Development of a model to predict prostate cancer at the apex (PCAP model) in patients undergoing robot-assisted radical prostatectomy

Abstract

Purpose: To develop a model based on preoperative variables to predict apical prostate cancer.

Methods: We performed a retrospective analysis of 459 patients who underwent a robotic assisted radical prostatectomy (RALP) between January 2016 and September 2017. All patients had a preoperative biopsy and mpMRI of the prostate. Significant apical pathology (SAP) was defined as those patients who had a dominant nodule at the apex with a Gleason score > 6 and/or ECE at the apex. Binary logistic regression analyses were adopted to predict SAP. Variables included in the model were PSA, apical lesions prostate imaging reporting and data system (PI-RADS) score and apical biopsy Gleason score. The area under the curve (AUC) of the model was computed.

Results: A total of 121 (43.2%) patients had SAP. On univariable analysis, all apex-specific variables investigated emerged as predictors of SAP (all p < 0.05). On multivariable analysis PSA and apical PI-RADS score > 3 (all p < 0.05) emerged as significant predictors of SAP. The AUC of the model was 0.722.

Conclusion: Patients with PI-RADS 3, 4 or 5 lesions at the apex were three times as more likely to have true SAP compared to those who have PI-RADS < 3 or negative mpMRI prior to undergoing RALP.

Keywords: Apex; Multiparametric MRI; PI-RADS; Prostate cancer.