Abstract / Summary
This study aimed to develop and validate a novel nomogram for predicting prostate cancer in patients with gray-zone prostate-specific antigen (PSA), thereby reducing unnecessary prostate biopsies. Clinical data were retrospectively collected from patients who underwent prostate biopsy between January 2020 and April 2025 at our center. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for prostate cancer in the gray-zone PSA range. A nomogram was constructed based on these factors, and its discriminatory ability, calibration, and clinical utility were assessed for internal validation using the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA). A total of 476 patients were included, among whom 179 were diagnosed with prostate cancer. Older age, higher PSA density (PSAD), and a higher PI-RADS v2.1 score were identified as independent risk factors. The nomogram constructed from these three factors showed good predictive performance, with the area under the ROC curve indicating excellent discrimination. Calibration curves demonstrated acceptable agreement between predicted and actual probabilities in internal validation, and DCA confirmed its clinical net benefit. The nomogram combining age, PSAD, and PI-RADS v2.1 score provides a non-invasive and accurate tool for predicting prostate cancer, potentially reducing unnecessary biopsies in patients with gray-zone PSA.