Early Diagnosis Model of Colorectal Adenoma Using Laboratory Examinations

Colorectal cancer has the third highest incidence and second highest mortality rate of all malignant tumors worldwide. Distinct from most other cancers, colorectal cancer can be prevented; colonoscopy-based identification and removal of adenomatous polyps is the most effective preventive measure. Early intestinal adenomas rarely cause specific symptoms, and many patients are diagnosed at advanced stages once symptoms emerge, leading to unsatisfactory treatment and prognosis. Colonoscopy, the main diagnostic tool for intestinal adenoma, is invasive, resulting in limited patient compliance, while grassroots hospitals face shortages of medical resources. There is an urgent demand for a convenient, affordable and well-tolerated early diagnostic method for intestinal adenoma. Artificial intelligence techniques can efficiently analyze routine clinical laboratory data. This study aims to establish an AI-based predictive model combining clinical information and laboratory test results to realize early identification of intestinal adenoma and optimize patient prognosis.