Young-onset Colorectal Cancer Screening Based on Artificial Intelligence

Colorectal Cancer Clinical Trial

Official title:

Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06342622
• Other study ID #: Weiguo Dong
• Status: Completed
• Start date: December 1, 2023
• Est. completion date: January 25, 2024

Study information

• Verified date: January 2024
• Source: Renmin Hospital of Wuhan University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 11000
• Est. completion date: January 25, 2024
• Est. primary completion date: January 10, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 49 Years

Eligibility

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

Related Conditions & MeSH terms

• Colorectal Cancer
• Colorectal Neoplasms

Intervention

Diagnostic Test:
• Using routine clinical data and machine learning models.
This study used clinical data and machine learning model to screen young-onset colorectal cancer.

Locations

Country	Name	City	State
China	Renmin Hospital of Wuhan University	Wuhan	Hubei

Sponsors (1)

Lead Sponsor	Collaborator
Renmin Hospital of Wuhan University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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