Keywords: Machine Learning/Artificial Intelligence, ProstateSynopsis: Recently, predictive machine learning models have shown promise for prostate cancer diagnosis. The utility of MRI radiomic features for prostate cancer detection and classification has been shown several studies, but mostly using relatively small and single centre cohort. In this study, we showed that radiomics-based machine learning can perform relatively well compared to clinical practice, especially in large multicentre settings. On the patient-level analysis, the areas under the receiver-operating curves for PI-RADS reading by a radiologist and machine learning model were 90% and 89%, respectively.
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