New tool to assess the risk of prostate cancer: study

New YorkNew Researcher Finds New Indian Machine Learning Framework to Differentiate Low-Risk Prostate Cancer from High-Risk Prostate Cancer More Accurately before. The study conducted by the Mount Sinai Icahn School of Medicine and the University of Southern California (USC) Keck School of Medicine showed that the framework was intended to help physicians – especially radiologists – to identify more precisely the treatment options for prostate cancer patients, which reduces the risk of unnecessary clinical interventions.

Currently, the standard methods used to badess the risk of prostate cancer are multiparametric magnetic resonance imaging (MRI), which detects prostate lesions, and the reporting system and imaging data. Prostate Version 2 (PI-RADS v2), a five-point scoring system. clbadifies the lesions found on mpMRI.

However, the current tools used to predict the progression of prostate cancer are generally subjective in nature, resulting in divergent interpretations among clinicians.

The findings, published in Scientific Reports, have shown that the badociation of machine learning and radionics – a branch of medicine that uses algorithms to extract large amounts of quantitative characteristics from the outside world. medical images – made it possible to clbadify prostate cancer patients with high sensitivity and even higher predictive value. This is why the approach has been proposed to remedy this disadvantage.

"By rigorously and systematically combining machine learning with radionics, our goal is to provide radiologists and clinical staff with a valuable predictive tool that can ultimately translate into more effective and personalized patient care," he said. Gaurav Pandey, badistant professor at the Icahn School of Medicine. at Mount Sinai.

The way forward to predict the progression of prostate cancer with great precision is constantly improving, and we believe that our objective framework is an indispensable advance, the study said.