Early Diagnosis Model of Colorectal Adenoma Using Laboratory Examinations

NCT07793110 | Not Yet Recruiting

• 1 other identifier: WDRY2026-K168
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Colorectal Adenoma; Polyp

Keywords

laboratory tests; Colorectal adenoma; polyp; precancerous lesion

Outcome Measures

Primary Outcomes (1)

• Discriminative performance of the clinical prediction model for colorectal adenoma

  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.

  At the time of colonoscopy enrollment

Study Arms (1)

All enrolled participants

All subjects meeting inclusion criteria will be included in this single cohort. Participants will be divided into a retrospective training subset to construct the predictive model and a prospective validation subset to externally verify model performance for colorectal adenoma prediction.

Other: No active intervention

Interventions

No active intervention OTHER

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.

All enrolled participants

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This study enrolls adult patients aged ≥18 years who undergo colonoscopy. Subjects have pathologically confirmed diagnoses of colorectal adenoma or non-adenomatous colorectal lesions. Participants are capable of understanding and signing informed consent and can fully cooperate to complete all study-related procedures. Patients with unavailable laboratory data, other malignant lesions, autoimmune diseases, repeated enrollment records, pregnancy or lactation status will be excluded.

You may qualify if:

• Patients aged ≥ 18 years undergoing colonoscopy, with pathologically confirmed diagnosis of colorectal adenoma or non-adenomatous lesions (e.g., inflammatory polyps, hyperplastic polyps, chronic inflammation, etc.);
• Capable of reading, understanding and signing the informed consent form;
• The investigator judges that the subject can understand the procedures of this clinical study, and is willing and able to cooperate with and complete all study procedures.

You may not qualify if:

• Unavailable clinical data including laboratory test results;
• Neoplastic lesions other than colorectal adenoma;
• Non-first diagnosis of colorectal adenoma;
• Autoimmune diseases;
• Repeated enrolled subjects (duplicate patients);
• Pregnancy or breastfeeding status;
• Failure to obtain informed consent;
• The investigator considers that the subject has high-risk diseases or other special conditions unsuitable for participating in this clinical trial.

Sponsors & Collaborators

• Renmin Hospital of Wuhan University: lead

Study Sites (1)

Renmin Hospital of Wuhan University

Wuhan, Hubei, 430060, China

Location

Related Publications (3)

• Rui F, Yeo YH, Tian X, Chen Y, Li J. Authors' reply to: Concerns Regarding "Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort study". EClinicalMedicine. 2024 Nov 14;78:102908. doi: 10.1016/j.eclinm.2024.102908. eCollection 2024 Dec. No abstract available.

  PMID: 39619238 BACKGROUND

• Zhao S, Wang S, Pan P, Xia T, Chang X, Yang X, Guo L, Meng Q, Yang F, Qian W, Xu Z, Wang Y, Wang Z, Gu L, Wang R, Jia F, Yao J, Li Z, Bai Y. Magnitude, Risk Factors, and Factors Associated With Adenoma Miss Rate of Tandem Colonoscopy: A Systematic Review and Meta-analysis. Gastroenterology. 2019 May;156(6):1661-1674.e11. doi: 10.1053/j.gastro.2019.01.260. Epub 2019 Feb 6.

  PMID: 30738046 BACKGROUND

• Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022 Jan;72(1):7-33. doi: 10.3322/caac.21708. Epub 2022 Jan 12.

  PMID: 35020204 BACKGROUND

MeSH Terms

Conditions

Polyps

Condition Hierarchy (Ancestors)

Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Physician

Study Record Dates

• First Submitted: August 13, 2026
• First Posted: August 28, 2026
• Study Start (Estimated): September 1, 2026
• Primary Completion (Estimated): September 1, 2027
• Study Completion (Estimated): September 1, 2028
• Last Updated: August 28, 2026

Record last verified: 2026-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)