Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features.

backgroundTo develop and validate an interpretable machine learning model based on intratumoral and peritumoral radiomics combined with clinicoradiological features and metabolic information from magnetic resonance spectroscopy (MRS), to predict clinically significant prostate cancer (csPCa, Gleason score ≥ 3 + 4) and avoid unnecessary biopsies.

methodsThis study retrospectively analyzed 350 patients with suspicious prostate lesions from our institution who underwent 3.0 Tesla multiparametric magnetic resonance imaging (mpMRI) prior to biopsy (training set, n = 191, testing set, n = 83, and a temporal validation set, n = 76). Intratumoral and peritumoral volumes of interest (VOI

resultsA total of 350 patients were included, comprising 173 patients with csPCa (49.4%) and 177 patients with non-csPCa (50.6%). The intra-rad-score and peri-rad-score were constructed via 10 and 16 radiomic features. The combined model demonstrated the highest AUC, accuracy, F1 score, sensitivity, and specificity in the testing set (0.968, 0.928, 0.927, 0.932, and 0.923, respectively) and in the temporal validation set (0.940, 0.895, 0.890, 0.923, and 0.875, respectively). SHAP analysis revealed that the intra-rad-score, PSAD, peri-rad-score, and PI-RADS score were the most important predictors of the combined model.

conclusionWe developed and validated a robust machine learning model incorporating intratumoral and peritumoral radiomic features, along with clinicoradiological and metabolic parameters, to accurately identify csPCa. The prediction process was visualized via SHAP analysis to facilitate clinical decision- making.