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

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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07183124

Enrollment

1500

Registered

2025-09-19

Start date

2025-10-01

Completion date

2026-07-31

Last updated

2025-09-19

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

Conditions

General Surgery, Medical Informatics, Oncology

Keywords

rectal cancer, 3D Modeling, deep learning, Pelvic CT Imaging, Clinical Prediction Model

Brief 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.

Interventions

DIAGNOSTIC_TESTAI-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.

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

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.

Design outcomes

Primary

Measure	Time frame	Description
Sensitivity and specificity of the AI-assisted 3D imaging model for predicting circumferential resection margin (CRM) negativity	Day 1 (At the time of retrospective imaging analysis)	Model predictions are compared with pathology results (gold standard) to assess diagnostic accuracy.

Secondary

Measure	Time frame	Description
Accuracy and agreement of AI model predictions with MRI interpretations	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.

Countries

Taiwan

Contacts

Primary ContactChun-Yu Lin, PhD

classicpiano2003@gmail.com886-4-23592525

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026