Development and External Validation of a Machine Learning Model for Pre-Endoscopic Prediction of Montreal Disease Extent (E1/E2/E3) in Ulcerative Colitis Using Symptoms, Signs, and Laboratory Tests: A Multicenter Retrospective Observational Study

Development and External Validation of a Machine Learning Model for Pre-Endoscopic Prediction of Montreal Disease Extent (E1/E2/E3) in Ulcerative Colitis

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07721987

Enrollment

1500

Registered

2026-07-23

Start date

2015-11-20

Completion date

2026-09-01

Last updated

2026-08-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Ulcerative Colitis (Disorder)

Brief summary

This multicenter retrospective observational study aims to develop and externally validate a machine learning model that predicts Montreal ulcerative colitis (UC) disease extent (E1: limited/proctitis; E2: left-sided; E3: extensive) using pre-endoscopic clinical information, including symptoms, signs, and laboratory tests. The model is intended to assist clinical assessment before endoscopic confirmation and is not designed to replace colonoscopy or histopathology. Data from development centers (Centers A and B) will be used for model development with nested cross-validation; data from independent external centers (Centers C and D) will be used for external validation only.

Interventions

None listed

Study design

Observational model

OTHER

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 85 Years

Healthy volunteers

Inclusion criteria

1. Confirmed diagnosis of ulcerative colitis by endoscopy ± histopathology. 2. Montreal disease extent classifiable as E1 (limited), E2 (intermediate/left-sided), or E3 (extensive) and mapped to study labels 1/2/3. 3. Pre-endoscopic baseline data available: demographics, symptoms, signs, and laboratory tests used as model predictors. 4. Predictors collected before or independent of endoscopic findings used for the outcome label (endoscopic extent not used as input). 5. One index visit per patient (duplicate/non-index visits excluded).

Exclusion criteria

1. Non-UC diagnosis or Montreal extent not assignable. 2. Missing patient identifier/linkage or unlabelable outcome. 3. Incomplete endoscopic gold standard for Montreal extent classification. 4. Duplicate or non-index visits. 5. Critical predictor data unavailable and not handled by the prespecified modeling pipeline

Design outcomes

Primary

Measure	Time frame
Macro one-vs-rest area under the receiver operating characteristic curve (macro AUC-OVR) for three-class Montreal extent prediction (E1 vs E2 vs E3) in the external validation cohort (n=247).	At index visit / endoscopic assessment

Outcome results