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

Taichung Veterans General Hospital

Status: Not yet enrolling

Conditions: Medical Informatics; Oncology; General Surgery

Treatments: Diagnostic Test: AI-Assisted 3D Imaging Model for Tumor and CRM Assessmen

Study type: Observational

Funder types: Other

Identifiers

NCT07183124

TCVGH-NHRI1142008 (Other Grant/Funding Number)

CE25536A

Details and patient eligibility

About

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.

Enrollment: 1,500 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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.