3D Modeling for Detecting Locally Advanced Rectal Cancer With Positive Circumferential Resection Margin
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-Assisted 3D Imaging Model for Tumor and CRM Assessmen.
- Who it may be relevant to
- Registry conditions: General Surgery, Oncology, Medical Informatics. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Taiwan
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Using 3D Modeling to Detect Locally Advanced Rectal Cancer With Positive Circumferential Resection Margin
Overview
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 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.
Primary outcome measures
- 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)]
Secondary outcome measures (1)
- Accuracy and agreement of AI model predictions with MRI interpretations [Time frame: Day 1 (At the time of retrospective imaging analysis)]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Taiwan · 1 center
- Taichung Veterans General Hospital — Taichung
Identifiers
NCT: NCT07183124 · CE25536A · TCVGH-NHRI1142008