Rectal Cancer Stage III
Conditions
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.
Interventions
OTHERAs training material for deep learning model.
Using labeled images as training materials for artificial intelligence to develop object detecting model.
OTHERAs materials for external validation for the buildup model.
Using the external validation set to evaluate prediction rate and survival outcome.
Sponsors
Taichung Veterans General Hospital
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to 100 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| accuracy of artificial intelligence with experienced physician | 1 week after images done. | accuracy between artificial intelligence and experienced physician |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| real life survival outcome of diagnosis by artificial intelligence. | 5 years after diagnosed | real life survival outcome by artificial intelligence. |
Countries
Taiwan
Outcome results
None listed