External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Not Applicable / Observational study.
- Who it may be relevant to
- Registry conditions: Colonoscopy. Basic parameters: 18 years — 75 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults-A Prospective, Observational, Multicentre Study
Overview
Colorectal adenomas are precursors to colorectal cancer (CRC). Accurate pre-procedure risk stratification could optimize colonoscopy yield and resource allocation in India, where adenoma prevalence varies by age, sex, and lifestyle/metabolic factors. ML models can integrate multiple predictors to estimate individualized risk. Existing risk scores are largely Western; performance and calibration may not be appropriate in Indian populations with different socio-demographic and metabolic profiles. External, prospective, multicentre validation is essential before clinical implementation.
Interventions
- Procedure Not Applicable / Observational study
No study-specific intervention is administered. Participants undergo standard-of-care diagnostic colonoscopy and histopathological evaluation. A locked machine-learning model is applied to routinely collected baseline clinical and demographic data for risk prediction only, without influencing clinical management.
Primary outcome measures
- Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model [Time frame: 1 YEAR]
Secondary outcome measures (1)
- Validation Performance of the Machine Learning Prediction Model [Time frame: 1 YEAR]
Eligibility criteria
Inclusion criteria
- Adults ≥18 years undergoing diagnostic colonoscopy.
- Adequate bowel preparation (Boston Bowel Preparation Scale total ≥6 with each segment ≥2).
- Complete examination (cecal intubation; withdrawal time ≥6 min when no therapy).
- Availability of all model predictors per CRF.
Exclusion criteria
- • Known CRC or polyp, prior colectomy, polyposis syndromes, known IBD, or strong hereditary CRC syndromes (e.g., Lynch) if excluded in derivation.
- Inadequate prep, incomplete colonoscopy, obstructing lesions preventing optical diagnosis beyond obstruction.
- Emergency colonoscopies, therapeutic-only procedures without diagnostic intent.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07329816 · VALID-ADENOMA-IN