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Completed

NCT Number: NCT05723965

Using Artificial Intelligence 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.

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Key information

Conditions

Rectal Cancer Stage III Colorectal Neoplasms Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Intestinal Diseases Intestinal Neoplasms Neoplasms Neoplasms by Site Rectal Diseases Rectal Neoplasms

Age range

20 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Taichung Verterans General Hospital

Taichung, Taiwan

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

As training material for deep learning model.

Other

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

As materials for external validation for the buildup model.

Other

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

Primary outcomes

  1. accuracy of artificial intelligence with experienced physician

    Time frame: 1 week after images done.

    accuracy between artificial intelligence and experienced physician

Secondary outcomes

  1. real life survival outcome of diagnosis by artificial intelligence.

    Time frame: 5 years after diagnosed

    real life survival outcome by artificial intelligence.

Sponsors and collaborators

Lead sponsor

Taichung Veterans General Hospital

Other

Registry information

Official study title

Using CNN Image Recognition to Predict Rectal Cancer Outcomes

Important dates

Study start
2010
Primary completion
2022
Study completion
2022
First posted
Feb 13, 2023
Registry last updated
Feb 13, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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