Researchers have developed a new framework that uses machine learning to predict prostate cancer progression, according to new findings published in Scientific Reports.

The team included representatives 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. Their goal was to give healthcare providers a more effective way to differentiate between low- and high-risk prostate cancer and improve patient care.

Sixty-eight prostate cancer patients who presented at a single institution from March 2013 to May 2016 were included in the retrospective study. Multiparametric MRI images (mpMRI) of the prostate and transrectal ultrasound-magnetic resonance imaging fusion-guided biopsy of the prostate within two months of mpMRI were available for all patients.

The researchers’ use of both machine learning and radiomics achieved a high sensitivity and a high predictive value. They noted their large training and validation data sets resulted in more accurate predictions than prior studies.

“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,” Gaurav Pandey, PhD, Assistant professor of genetics and genomic sciences at the Icahn School of Medicine at Mount Sinai and senior corresponding author of the study, said in a prepared statement. “The pathway to predicting prostate cancer progression with high accuracy is ever improving, and we believe our objective framework is a much-needed advancement.”