A new study published in Nature applies machine learning and radiomics to multiparametric magnetic resonance imaging (mpMRI) to stratify risks of prostate cancer (PCa), the third most common cause of death and the most prevalent male malignancy worldwide. The ability to accurately and objectively assess aggressiveness of PCa could help identify appropriate treatment, improving outcomes and reducing mortality.
The study used radiomics to convert collections of clinical images into structured quantitative data that helps model tumor behavior. The radiomics classifier performed significantly better than randomized versions, indicating a real relationship between radiomics features and PCa risk. Read the full article here.
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