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MACHINE LEARNING, TUMOR DNA TESTING A WINNING COMBO IN COLON CANCER
The study involved the application of a machine-learning algorithm to the development of diagnostic and prognostic models, based on a cohort of 801 patients with colorectal cancer and 1,021 healthy controls. A diagnostic prediction model assessing a panel of DNA methylation markers accurately distinguished patients from healthy individuals with a sensitivity and specificity of 87.5% and 89.9%, respectively, and outperformed the clinically available blood test for carcinoembryonic antigen (CEA). Modeling also predicted the prognosis and survival of colorectal cancer patients, a statistically significant result.The researchers noted that a single ctDNA methylation marker cg10673833 demonstrated sensitivity of 89.7%, specificity of 86.8%, and an area under the curve of 0.90 for detecting colon cancer and precancerous lesions in a prospective study of 1,493 samples from high-risk people that compared blood testing with colonoscopy.The findings show the value of ctDNA methylation markers for diagnosis in asymptomatic individuals, as well as for surveillance and predicting outcomes following a diagnosis of colon cancer, the researchers concluded. READ MORE