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NCT Number: NCT07183124

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

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.

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Key information

Conditions

General Surgery Colorectal Neoplasms Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Intestinal Diseases Intestinal Neoplasms Medical Informatics Neoplasms Neoplasms by Site Oncology Rectal Diseases Rectal Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Taichung Veterans General Hospital

Taichung, Taiwan

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Diagnosed with rectal cancer, clinical stage II-III, with no distant metastasis (M0)
  • Age over 18 years, with adequate physical status classified as American Society of Anesthesiologists (ASA) I-III, capable of receiving treatment and surgery
  • No history of other malignancies or major diseases affecting study assessment within the past three years.
  • Complete medical records, including available CT and MRI imaging.

Exclusion criteria

  • Patients with clinical stage I or IV rectal cancer.
  • Age under 18 years, or physical status not meeting American Society of Anesthesiologists (ASA) I-III criteria, unable to undergo surgery or related treatment.
  • Presence of other major diseases or malignancies affecting tumor assessment (e.g., diagnosis of another malignancy within the past three years, uncontrolled cardiovascular disease).
  • Incomplete medical records or imaging data, including missing required CT or MRI images.

Treatment and study plan

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

Diagnostic Test

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.

Primary outcomes

  1. Sensitivity and specificity of the AI-assisted 3D imaging model for predicting circumferential resection margin (CRM) negativity

    Time frame: Day 1 (At the time of retrospective imaging analysis)

    Model predictions are compared with pathology results (gold standard) to assess diagnostic accuracy.

Secondary outcomes

  1. Accuracy and agreement of AI model predictions with MRI interpretations

    Time frame: Day 1 (At the time of retrospective imaging analysis)

    Agreement between AI model, MRI, and pathology results will be analyzed using Kappa statistics to evaluate consistency and reliability.

Study contacts

Contact information is provided by the study sponsor or research team.

Chun-Yu Lin, PhD

CONTACT

[email protected]

886-4-23592525 ext. 5161

Sponsors and collaborators

Lead sponsor

Taichung Veterans General Hospital

Other

Collaborators

  • National Health Research Institutes, Taiwan

Registry information

Official study title

Using 3D Modeling to Detect Locally Advanced Rectal Cancer With Positive Circumferential Resection Margin

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Sep 19, 2025
Registry last updated
Sep 19, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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