"Researchers Develop Prostate Cancer Prediction Tool That Has Unmatched Accuracy"
A team of researchers from the Icahn School of Medicine at Mount Sinai and Keck School of Medicine at the University of Southern California (USC) have developed a novel machine-learning framework that distinguishes between low- and high-risk prostate cancer with more precision than ever before. The framework, described in a Scientific Reports paper, is intended to help physicians—in particular, radiologists—more accurately identify treatment options for prostate cancer patients, lessening the chance of unnecessary clinical intervention. Prostate cancer is one of the leading causes of cancer death in American men, second only to lung cancer. While recent advances in prostate cancer research have saved many lives, objective prediction tools have, until now, remained an unmet need. “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,” said Gaurav Pandey, PhD, assistant professor of genetics and genomic sciences at the Icahn School of Medicine at Mount Sinai “The pathway to predicting prostate cancer progression with high accuracy is ever improving, and we believe our objective framework is a much-needed advancement.”
— Gaurav Pandey, PhD, Assistant Professor, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai