NCT05723965

Using Artificial Intelligence to Predict Rectal Cancer Outcomes

Using CNN Image Recognition to Predict Rectal Cancer Outcomes

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 720 (actual)
• Sponsor: Taichung Veterans General Hospital · Academic / Other
• Sex: All
• Age: 20 Years – 100 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Rectal Cancer Stage III

Interventions

Type	Name	Description
OTHER	As training material for deep learning model.	Using labeled images as training materials for artificial intelligence to develop object detecting model.
OTHER	As materials for external validation for the buildup model.	Using the external validation set to evaluate prediction rate and survival outcome.

Timeline

• Start date: 2010-10-01
• Primary completion: 2022-07-31
• Completion: 2022-12-31
• First posted: 2023-02-13
• Last updated: 2023-02-13

Locations

1 site across 1 country: Taiwan