Predicting cancer detection rates from multiparametric prostate MRI: Beyond the PI-RADS classification system.

Abstract

Introduction: Although the Prostate Imaging-Reporting and Data System (PI RADS) categorization represents the standard method for assessing the risk of prostate cancer using prostate magnetic resonance imaging (MRI), there exists wide variation in cancer detection rates (CDRs) in real-world practice. We therefore evaluated the association of clinical and radiographic features with CDRs and developed a predictive model to improve clinical management.

Methods: We identified men aged 18-89 years with elevated prostate-specfic antigen (PSA) or on active surveillance for prostate cancer who underwent MRI-ultrasound (US) fusion biopsy or in-bore MRI-targeted biopsy. The associations of features with the per-lesion CDR (Gleason 6- 10) and clinically significant (cs) CDR (Gleason 7-10) were examined using logistic regression, and results were operationalized into a predictive model.

Results: Targeted biopsy was performed for 347 lesions in 281 patients. Overall, the CDR was 49.0% and the csCDR was 28.0%. On multivariable analysis, increasing PI-RADS category, no prior prostate biopsies, smaller prostate size, and increasing PSA density were independently associated with higher CDR, while 0-1 prior prostate biopsies, and a solitary PI-RADS 3-5 lesion were associated with higher csCDR. A predictive model provided a greater net benefit than a strategy of performing biopsy in all PI-RADS 3-5 lesions across a wide range of threshold probabilities.

Conclusions: Several clinical and radiographic features are independently associated with the risk of prostate cancer in men undergoing MRI-targeted biopsy. A predictive model based on these features can improve clinical decisions regarding biopsy compared to the conventional strategy of performing biopsy for all PI-RADS 3-5 lesions.