Development of novel nomograms for predicting prostate cancer in biopsy-naive patients with PSA < 10 ng/ml and PI-RADS ≤ 3 lesions - Chai, Jia-gui; Li, Yu-hang; Ke, Chang-xing (Frontiers in Oncology, 2025)
PurposeTo develop novel nomograms for predicting prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in patients with prostate-specific antigen (PSA) < 10 ng/ml and PI-RADS v2.1 score ≤ 3.MethodsWe retrospectively collected data from 327 men with PSA < 10 ng/ml and PI-RADS score ≤ 3 from June 2020 to June 2024 in our hospital. Clinical data were compared among the PI-RADS scores 1-3 population, PI-RADS scores 1-2 population, and PI-RADS score 3 population. Logistic regression analyses were conducted to identify independent risk factors for PCa or csPCa, and nomograms were subsequently developed. The nomograms were evaluated via receiver operating curves (ROC), calibration curves, and decision curve analysis (DCA). Internal validation was conducted using bootstrap methods.ResultsAmong the 327 patients, 224 (68.50%) were diagnosed with benign, 65 (19.87%) with csPCa, and 38 (11.62%) with clinically insignificant prostate cancer (cisPCa). Prostate-specific antigen density (PSAD), lesion volume (LV), lesion location, and apparent diffusion coefficient (ADC) were found to be independent risk factors for PCa and csPCa in PI-RADS scores 1-3 population. PSAD and lesion location were independent risk factors for PCa in the PI-RADS scores 1-2 population, while PSAD, lesion location and ADC were independent risk factors for PCa in the PI-RADS score 3 population. Four nomograms were established based on these variables. For the population with PI-RADS scores 1-3, the area under the ROC (AUC) for predicting PCa and csPCa was 0.78 and 0.79, respectively. For patients with PI-RADS scores 1-2, the AUC for predicting PCa was 0.75. For patients with PI-RADS score 3, the AUC for predicting PCa was 0.78. The calibration curves revealed good concordance between the predicted probability and the actual probability. DCA demonstrated the net benefit of nomograms. Internal validation revealed strong discrimination of the nomograms.ConclusionWe developed novel nomograms with acceptable discriminability for predicting PCa and csPCa in patients with PSA < 10 ng/ml and PI-RADS score ≤ 3. These models can assist urologists in determining the necessity of prostate biopsy.