Clinical trial · Observational
Using Artificial Intelligence to Predict Rectal Cancer Outcomes
Using CNN Image Recognition to Predict Rectal Cancer Outcomes
- 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)
Investigator retrospective collect cases during 2010-2021 diagnosed as rectal adenocarcinoma with high quality CT images. Local advanced rectal cancer cases were labeled as "disease". Nor were defined " normal". Using artificial intelligence CNN on jupyter notebook with open phyton code to train and develop models capable to recognizing local advanced rectal cancer. Modify the phyton code for better predict rate and help physician to quickly evaluate disease severity for fresh rectal cancer cases.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Rectal Cancer Stage III | Malignant Rectal Neoplasm | CURATED_BROADER | 0.78 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| As materials for external validation for the buildup model. | Other | — | UNRESOLVED |
| As training material for deep learning model. | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- rectal cancer lesion images for training
- description
- Rectal cancer lesion images. Images with threatened (\<2mm) circumferential margin of rectal cancer were labeled as "diseased". Otherwise, images were labeled as "normal". Using these materials as training materials for AI deep learning model buildup.
- interventionNames
- Other: As training material for deep learning model.
- label
- rectal cancer lesion images for testing.
- description
- Using the buildup AI deep learning models from training cohort. Evaluating prediction rate of the model and analysis survival outcomes.
- interventionNames
- Other: As materials for external validation for the buildup model.
Primary outcomes (1)
- measure
- accuracy of artificial intelligence with experienced physician
- timeFrame
- 1 week after images done.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 20 Years
- Maximum age
- 100 Years
Show eligibility criteria text
Inclusion Criteria: * clinical staging T3-4 with high quality CT images. Exclusion Criteria: * 1\. not primary malignancy lesion * 2\. not localizing rectum * 3\. T1-2 lesion * 4\. non contrast or poor quality images
References
Publications (0)
Data not yet available