Early Diagnosis Model of Colorectal Adenoma Using Laboratory Examinations

Condition(s) studied

Investigational drug(s) / intervention(s)

No active intervention: This is an observational cohort study. No drugs, surgical procedures or therapeutic interventions will be provided to participants. Only routine clinical laboratory and demographic data are collected for constructing and validating a prediction model for colorectal adenoma.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Discriminative performance of the clinical prediction model for colorectal adenoma — At the time of colonoscopy enrollment
  Construct an automatic extraction model for laboratory test data and an early colorectal adenoma diagnosis model based on laboratory test results, and prospectively verify the adenoma detection rate among patients stratified by the model into high-risk and low-risk adenoma groups.

Trial sites (1)

More Renmin Hospital of Wuhan University trials in China

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