Using Artificial Intelligence to Predict Rectal Cancer Outcomes

NCT ID: NCT05723965

Last Updated: 2023-02-13

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 720 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2010-10-01
• Study Completion Date: 2022-12-31

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.

Conditions

Rectal Cancer Stage III

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: RETROSPECTIVE

Study Groups

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.

As training material for deep learning model.

Intervention Type: OTHER

Using labeled images as training materials for artificial intelligence to develop object detecting model.

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.

As materials for external validation for the buildup model.

Intervention Type: OTHER

Using the external validation set to evaluate prediction rate and survival outcome.

Interventions

As training material for deep learning model.

Using labeled images as training materials for artificial intelligence to develop object detecting model.

Intervention Type: OTHER

As materials for external validation for the buildup model.

Using the external validation set to evaluate prediction rate and survival outcome.

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 20 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Taichung Veterans General Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

ChunuYu Lin, M.D.

Role: PRINCIPAL_INVESTIGATOR

Taichung Veterans General Hospital

Locations

Taichung Verterans General Hospital

Taichung, , Taiwan

Countries

Taiwan

Other Identifiers

CE21235B

Identifier Type: -

Identifier Source: org_study_id