Colorectal cancer (CRC) ranks among the most common and lethal cancers worldwide, accounting for approximately 10% of all cancer diagnoses. While advances in prevention and treatment have improved outcomes, predicting which patients will survive remains a complex challenge—one that depends on an intricate interplay between molecular biology and clinical factors.
A research paper, titled “Machine learning-based survival prediction in colorectal cancer combining clinical and biological features” was published in Volume 16 of Oncotarget by an international team of researchers, demonstrating how machine learning can integrate these two domains to achieve highly accurate survival predictions.
The team’s investigation demonstrates that combining clinical features—such as pathological stage, age, and lymph node status—with biological markers—including the E2F8 gene and hsa-miR-495-3p—can significantly improve the ability to predict patient survival.
Full blog - https://www.oncotarget.org/2026/03/25/predicting-colorectal-cancer-survival-how-machine-learning-combines-clinical-and-biological-clues/
Paper DOI - https://doi.org/10.18632/oncotarget.28783
Correspondence to - Lucas M. Vieira - lvieira@health.ucsd.edu
Abstract video - https://www.youtube.com/watch?v=cy7UL5ZUKuI
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Keywords - cancer, colorectal cancer, machine learning, feature selection, non-coding RNAs, genes
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