Prediction of the Gleason Score of Prostate Cancer Patients Using 68Ga-PSMA-PET/CT Radiomic Models

To predict Gleason Score (GS) using radiomic features from 68Ga-PSMA-PET/CT images in primary prostate cancer. 138 patients undergoing 68Ga-PSMA-PET/CT imaging were categorized based on GS, with GS above 4 + 3 as malignant and under 3 + 4 as benign tumors. radiomic features were extracted from tumors’ volume of interest in both PET and CT images, using Feature Elimination with cross-validation. Fusion features were generated by combining features at the feature level; average of features (PET/CTAveFea) or concatenated features (PET/CTConFea). The performance of various models was compared using area under the curve, sensitivity and specificity. Wilcoxon test and F1-score test were used to find the best model. Predictive models were developed for CT-only, PET-only, and PET/CT feature-level fusion models. Random Forest achieved the highest accuracy on CT with 0.74 ± 0.01 AUCMean, 0.75 ± 0.07 sensitivity, and 0.62 ± 0.08 specificity. Logistic regression (LR) exhibited the best predictive performance on PET images with 0.74 ± 0.05 AUCMean, 0.7 ± 0.13 sensitivity, and 0.78 ± 0.14 specificity. The best predictive PET/CTAveFea was achieved by LR, resulting in 0.72 ± 0.07 AUCMean, 0.74 ± 0.12 sensitivity, and 0.63 ± 0.02 specificity. In the case of PET/CTConFea, LR showed the best predictive performance with 0.78 ± 0.08 AUCMean, 0.81 ± 0.09 sensitivity, and 0.66 ± 0.15 specificity. The results demonstrated that radiomic models derived from 68Ga-PSMA-PET/CT images could differentiate between benign and malignant tumors based on GS.