification when using MRF-TB.

CONCLUSION

The addition of MRF-TB to systematic biopsy may change eligibility for active surveillance in only a small proportion of patients with prostate cancer. Our findings support the need for routine use of quantitative risk assessment over risk groupings to promote more nuanced decision making for localized cancer.

Multi-institutional analysis of clinical and imaging risk factors for detecting clinically significant prostate cancer in men with PI-RADS 3 lesions

BACKGROUND

Most Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions do not contain clinically significant prostate cancer (CSPCa; grade group ≥2). This study was aimed at identifying clinical and magnetic resonance imaging (MRI)-derived risk fac- tors that predict CSPCa in men with PI-RADS 3 lesions.

METHODS

This study analyzed the detection of CSPCa in men who underwent MRI-targeted biopsy for PI-RADS 3 lesions. Multivariable logistic regression models with goodness-of-fit testing were used to identify variables associated with CSPCa. Receiver operating curves and decision curve analyses were used to estimate the clinical utility of a predictive model.

RESULTS

Of the 1784 men reviewed, 1537 were included in the training cohort, and 247 were included in the validation cohort. The 309 men with CSPCa (17.3%) were older, had a higher prostate-specific antigen (PSA) density, and had a greater likelihood of an anteriorly located lesion than men without CSPCa (p < .01). Multivariable analysis revealed that PSA density (odds ratio [OR], 1.36; 95% confidence interval [CI], 1.05-1.85; p < .01), age (OR, 1.05; 95% CI, 1.02-1.07; p < .01), and a biopsy-naive status (OR, 1.83; 95% CI, 1.38-2.44) were independently associated with CSPCa. A prior negative biopsy was negatively associated (OR, 0.35; 95% CI, 0.24-0.50; p < .01). The application of the model to the validation cohort resulted in an area under the curve of 0.78. A predicted risk threshold of 12% could have prevented 25% of biopsies while detecting almost 95% of CSPCas with a sensitivity of 94% and a specificity of 34%.

CONCLUSIONS

For PI-RADS 3 lesions, an elevated PSA density, older age, and a biopsy-naive status were associated with CSPCa, whereas a prior negative biopsy was negatively associated. A predictive model could prevent PI-RADS 3 biopsies while missing few CSPCas.

LAY SUMMARY

Among men with an equivocal lesion (Prostate Imaging-Reporting and Data System 3) on multiparametric magnetic resonance imaging (mpMRI), those who are older, those who have a higher prostate-specific antigen density, and those who have never had a biopsy before are at higher risk for having clinically significant prostate cancer (CSPCa) on subsequent biopsy. However, men with at least one negative biopsy have a lower risk of CSPCa. A new predictive model can greatly reduce the need to biopsy equivocal lesions noted on mpMRI while missing only a few cases of CSPCa.

Augmenting prostate magnetic resonance imaging reporting to incorporate diagnostic recommendations based upon clinical risk calculators

Risk calculators have offered a viable tool for clinicians to stratify patients at risk of prostate cancer (PCa) and to mitigate the low sensitivity and specificity of screening prostate specific antigen (PSA). While initially based on clinical and demographic data, incorporation of multiparametric magnetic resonance imaging (MRI) and the validated prostate imaging reporting and data system suspicion scoring system has standardized and improved risk stratification beyond the use of PSA and patient parameters alone. Biopsy-naïve patients with lower risk profiles for harboring clinically significant PCa are often subjected to uncomfortable, invasive, and potentially unnecessary prostate biopsy procedures. Incorporating risk calculator data into prostate MRI reports can broaden the role of radiologists, improve communication with clinicians primarily managing these patients, and help guide clinical care in directing the screening, detection, and risk stratification of PCa.

The Mount Sinai Prebiopsy Risk Calculator for Predicting any Prostate Cancer and Clinically Significant Prostate Cancer: Development of a Risk Predictive Tool and Validation with Advanced Neural Networking, Prostate Magnetic Resonance Imaging Outcome Database, and European Randomized Study of Screening for Prostate Cancer Risk Calculator

Background

The European Association of Urology guidelines recommend the use of imaging, biomarkers, and risk calculators in men at risk of prostate cancer. Risk predictive calculators that combine multiparametric magnetic resonance imaging with prebiopsy variables aid as an individualized decision-making tool for patients at risk of prostate cancer, and advanced neural networking increases reliability of these tools.

Objective

To develop a comprehensive risk predictive online web-based tool using magnetic resonance imaging (MRI) and clinical data, to predict the risk of any prostate cancer (PCa) and clinically significant PCa (csPCa) applicable to biopsy-naïve men, men with a prior negative biopsy, men with prior positive low-grade cancer, and men with negative MRI.

Design setting and participants

Institutional review board-approved prospective data of 1902 men undergoing biopsy from October 2013 to September 2021 at Mount Sinai were collected.

Outcome measurements and statistical analysis

Univariable and multivariable analyses were used to evaluate clinical variables such as age, race, digital rectal examination, family history, prostate-specific antigen (PSA), biopsy status, Prostate Imaging Reporting and Data System score, and prostate volume, which emerged as predictors for any PCa and csPCa. Binary logistic regression was performed to study the probability. Validation was performed with advanced neural networking (ANN), multi-institutional European cohort (Prostate MRI Outcome Database [PROMOD]), and European Randomized Study of Screening for Prostate Cancer Risk Calculator (ERSPC RC) 3/4.

Results and limitations

Overall, 2363 biopsies had complete clinical information, with 57.98% any cancer and 31.40% csPCa. The prediction model was significantly associated with both any PCa and csPCa having an area under the curve (AUC) of 81.9% including clinical data. The AUC for external validation was calculated in PROMOD, ERSPC RC, and ANN for any PCa (0.82 vs 0.70 vs 0.90) and csPCa (0.82 vs 0.78 vs 0.92), respectively. This study is limited by its retrospective design and overestimation of csPCa in the PROMOD cohort.

Conclusions

The Mount Sinai Prebiopsy Risk Calculator combines PSA, imaging and clinical data to predict the risk of any PCa and csPCa for all patient settings. With accurate validation results in a large European cohort, ERSPC RC, and ANN, it exhibits its efficiency and applicability in a more generalized population. This calculator is available online in the form of a free web-based tool that can aid clinicians in better patients counseling and treatment decision-making.

Patient summary

We developed the Mount Sinai Prebiopsy Risk Calculator (MSP-RC) to assess the likelihood of any prostate cancer and clinically significant disease based on a combination of clinical and imaging characteristics. MSP-RC is applicable to all patient settings and accessible online.

A review of artificial intelligence in prostate cancer detection on imaging

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.