Using Artificial Intelligence to Predict Rectal Cancer Outcomes

NCT05723965 | Completed

• 1 other identifier: CE21235B
• Study Type: observational
• Target: 720
• Locations: 1 country
• Sites: 1

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.

Conditions

Rectal Cancer Stage III

Outcome Measures

Primary Outcomes (1)

• accuracy of artificial intelligence with experienced physician

  accuracy between artificial intelligence and experienced physician

  1 week after images done.

Secondary Outcomes (1)

• real life survival outcome of diagnosis by artificial intelligence.

  5 years after diagnosed

Study Arms (2)

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.

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.

Interventions

As training material for deep learning model. OTHER

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

rectal cancer lesion images for training

As materials for external validation for the buildup model. OTHER

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

rectal cancer lesion images for testing.

Eligibility Criteria

• Age: 20 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• clinical staging T3-4 with high quality CT images.

You may not qualify if:

• \. not primary malignancy lesion
• \. not localizing rectum
• \. T1-2 lesion
• \. non contrast or poor quality images

Sponsors & Collaborators

• Taichung Veterans General Hospital: lead

Study Sites (1)

Taichung Verterans General Hospital

Taichung, Taiwan

Location

MeSH Terms

Conditions

Rectal Neoplasms

Condition Hierarchy (Ancestors)

Colorectal Neoplasms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Intestinal Diseases; Rectal Diseases

Study Officials

• ChunuYu Lin, M.D.

  Taichung Veterans General Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 5, 2023
• First Posted: February 13, 2023
• Study Start: October 1, 2010
• Primary Completion: July 31, 2022
• Study Completion: December 31, 2022
• Last Updated: February 13, 2023

Record last verified: 2023-02

Locations

TW (1)