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Abstract #2606

Comparison of machine learning methods for detection of prostate cancer using bpMRI radiomics features

Ethan J Ulrich1, Jasser Dhaouadi1, Robben Schat2, Benjamin Spilseth2, and Randall Jones1
1Bot Image, Omaha, NE, United States, 2Radiology, University of Minnesota, Minneapolis, MN, United States


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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