Using Artificial Intelligence to Predict Rectal Cancer Outcomes

Completed

• Conditions: Rectal Cancer Stage III
• Interventions: Other: As materials for external validation for the buildup model.; Other: As training material for deep learning model.

• Registration Number: NCT05723965
• Lead Sponsor: Taichung Veterans General Hospital

Brief Summary

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.

Detailed Description

From 2010.10.1\~2021.12.31, rectal cancer patients with cT3-4 lesion was included. Collect high quality CT images with DICOM files in tumor segment. cT1-2, low rectal lesions, non-CRC cases were not included. Non-contrast and artificial defect images were also excluded. CT images were labeled as" diseased " when CRM were threatened (\<2mm). All images were labeled according to judgment of 2 specialist. The data were separated into 2 parts. One for AI model training and testing, another for external validation. The training testing dataset was achieved by deep learning neural network and evaluating model accuracy performance. Then the model was applied into external validation dataset for real-world testing, evaluating coherent rate between AI and the Dr. decision. Furthermore, to see the cancer survival outcomes according to AI model prediction results.

Study Timeline

• Study Start: 2010-10-01
• Primary Completion: 2022-07-31
• Study Completion: 2022-12-31
• Study First Submitted: 2023-01-05
• Status Verified: 2023-02
• Study First Submitted QC: 2023-02-09
• Last Update Submitted: 2023-02-09
• Study First Posted: 2023-02-13
• Last Update Posted: 2023-02-13

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 720

Inclusion Criteria

• clinical staging T3-4 with high quality CT images.

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Exclusion Criteria

• 1. not primary malignancy lesion
• 2. not localizing rectum
• 3. T1-2 lesion
• 4. non contrast or poor quality images

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
rectal cancer lesion images for testing.	As materials for external validation for the buildup model.	Using the buildup AI deep learning models from training cohort. Evaluating prediction rate of the model and analysis survival outcomes.
rectal cancer lesion images for training	As training material for deep learning model.	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.

Primary Outcome Measures

Name	Time	Method
accuracy of artificial intelligence with experienced physician	1 week after images done.	accuracy between artificial intelligence and experienced physician

Secondary Outcome Measures

Name	Time	Method
real life survival outcome of diagnosis by artificial intelligence.	5 years after diagnosed	real life survival outcome by artificial intelligence.

Trial Locations

Locations (1)

Taichung Verterans General Hospital; 🇨🇳 Taichung, Taiwan