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New Machine Learning Tool Accurately Predicts Prostate Cancer

By Medimaging International staff writers
Posted on 01 Mar 2019
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Image: Researchers have developed a machine-learning framework that can distinguish between low- and high-risk prostate cancer with greater precision (Photo courtesy of iStock).
Image: Researchers have developed a machine-learning framework that can distinguish between low- and high-risk prostate cancer with greater precision (Photo courtesy of iStock).
Researchers from the Icahn School of Medicine at Mount Sinai (New York, NY, USA) and Keck School of Medicine at the University of Southern California (Los Angeles, CA, USA) have developed a machine-learning framework that can distinguish between low- and high-risk prostate cancer with greater precision than ever before. The framework is expected intended to help physicians, particularly radiologists, in identifying treatment options more accurately for prostate cancer patients, thereby reducing the need for unnecessary clinical intervention.

The standard methods currently being used to assess prostate cancer risk are multi-parametric 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 the mpMRI. These tools are intended to soundly predict the likelihood of clinically significant prostate cancer. However, PI-RADS v2 scoring is subjective and does not distinguish clearly between intermediate and malignant cancer levels (scores 3, 4, and 5), resulting in differing interpretations among clinicians most of the time.

In order to remedy this drawback, it has been proposed to combine machine learning with radiomics—a branch of medicine that uses algorithms to extract large amounts of quantitative characteristics from medical images. While other studies have only tested a limited number of machine learning methods to address this limitation, the Mount Sinai and USC researchers have developed a predictive framework that rigorously and systematically assessed many such methods to identify the best-performing one. The framework also leverages larger training and validation data sets than previous studies did, allowing the researchers to classify the 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 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 publication alongside co-corresponding author Bino Varghese, PhD, Assistant Professor of Research Radiology at the Keck School of Medicine at USC. “The pathway to predicting prostate cancer progression with high accuracy is ever improving, and we believe our objective framework is a much-needed advancement.”

Related Links:
Icahn School of Medicine at Mount Sinai
Keck School of Medicine at the University of Southern California


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