关键词: 前列腺癌, PI-RADS 3分, 影像组学

Abstract: Objective　To explore the application value of radiomics in the differential diagnosis of benign and malignant lesions classified as PI-RADS 3 in prostate imaging reports, providing an objective basis for clinical decision-making. Methods　A retrospective study included 96 patients who underwent prostate biopsy or surgery at Shantou Central Hospital from January 2018 to December 2024, all with preoperative magnetic resonance imaging (MRI) reports classified as PI-RADS 3 and confirmed by pathology, with an average age of (70.60±7.41) years. All patients underwent multiparametric MRI. Using 3D-Slicer software, regions of interest (ROIs) of the lesions were manually delineated on axial T2-weighted images (T2WI), and 1 127 radiomic features were extracted using open-source software FAE. In the training group (67 cases), recursive feature elimination (RFE) was employed to select key features, and a radiomic score (Radscore) model was constructed based on the coefficients of these features. The model's performance was evaluated in the validation group (29 cases) and compared with traditional indicators such as prostate-specific antigen (PSA) and apparent diffusion coefficient (ADC). Independent samples t tests, Mann-Whitney U tests, and χ² tests were used for intergroup comparisons, logistic regression analysis was conducted to build the model, and receiver operating characteristic (ROC) curves were plotted to assess diagnostic performance. Results　Six key features were selected from the 1 127 features to construct the Radscore model. In the training group, the area under the curve (AUC) of the Radscore model for distinguishing benign from malignant lesions was 0.974 [95% confidence interval (CI) 0.942-1.000], with a sensitivity of 92.9% and specificity of 100.0%. In the validation group, the AUC was 0.933 (95% CI 0.813-1.000), with a sensitivity of 95.8% and specificity of 80.0%. The diagnostic performance (AUC) of the Radscore model was significantly higher than that of the PSA and ADC value models in both the training and validation groups. Multivariable logistic regression analysis indicated that Radscore was an independent factor for predicting prostate cancer (OR=1.80, P=0.003). Conclusion　The T2WI-based radiomics model significantly improves diagnostic accuracy for PI-RADS 3 lesions by quantifying tumor heterogeneity, surpassing traditional indicators (PSA/ADC). This model provides an objective basis for clinical decision-making, helping to optimize biopsy strategies and reduce unnecessary invasive procedures.

Key words: Prostate cancer, PI-RADS 3 score, Radiomics