Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| General Surgery | — | UNRESOLVED | — |
| Medical Informatics | — | UNRESOLVED | — |
| Oncology | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-Assisted 3D Imaging Model for Tumor and CRM Assessmen | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Sensitivity and specificity of the AI-assisted 3D imaging model for predicting circumferential resection margin (CRM) negativity
- timeFrame
- Day 1 (At the time of retrospective imaging analysis)
- description
- Model predictions are compared with pathology results (gold standard) to assess diagnostic accuracy.
Secondary outcomes (1)
- measure
- Accuracy and agreement of AI model predictions with MRI interpretations
- timeFrame
- Day 1 (At the time of retrospective imaging analysis)
- description
- Agreement between AI model, MRI, and pathology results will be analyzed using Kappa statistics to evaluate consistency and reliability.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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.
References
Publications (0)
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