Using Artificial Intelligence to Predict Rectal Cancer Outcomes

Rectal Cancer Stage III Clinical Trial

Official title:

Using CNN Image Recognition to Predict Rectal Cancer Outcomes

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05723965
• Other study ID #: CE21235B
• Status: Completed
• Start date: October 1, 2010
• Est. completion date: December 31, 2022

Study information

• Verified date: February 2023
• Source: Taichung Veterans General Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 720
• Est. completion date: December 31, 2022
• Est. primary completion date: July 31, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 20 Years to 100 Years

Eligibility

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

Related Conditions & MeSH terms

• Rectal Cancer Stage III
• Rectal Neoplasms

Intervention

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

Locations

Country	Name	City	State
Taiwan	Taichung Verterans General Hospital	Taichung

Sponsors (1)

Lead Sponsor	Collaborator
Taichung Veterans General Hospital

Country where clinical trial is conducted

Taiwan, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	accuracy of artificial intelligence with experienced physician	accuracy between artificial intelligence and experienced physician	1 week after images done.
Secondary	real life survival outcome of diagnosis by artificial intelligence.	real life survival outcome by artificial intelligence.	5 years after diagnosed