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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients with young-onset colorectal cancer | Patients were diagnosed with young-onset colorectal cancer after receiving colonoscopy examination. |
| |
| Patients without young-onset colorectal cancer | Patients were ruled out young-onset colorectal cancer after receiving colonoscopy examination. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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). | through study completion, an average of 1 year |
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Inclusion Criteria:
Exclusion Criteria:
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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).
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| Name | Affiliation | Role |
|---|---|---|
| Dong Weiguo, PhD | Renmin Hospital of Wuhan University | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Renmin Hospital of Wuhan University | Wuhan | Hubei | 430060 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39438621 | Derived | 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. |
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Involving patient privacy information
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| ID | Term |
|---|---|
| D015179 | Colorectal Neoplasms |
| ID | Term |
|---|---|
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
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| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D003108 | Colonic Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |