Colorectal CancerNCT06342622
Young-onset Colorectal Cancer Screening Based on Artificial Intelligence
An observational study of Colorectal Cancer, run by Renmin Hospital of Wuhan University at 1 site in China.
Trial summary
In this study, we aimed to develop, internally and temporally validate the machine learning models to help screen YOCRC bansed on the retrospective extracted Electronic Medical Records (EMR) data. Read more about this study
- Who is it for?
- Ages 18 to 49 yearsHealthy volunteers can take part
- Conditions the study is for
- Colorectal Cancer
- What is being tested?
- Using routine clinical data and machine learning models.
- How many people
- 11,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2023to 25 Jan 2024
- 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 18 to 49 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: The study population we extracted in this study were from department of Gastroenterology, department of Oncology, etc. More specifically, there were two major sources for the study participants: some individuals included in our study had relevant symptoms (such as chronic abdominal pain, altered bowel habit, unexplained weight loss, hematochezia), and they received colonoscopy examination under the advice of the doctor, while some individuals come to the hospital just for a comprehensive physical examination (the physical examination items include colonoscopy).
Who may be able to join
- Newly diagnosed with CRC (YOCRC group)
- Age at 18-49 when diagnosis (YOCRC group)
- Never received any CRC-related treatment (YOCRC group)
- No CRC confirmed by colonoscopy or pathology (non-YOCRC group)
- Age at 18-49 (non-YOCRC group)
Who cannot join
- Hospital stay less than 24 hours or with incomplete Complete Blood Count
- Patients with inflammatory bowel disease or hereditary CRC syndromes
- History of other types of primary malignant tumor and other reasons that made them unsuitable for enrollment
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records
In this study, we aimed to develop, internally and temporally validate the machine learning models to help screen YOCRC bansed on the retrospective extracted Electronic Medical Records (EMR) data.
Read the detailed description
Diagnosis of young-onset colorectal cancer (YOCRC) has become more common in recent decades. Screening CRC among younger adults still remains a challenge. In this study, We plan to retrospectively extracte the relevant clinical data of young individuals who underwent colonoscopy from 2013 to 2022 using Electronic Medical Record (EMR). Multiple supervised machine learning techniques will be applied to distinguish YOCRC and non-YOCRC individuals, the above classifiers will be trained and internally validated in the training dataset and internal validation dataset admitted between 2013 and 2021, respectively. We will also assess the temporal external validity of the classifiers based on the admissions from 2022.
- Conditions
- Colorectal Cancer
- Being tested
- Using routine clinical data and machine learning models.
- Keywords
- Young-onset colorectal cancer, Cancer screening, Artificial intelligence, Machine learning
- Medical subject headings
- Colorectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Using routine clinical data and machine learning models.Diagnostic test
This study used clinical data and machine learning model to screen young-onset colorectal cancer.
| Study group | Role | What they receive |
|---|---|---|
| Patients with young-onset colorectal cancer | Patients were diagnosed with young-onset colorectal cancer after receiving colonoscopy examination. Receives: Diagnostic Test: Using routine clinical data and machine learning models. | |
| Patients without young-onset colorectal cancer | Patients were ruled out young-onset colorectal cancer after receiving colonoscopy examination. Receives: Diagnostic Test: Using routine clinical data and machine learning models. |
What is being measured
The primary outcome is the main question the study is designed to answer.
The performance of machine learning screening models
The performance of young-onset colorectal cancer screening models will be assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC), Accuracy, Recall, Specificity, Negative predictive value (NPV), Positive predictive value (PPV, or called Precision).
Measured: through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Apr 2024
- Study start1 Dec 2023
- Primary completion10 Jan 2024
- Study completion25 Jan 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Colorectal CancerStudies 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 11,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Dong Weiguo, PhD, Study chair, Renmin Hospital of Wuhan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06342622
- Sponsor's protocol ID
- Weiguo Dong
- Status last verified
- Jan 2024
- First submitted
- 15 Mar 2024
- Last updated
- 2 Apr 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Involving patient privacy information
Publications
Zhen J, Li J, Liao F, Zhang J, Liu C, Xie H, Tan C, Dong W. Development and validation of machine learning models for young-onset colorectal cancer risk stratification. NPJ Precis Oncol. 2024 Oct 22;8(1):239. doi: 10.1038/s41698-024-00719-2.
