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“The pathway to predicting prostate cancer progression with high accuracy is ever improving, and we believe our objective framework is a much-needed advancement.”Ĭurrently, the standard methods used to assess prostate cancer risk are multiparametric magnetic resonance imaging (mpMRI)-which detects prostate lesions-and the Prostate Imaging Reporting and Data System, version 2 (PI-RADS v2)-a five-point scoring system that classifies lesions found on mpMRI. “By rigorously and systematically combining machine learning with radiomics, our goal is to provide radiologists and clinical personnel with a sound prediction tool that can eventually translate to more effective and personalized patient care,” senior author Gaurav Pandey, PhD, assistant professor of genetics and genomic sciences at the Icahn School of Medicine, said in a prepared statement from Mount Sinai. The team of experts from the Icahn School of Medicine at Mount Sinai in New York City and the Keck School of Medicine at the University of Southern California in Los Angeles, hope the AI technology-which combines machine learning and radiomics-can help radiologists more accurately identify prostate cancer treatment for patients. 7 in the journal Scientific Reports.Īfter testing various types of machine-learning classifiers, the researchers found the best performing classifier could potentially outperform assessments produced using Prostate Imaging Reporting and Data System (PI-RADS) version 2.
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A multi-institutional team of researchers has developed a new AI learning algorithm that can distinguish between low- and high-risk prostate cancer from multiparametric (mpMRI) scans with higher sensitivity and predictive value than current risk assessment approaches, according to research published online Feb.