External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults

NCT07329816 | Not Yet Recruiting DMC

• 1 other identifier: VALID-ADENOMA-IN
• Study Type: observational
• Target: 1,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Colonoscopy

Outcome Measures

Primary Outcomes (1)

• Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model

  Area under the receiver operating characteristic curve (AUROC) of the machine learning-based prediction model for identifying the presence of histologically proven colonic adenoma

  1 YEAR

Secondary Outcomes (1)

• Validation Performance of the Machine Learning Prediction Model

  1 YEAR

Study Arms (1)

Single prospective observational cohort

Participants undergo standard-of-care colonoscopy No allocation into treatment or comparison arms

Procedure: Not Applicable / Observational study

Interventions

Not Applicable / Observational study PROCEDURE

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.

Single prospective observational cohort

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

1000

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Asian Institute of Gastroenterology, India: lead

MeSH Terms

Interventions

Observation

Intervention Hierarchy (Ancestors)

Methods; Investigative Techniques

Central Study Contacts

DR. NITIN JAGTAP, MD,DM

CONTACT

8712015028; docsnitin13@gmail.com

DR NITIN JAGTAP, MD,DM

CONTACT

8712015028; docsnitin13@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Consultant Gastroenterology

Study Record Dates

• First Submitted: December 26, 2025
• First Posted: January 9, 2026
• Study Start: February 1, 2026
• Primary Completion (Estimated): March 30, 2027
• Study Completion (Estimated): March 30, 2027
• Last Updated: January 12, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share