PubMed 39438621
Study at a glance
From the registry record, last updated by the sponsor on 2 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records
- ClinicalTrials.gov ID
- NCT06342622
- Acronym / sponsor ID
- Weiguo Dong
- Conditions
- Colorectal Cancer
- Keywords
- Young-onset colorectal cancer, Cancer screening, Artificial intelligence, Machine learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 11,000 participants (actual)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 1 Dec 2023 (actual)
- Primary completion
- 10 Jan 2024 (actual)
- Study completion
- 25 Jan 2024 (actual)
- First posted
- 2 Apr 2024 (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.
In this study, we aimed to develop, internally and temporally validate the machine learning models to help screen YOCRC bansed on the retrospective extracted Electronic Medical Records (EMR) data.
Detailed description
Diagnosis of young-onset colorectal cancer (YOCRC) has become more common in recent decades. Screening CRC among younger adults still remains a challenge. In this study, We plan to retrospectively extracte the relevant clinical data of young individuals who underwent colonoscopy from 2013 to 2022 using Electronic Medical Record (EMR). Multiple supervised machine learning techniques will be applied to distinguish YOCRC and non-YOCRC individuals, the above classifiers will be trained and internally validated in the training dataset and internal validation dataset admitted between 2013 and 2021, respectively. We will also assess the temporal external validity of the classifiers based on the admissions from 2022.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Patients with young-onset colorectal cancer | – | Diagnostic Test: Using routine clinical data and machine learning models.Patients were diagnosed with young-onset colorectal cancer after receiving colonoscopy examination. |
| Patients without young-onset colorectal cancer | – | Diagnostic Test: Using routine clinical data and machine learning models.Patients were ruled out young-onset colorectal cancer after receiving colonoscopy examination. |
Who may qualify
- Ages
- 18 to 49 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- The study population we extracted in this study were from department of Gastroenterology, department of Oncology, etc. More specifically, there were two major sources for the study participants: some individuals included in our study had relevant symptoms (such as chronic abdominal pain, altered bowel habit, unexplained weight loss, hematochezia), and they received colonoscopy examination under the advice of the doctor, while some individuals come to the hospital just for a comprehensive physical examination (the physical examination items include colonoscopy).
Key inclusion criteria (5 listed)
- Newly diagnosed with CRC (YOCRC group)
- Age at 18-49 when diagnosis (YOCRC group)
- Never received any CRC-related treatment (YOCRC group)
- No CRC confirmed by colonoscopy or pathology (non-YOCRC group)
- Age at 18-49 (non-YOCRC group)
Key exclusion criteria (3 listed)
- Hospital stay less than 24 hours or with incomplete Complete Blood Count
- Patients with inflammatory bowel disease or hereditary CRC syndromes
- History of other types of primary malignant tumor and other reasons that made them unsuitable for enrollment
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 |
|---|---|---|
| Using routine clinical data and machine learning models.This study used clinical data and machine learning model to screen young-onset colorectal cancer. | Diagnostic test | Patients with young-onset colorectal cancer, Patients without young-onset colorectal cancer |
Primary outcome
The performance of machine learning screening models
Time frame: through study completion, an average of 1 year
The performance of young-onset colorectal cancer screening models will be assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC), Accuracy, Recall, Specificity, Negative predictive value (NPV), Positive predictive value (PPV, or called Precision).
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 2 Apr 2024.
- Derived from this study: PubMed 39438621Zhen J, Li J, Liao F, Zhang J, Liu C, Xie H, Tan C, Dong W. Development and validation of machine learning models for young-onset colorectal cancer risk stratification. NPJ Precis Oncol. 2024 Oct 22;8(1):239. doi: 10.1038/s41698-024-00719-2.
- Individual participant dataSharing planned: Undecided. Involving patient privacy information
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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.
Dong Weiguo, PhD study chair
Renmin Hospital of Wuhan University
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 Colorectal Cancer, run by Renmin Hospital of Wuhan University at 1 site in China.
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 5 inclusion and 3 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 2 Apr 2024. 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 China. 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?
The performance of machine learning screening models (time frame: through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06342622, which also lists 1 linked publication.
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 2 Apr 2024.