Abstract

e17094 Background: The application of the Prostate Imaging Reporting and Data System (PI-RADS) has accelerated the use and promotion of multi-parameter magnetic resonance imaging (mpMRI) in prostate cancer (PCa) detection. Despite the high PCa detection rate, a considerable proportion of PI-RADS category 4 and 5 lesions are benign. The primary goal of this study is to develop and validate a prediction model for non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions. Methods: We retrospectively collected patients with PI-RADS category 4 or 5 lesions who underwent transperineal biopsies at West China Hospital from January 2018 to December 2022. Cases with prostate-specific antigen (PSA) > 20 ng/ml, PCa away from the area of the index lesion, and > 3 months from mpMRI to prostate biopsy were excluded. The included patients were randomly divided into a training cohort (70%) and a validation cohort (30%) for building and validating the prediction model, respectively. Logistic regression was used to screen independent predictors of non-PCa, and a nomogram was drawn based on the regression coefficients of the prediction model. The discrimination and calibration were assessed by the C-index and the calibration plots, respectively, the latter being quantified by the Brier score. Decision curve analysis (DCA) and clinical impact curve (CIC) were applied to measure the clinical net benefit. Results: A total of 1580 patients were included and the prevalence of non-PCa was 40.1%. Five independent predictors of non-PCa were incorporated into the optimal model, including age, prostate volume (PV), prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) values, and lesion zone. The C-index was 0.925 (95%CI, 0.910-0.939) and 0.931 (95%CI, 0.910-0.953) in the training cohort and validation cohort, respectively. Favorable agreement was exhibited from the calibration curve, with a Brier score of 0.104. The DCA demonstrated an increased net benefit of the nomogram over a wide range of threshold probabilities compared to biopsy-free-all or biopsy-free-none strategy. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2%, and 25.1% men forewent biopsies, respectively). Conclusions: We developed the first five-item predictive model based on quantitative ADC and PSAD in patients with PI-RADS category ≥ 4 lesions. In a risk-averse scenario, the strategy may safely identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies. [Table: see text]