Multiple prostate cancer detection AI models—including random forest, neural network, XGBoost, and a novel boosted parallel random forest (bpRF)—are trained and tested using radiomics features from 958 bi-parametric MRI (bpMRI) studies from 5 different MRI platforms. After data preprocessing—consisting of prostate segmentation, registration, and intensity normalization—radiomic features are extracted from the images at the pixel level. The AI models are evaluated using 5-fold cross-validation for their ability to detect and classify cancerous prostate lesions. The free-response ROC (FROC) analysis demonstrates the superior performance of the bpRF model at detecting prostate cancer and reducing false positives.
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