Colorectal AdenomaNCT07793110
Early Diagnosis Model of Colorectal Adenoma Using Laboratory Examinations
An observational study of Colorectal Adenoma and Polyp, run by Renmin Hospital of Wuhan University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Colorectal Adenoma, Polyp
- What is being tested?
- No active intervention
- How many people
- 400 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2026to 1 Sep 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Renmin Hospital of Wuhan University
Wuhan, Hubei, 430060
Honggang H Yu, PhD, 19986910547, yuhonggang1968@163.com
Xinran X zhang, Master, 18972619028, 18972619028@163.com
About this study
Official title: Construction and Validation of an Early Diagnosis Model for Colorectal Adenoma Based on 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.
Read the detailed description
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.
- Conditions
- Colorectal Adenoma, Polyp
- Being tested
- No active intervention
- Keywords
- laboratory tests, Colorectal adenoma, polyp, precancerous lesion
- Medical subject headings
- Polyps
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No active interventionOther
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 group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: No active intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: At the time of colonoscopy enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Aug 2026
- Study start1 Sep 2026 (estimated)
- Primary completion1 Sep 2027 (estimated)
- Study completion1 Sep 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Colorectal AdenomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yu Honggang, Renmin Hospital of Wuhan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07793110
- Sponsor's protocol ID
- WDRY2026-K168
- Status last verified
- Aug 2026
- First submitted
- 13 Aug 2026
- Last updated
- 28 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- 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.
Publications
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.
PubMed 39619238Zhao 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.
PubMed 30738046Siegel 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.
PubMed 35020204
Study at a glance
From the registry record, last updated by the sponsor on 28 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Construction and Validation of an Early Diagnosis Model for Colorectal Adenoma Based on Laboratory Examinations
- ClinicalTrials.gov ID
- NCT07793110
- Acronym / sponsor ID
- WDRY2026-K168
- Conditions
- Colorectal Adenoma, Polyp
- Keywords
- laboratory tests, Colorectal adenoma, polyp, precancerous lesion
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 1 Sep 2026 (estimated)
- Primary completion
- 1 Sep 2027 (estimated)
- Study completion
- 1 Sep 2028 (estimated)
- First posted
- 28 Aug 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| All enrolled participants | – | Other: No active interventionAll 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (8 listed)
- 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);
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No active interventionThis 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. | Other | All enrolled participants |
Primary outcome
Discriminative performance of the clinical prediction model for colorectal adenoma
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Aug 2026.
- Background: PubMed 39619238Rui 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.
- Background: PubMed 30738046Zhao 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.
- Background: PubMed 35020204Siegel 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.
- Individual participant dataSharing planned: 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colorectal Adenoma and Polyp, run by Renmin Hospital of Wuhan University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 28 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Discriminative performance of the clinical prediction model for colorectal adenoma (time frame: At the time of colonoscopy enrollment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07793110, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Aug 2026.