A research team has created a new machine-learning framework that distinguishes between low- and high-risk prostate cancer, according to a paper published in Scientific Reports.
The framework aims to help physicians more accurately find treatment options for prostate cancer patients, reducing the chance of unneeded clinical intervention.
The researchers, from the Icahn School of Medicine at Mount Sinai in New York City and Keck School of Medicine at the University of Southern California in Los Angeles, combined machine learning with radiomics, which uses algorithms to extract large amounts of quantitative characteristics from medical images.
They evaluated multiple machine-learning methods to find the one that could most accurately distinguish between intermediate and malignant cancer levels.
Their framework also leverages bigger data sets than previous studies used, allowing them to classify patients' prostate cancer with high sensitivity and an even higher predictive value.
"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 senior corresponding author Gaurav Pandey, PhD. "The pathway to predicting prostate cancer progression with high accuracy is ever improving, and we believe our objective framework is a much-needed advancement."