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3D Modeling for Detecting Locally Advanced Rectal Cancer With Positive Circumferential Resection Margin

NCT07183124 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2025-09-19

No results posted yet for this study

Summary

This retrospective study aims to develop an AI-assisted 3D modeling system to improve staging accuracy for stage II-III locally advanced rectal cancer (LARC). High-quality CT images from Taichung Veterans General Hospital will be used to reconstruct tumor boundaries and spatial relationships. The AI model will be trained and validated against MRI and pathology results to predict circumferential resection margin (CRM) status. Outcomes include sensitivity, specificity, accuracy, and agreement with standard imaging. This system seeks to support precise tumor staging and inform future clinical decision-making.

Conditions

  • General Surgery
  • Oncology
  • Medical Informatics

Interventions

DIAGNOSTIC_TEST

AI-Assisted 3D Imaging Model for Tumor and CRM Assessmen

This study uses an AI-assisted 3D imaging model to analyze existing CT and MRI images of stage II-III locally advanced rectal cancer patients. The system reconstructs tumor boundaries and spatial relationships, predicts circumferential resection margin (CRM) status, and supports staging assessment. No interventions are performed on participants, and all data are collected retrospectively from routine clinical care.

Sponsors & Collaborators

  • National Health Research Institutes, Taiwan

    collaborator OTHER

  • Taichung Veterans General Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-10-01
Primary Completion
2026-06-30
Completion
2026-07-31

Countries

  • Taiwan

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07183124 on ClinicalTrials.gov