Young-onset Colorectal Cancer Screening Based on Artificial Intelligence
Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
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Hubei
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Wuhan, Hubei, China, 430060
- Renmin Hospital of Wuhan University
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- 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)
Exclusion Criteria:
- 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
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Patients with young-onset colorectal cancer
Patients were diagnosed with young-onset colorectal cancer after receiving colonoscopy examination.
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This study used clinical data and machine learning model to screen young-onset colorectal cancer.
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Patients without young-onset colorectal cancer
Patients were ruled out young-onset colorectal cancer after receiving colonoscopy examination.
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This study used clinical data and machine learning model to screen young-onset colorectal cancer.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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The performance of machine learning screening models
Time Frame: through study completion, an average of 1 year
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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).
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through study completion, an average of 1 year
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Study Chair: Dong Weiguo, PhD, Renmin Hospital of Wuhan University
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- Weiguo Dong
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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