An observational study in Colorectal Adenoma and Polyp, sponsored by Renmin Hospital of Wuhan University. Not yet recruiting at 1 site in China. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-08-28.
Sponsored by Renmin Hospital of Wuhan University · Observational
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.
Colorectal cancer ranks the third in incidence and the second in mortality among all malignant tumors, constituting a major global public health concern. Unlike many other malignancies, colorectal cancer is preventable. Early detection and resection of adenomatous polyps via colonoscopy represent the most effective strategy for colorectal cancer prevention. Intestinal adenomas often present without specific clinical symptoms in the early stage. By the time patients seek medical care due to symptomatic manifestations, most have progressed to the middle or advanced stage, which exerts severe adverse impacts on subsequent therapeutic outcomes and long-term survival prognosis.
Although colonoscopy serves as the primary modality for the early diagnosis of intestinal adenoma, it is an invasive procedure associated with poor adherence among some patients. In addition, primary medical institutions are constrained by limited medical resources. Accurate and timely early diagnosis of intestinal adenoma is closely linked to targeted clinical intervention and improved patient survival outcomes. Therefore, it is critical to identify an early diagnostic approach for intestinal adenoma that boasts high patient acceptance, low technical barriers, convenience and cost-effectiveness. In recent years, with the advancement and wider accessibility of data analytics tools such as artificial intelligence (AI), growing research efforts have focused on addressing this clinical challenge using AI algorithms. Laboratory testing is routinely performed in clinical practice and delivers timely results, and the massive volume of laboratory data provides evidence supporting early disease diagnosis and prognostic prediction. Advances in artificial intelligence enable clinicians to convert abundant clinical data into practical predictive models to enhance diagnostic performance. Accordingly, integrated analysis of electronic medical records and laboratory results using artificial intelligence facilitates timely detection and early diagnosis of intestinal adenoma, and ultimately improves patient survival prognosis.
312 studies on the registry are indexed under Polyps; 60 are open to participants now.
This study's planned enrollment of 400 is above the median of 209 across 101 observational studies indexed under Polyps.
Browse Polyps studies →Renmin Hospital of Wuhan University is the lead sponsor of 86 studies on the registry; 29 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
The diagnosis was confirmed by looking at a sample under a microscope: tissue from a biopsy or surgery (histology), or cells from a fluid or needle sample (cytology).
Your pathology report is the document that shows this.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
A condition in which the immune system attacks the body’s own tissues, such as rheumatoid arthritis, lupus, psoriasis, type 1 diabetes or inflammatory bowel disease.
Immunotherapy studies often leave out active autoimmune disease, and some name exceptions, such as thyroid disease on replacement hormone. Check the exceptions this study lists.
Histologically or cytologically confirmed
The diagnosis was confirmed by looking at a sample under a microscope: tissue from a biopsy or surgery (histology), or cells from a fluid or needle sample (cytology).
Your pathology report is the document that shows this.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Autoimmune disease
A condition in which the immune system attacks the body’s own tissues, such as rheumatoid arthritis, lupus, psoriasis, type 1 diabetes or inflammatory bowel disease.
Immunotherapy studies often leave out active autoimmune disease, and some name exceptions, such as thyroid disease on replacement hormone. Check the exceptions this study lists.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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
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.
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.
Time frame: At the time of colonoscopy enrollment
Plan to share: No — Individual participant data will not be shared publicly to protect patient privacy and comply with institutional data management regulations. No predefined data sharing plan has been established for this study.
Start is estimated for Sep 2026; primary completion for Sep 2027, 12 months later.
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This study is not yet recruiting, as verified in Aug 2026. You cannot join it, but the record below documents what was studied.
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Renmin Hospital of Wuhan University