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Interpretable Machine Learning Prediction Model for Submucosal Invasive Cancer in Large Sessile Colorectal Polyps: Development and Validation

Interpretable Machine Learning Prediction Model for Submucosal Invasive Cancer in Large Sessile Colorectal Polyps: Development and Validation

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500109235
Enrollment
Unknown
Registered
2025-09-16
Start date
2025-09-16
Completion date
Unknown
Last updated
2025-09-22

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

Conditions

Large sessile colorectal polyps

Interventions

Large Unstalked Colorectal Polyp Observation Group:None

Sponsors

Shanghai Tenth People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1.Adults aged 18 to 80 years; 2.Patients with endoscopic reports confirming the presence of large sessile colorectal polyps (diameter >=20 mm); 3.Baseline colonoscopy with complete examination report and pathological results.

Exclusion criteria

Exclusion criteria: 1.Polyps with a clearly defined stalk; 2.History of colorectal cancer or other malignancies; 3.History of inflammatory bowel disease;

Design outcomes

Primary

MeasureTime frame
AUROC (Area Under the Receiver Operating Characteristic Curve );Calibration curve;AUPRC (Area Under the Precision-Recall Curve);OR(Odds Ratio);Decision curve;—

Countries

China

Contacts

Public ContactLiu Feng

Shanghai Tenth People's Hospital

drliufeng@hotmail.com+86 21 6630 6725

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026