Machine Learning Enhances Diagnosis and Monitoring of Colorectal Cancer
A novel machine learning platform developed by researchers enhances the diagnosis and monitoring of colorectal cancer through metabolic biomarker analysis, offering a promising noninvasive approach to early detection and disease progression assessment.
Researchers have developed an innovative machine learning platform aimed at improving the diagnosis and monitoring of colorectal cancer. This advanced tool analyzes metabolic differences in biological samples from over 1,000 individuals, distinguishing between patients with colorectal cancer and healthy controls. The study identified specific metabolic shifts that correlate with disease severity and genetic risk factors, offering promising avenues for noninvasive diagnostics.
The platform, named PANDA (PLS-ANN-DA), integrates partial least squares-discriminant analysis (PLS-DA) and artificial neural networks (ANN) to enhance predictive accuracy. Using blood samples from large-scale research initiatives, the team examined metabolites—products of biochemical reactions—and transcriptomic data, providing a comprehensive view of molecular alterations associated with cancer.
Although not intended to replace colonoscopy, the current gold standard for screening, this biomarker discovery pipeline could significantly aid early detection and disease progression monitoring. Its potential also extends to assessing treatment responses more rapidly than traditional methods, such as pathology or protein markers.
The researchers emphasize that further validation with additional samples is necessary before clinical application. This approach also offers insights into cancer biology, especially pathways involving purines, which are crucial for DNA synthesis and repair. The study demonstrates a robust advancement in machine learning and bioinformatics, opening pathways for future diagnostic technologies in colorectal cancer management.
This groundbreaking work was published in iMetaOmics, with contributions from researchers at Ohio State University and the City of Hope National Cancer Center. Additional insights from this study could revolutionize how clinicians diagnose, monitor, and understand colorectal cancer in the future.
Source: https://medicalxpress.com/news/2025-05-machine-tool-colorectal-cancer.html
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