Diagnosing prostate cancer (PCa) has continually faced hurdles that have been difficult to overcome. The current diagnostic standard, the prostate-specific antigen or PSA test, has many limitations—as Richard J. Ablin, PhD, discoverer of PSA in 1970, has vociferously argued on countless occasions. Yet, with such limited diagnostic options and the speed at which PCa can metastasize and become deadly, the devil you know is better than the devil you don’t.

Still, science is always looking for better and faster disease diagnostic solutions and 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 just published data on a novel machine-learning framework they developed that distinguishes between low- and high-risk prostate cancer with more precision than ever before. Findings from the new study—published today in Scientific Reports through an article titled “Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images”—provide a framework intended to help physicians—in particular, radiologists—more accurately identify treatment options for PCa patients, lessening the chance of unnecessary clinical intervention.

Presently, the standard methods used to assess PCa 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 the mpMRI. Together, 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), often leading to differing interpretations among clinicians.

“This paper presents a systematic and rigorous framework comprised of classification, cross-validation, and statistical analyses that were developed to identify the best performing classifier for PCa risk stratification based on mpMRI-derived radiomic features derived from a sizeable cohort,” the authors write. “This classifier performed well in an independent validation set, including performing better than PI-RADS v2 in some aspects, indicating the value of objectively interpreting mpMRI images using radiomics and classification methods for PCa risk assessment.”

Combining machine learning with radiomics—a branch of medicine that uses algorithms to extract large amounts of quantitative characteristics from medical images—has been proposed as an approach to remedy this drawback. However, other studies have only tested a limited number of machine learning methods to address this limitation. In contrast, the Mount Sinai and USC researchers 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. As a result, researchers were able to classify patients’ PCa 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,” concludes senior study investigator 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.”

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