Predictive clinical characteristics for adverse pathological outcomes in intermediate- and high-risk prostate cancer...

Adverse pathological features in prostate cancer (PCa) are characteristics found in biopsy tissue that indicate a more aggressive or advanced disease. This study aims to develop a biopsy-based model for assessing the risk of adverse PCa and to evaluate its performance against the European Randomized Study of Screening for Prostate Cancer (ERSPC) Risk Calculator (RC) 3/4 and PSA models.

Between January 2017 and December 2022, patients with prostate-specific antigen (PSA) levels of ≤ 50 ng/mL underwent prostate biopsies. The patients' age, PSA, digital rectal exam, prostate volume, PSA density (PSAD), previous negative biopsy, number of positive cores, Gleason score, and biopsy outcomes were documented. Patients were classified into categories: no cancer, very low-risk, low-risk, intermediate-risk, and high-risk groups. We investigated the relationship between our model and adverse PCa using a binary Generalized Linear Model (GLM). We evaluated the model's discriminatory ability using the area under the receiver operating characteristic curve (AUC) and compared its predictive performance with the adverse model in terms of discrimination, calibration, and clinical utility.

Out of 824 patients, PCa was diagnosed in 320 (38.8%) men, and 203 (24.6%) had unfavorable PCa. The GLM demonstrated improved performance metrics, with an AUC of 0.766, compared to 0.639 for the RC 3/4 model and 0.655 for the PSA model. The GLM showed a good fit and provided a greater net benefit.

The study identified clinical predictors of adverse PCa during biopsy, demonstrating moderate discrimination and clinical utility. Further large multicenter studies are required for validation.