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: Completed
• Conditions: Rectal Cancer Stage III
• Intervention / Treatment: Other: As training material for deep learning model.; Other: As materials for external validation for the buildup model.

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.

• Study Type: Observational
• Enrollment (Actual): 720

Contacts and Locations

Study Locations

• Taiwan
  • Taichung, Taiwan
    • Taichung Verterans General Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 20 years to 100 years (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Rectal cancer diagnosed during 2010.10.1-2022.7.31. clinical T3-4 lesion. with high quality CT images with contrast.

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?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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.	Other: As training material for deep learning model.; 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.	Other: As materials for external validation for the buildup model.; Using the external validation set to evaluate prediction rate and survival outcome.

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Taichung Veterans General Hospital
• Investigators: Principal Investigator: ChunuYu Lin, M.D., Taichung Veterans General Hospital

Study record dates

Study Major Dates

• Study Start (ACTUAL): October 1, 2010
• Primary Completion (ACTUAL): July 31, 2022
• Study Completion (ACTUAL): December 31, 2022

Study Registration Dates

• First Submitted: January 5, 2023
• First Submitted That Met QC Criteria: February 9, 2023
• First Posted (ACTUAL): February 13, 2023

Study Record Updates

• Last Update Posted (ACTUAL): February 13, 2023
• Last Update Submitted That Met QC Criteria: February 9, 2023
• Last Verified: February 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms; Neoplasms by Site; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Intestinal Diseases; Intestinal Neoplasms; Rectal Diseases; Colorectal Neoplasms; Rectal Neoplasms
• Other Study ID Numbers: CE21235B

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No