Ignite Creation Date:
2024-05-06 @ 6:37 PM
Last Modification Date:
2024-10-26 @ 2:51 PM
Study NCT ID:
NCT05723965
Status:
COMPLETED
Last Update Posted:
2023-02-13
First Post:
2023-01-05
Brief Title:
Using Artificial Intelligence to Predict Rectal Cancer Outcomes
Sponsor:
Taichung Veterans General Hospital
Organization:
Taichung Veterans General Hospital
Study Overview
Official Title:
Using CNN Image Recognition to Predict Rectal Cancer Outcomes
Status:
COMPLETED
Status Verified Date:
2023-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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 201010120211231 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None