Using Artificial Intelligence to Predict Rectal Cancer Outcomes

Study Overview
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Brief Summary
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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
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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.

Official Title
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Using CNN Image Recognition to Predict Rectal Cancer Outcomes

Conditions
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Rectal Cancer Stage III

Intervention / Treatment
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* Other: As training material for deep learning model.
* Other: As materials for external validation for the buildup model.


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 100 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| rectal cancer lesion images for training<br>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. | Other: As training material for deep learning model.<br>* Using labeled images as training materials for artificial intelligence to develop object detecting model.<br>|
| rectal cancer lesion images for testing.<br>Using the buildup AI deep learning models from training cohort. Evaluating prediction rate of the model and analysis survival outcomes. | Other: As materials for external validation for the buildup model.<br>* Using the external validation set to evaluate prediction rate and survival outcome.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| accuracy of artificial intelligence with experienced physician | accuracy between artificial intelligence and experienced physician | 1 week after images done. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| real life survival outcome of diagnosis by artificial intelligence. | real life survival outcome by artificial intelligence. | 5 years after diagnosed |