How to implement magnetic resonance imaging before prostate biopsy in clinical practice: nomograms for saving biopsies.

Purpose: To combine multiparametric MRI (mpMRI) findings and clinical parameters to provide nomograms for diagnosing different scenarios of aggressiveness of prostate cancer (PCa).
Methods: A cohort of 346 patients with suspicion of PCa because of abnormal finding in digital rectal examination (DRE) and/or high prostate specific antigen (PSA) level received mpMRI prior to prostate biopsy (PBx). A conventional 12-core transrectal PBx with two extra cores from suspicious areas in mpMRI was performed by cognitive fusion. Multivariate logistic regression analysis was performed combining age, PSA density (PSAD), DRE, number of previous PBx, and mpMRI findings to predict three different scenarios: PCa, significant PCa (ISUP-group ≥ 2), or aggressive PCa (ISUP-group ≥ 3). We validate models by ROC curves, calibration plots, probability density functions (PDF), and clinical utility curves (CUC). Cut-off probabilities were estimated for helping decision-making in clinical practice.
Results: Our cohort showed 39.6% incidence of PCa, 32.6% of significant PCa, and 23.4% of aggressive PCa. The AUC of predictive models were 0.856, 0.883, and 0.911, respectively. The PDF and CUC showed 11% missed diagnoses of significant PCa (35 cases of 326 significant PCa expected in 1000 proposed Bx) when choosing < 18% as the cutoff of probability for not performing PBx; the percentage of saved PBx was 47% (474 avoided PBx in 1000 proposed).
Conclusion: We developed clinical and mpMRI-based nomograms with a high discrimination ability for three different scenarios of PCa aggressiveness (https://urostatisticalsolutions.shinyapps.io/MRIfusionPCPrediction/). Specific clinical cutoff points allow us to save a high number of PBx with a minimum of missed diagnoses.