Rectal Cancer Stage IIINCT05723965
Using Artificial Intelligence to Predict Rectal Cancer Outcomes
An observational study of Rectal Cancer Stage III, run by Taichung Veterans General Hospital at 1 site in Taiwan.
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". Read more about this study
- Who is it for?
- Ages 20 to 100 years
- Conditions the study is for
- Rectal Cancer Stage III
- What is being tested?
- As training material for deep learning model. and As materials for external validation for the buildup model.
- How many people
- 720 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Oct 2010to 31 Dec 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Rectal cancer diagnosed during 2010.10.1-2022.7.31. clinical T3-4 lesion. with high quality CT images with contrast.
Who may be able to join
- clinical staging T3-4 with high quality CT images.
Who cannot join
- 1. not primary malignancy lesion
- 2. not localizing rectum
- 3. T1-2 lesion
- 4. non contrast or poor quality images
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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
- Being tested
- As training material for deep learning model., As materials for external validation for the buildup model.
- Medical subject headings
- Rectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Other: As materials for external validation for the buildup model. |
What is being measured
The primary outcome is the main question the study is designed to answer.
accuracy of artificial intelligence with experienced physician
accuracy between artificial intelligence and experienced physician
Measured: 1 week after images done.
Secondary outcomes (1)
real life survival outcome of diagnosis by artificial intelligence.
real life survival outcome by artificial intelligence.
Measured: 5 years after diagnosed
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Feb 2023
- Study start1 Oct 2010
- Primary completion31 Jul 2022
- Study completion31 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Rectal Cancer Stage IIIStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 720 participants (actual)
Sponsor and investigators
- Lead sponsor
- Taichung Veterans General Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- ChunuYu Lin, M.D., Principal investigator, Taichung Veterans General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05723965
- Sponsor's protocol ID
- CE21235B
- Status last verified
- Feb 2023
- First submitted
- 5 Jan 2023
- Last updated
- 13 Feb 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using CNN Image Recognition to Predict Rectal Cancer Outcomes
- ClinicalTrials.gov ID
- NCT05723965
- Acronym / sponsor ID
- CE21235B
- Conditions
- Rectal Cancer Stage III
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 720 participants (actual)
- Lead sponsor
- Taichung Veterans General HospitalUniversity, hospital or other
- Study start
- 1 Oct 2010 (actual)
- Primary completion
- 31 Jul 2022 (actual)
- Study completion
- 31 Dec 2022 (actual)
- First posted
- 13 Feb 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| rectal cancer lesion images for training | – | Other: As training material for deep learning model.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. |
| rectal cancer lesion images for testing. | – | Other: As materials for external validation for the buildup model.Using the buildup AI deep learning models from training cohort. Evaluating prediction rate of the model and analysis survival outcomes. |
Who may qualify
- Ages
- 20 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Rectal cancer diagnosed during 2010.10.1-2022.7.31. clinical T3-4 lesion. with high quality CT images with contrast.
Key inclusion criteria (1 listed)
- clinical staging T3-4 with high quality CT images.
Key exclusion criteria (4 listed)
- 1. not primary malignancy lesion
- 2. not localizing rectum
- 3. T1-2 lesion
- 4. non contrast or poor quality images
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| As training material for deep learning model.Using labeled images as training materials for artificial intelligence to develop object detecting model. | Other | rectal cancer lesion images for training |
| As materials for external validation for the buildup model.Using the external validation set to evaluate prediction rate and survival outcome. | Other | rectal cancer lesion images for testing. |
Primary outcome
accuracy of artificial intelligence with experienced physician
Time frame: 1 week after images done.
accuracy between artificial intelligence and experienced physician
1 secondary outcome
real life survival outcome of diagnosis by artificial intelligence.
Time frame: 5 years after diagnosed
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Feb 2023.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
ChunuYu Lin, M.D. principal investigator
Taichung Veterans General Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Rectal Cancer Stage III, run by Taichung Veterans General Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
accuracy of artificial intelligence with experienced physician (time frame: 1 week after images done.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Feb 2023.