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, smaller prostate size, and increasing PSA density were independently associated with higher CDR, while prior prostate biopsy was associated with lower CDR. A solitary PI-RADS 3-5 lesion was independently associated with higher csCDR, while 2+ prior prostate biopsies was associated with a lower 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.