Using Artificial Intelligence to Predict Rectal Cancer Outcomes
Using CNN Image Recognition to Predict Rectal Cancer Outcomes
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.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
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Taichung, Taiwan
- Taichung Verterans General Hospital
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
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
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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rectal cancer lesion images for training
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.
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Using labeled images as training materials for artificial intelligence to develop object detecting model.
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rectal cancer lesion images for testing.
Using the buildup AI deep learning models from training cohort.
Evaluating prediction rate of the model and analysis survival outcomes.
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Using the external validation set to evaluate prediction rate and survival outcome.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
accuracy of artificial intelligence with experienced physician
Time Frame: 1 week after images done.
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accuracy between artificial intelligence and experienced physician
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1 week after images done.
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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real life survival outcome of diagnosis by artificial intelligence.
Time Frame: 5 years after diagnosed
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real life survival outcome by artificial intelligence.
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5 years after diagnosed
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: ChunuYu Lin, M.D., Taichung Veterans General Hospital
Study record dates
Study Major Dates
Study Start (ACTUAL)
Study Start
Primary Completion (ACTUAL)
Primary Completion
Study Completion (ACTUAL)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (ACTUAL)
First Posted
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- CE21235B
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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