Young-onset Colorectal Cancer Screening Based on Artificial Intelligence

Completed

• Conditions: Colorectal Cancer
• Interventions: Diagnostic Test: Using routine clinical data and machine learning models.

• Registration Number: NCT06342622
• Lead Sponsor: Renmin Hospital of Wuhan University

Brief 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.

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 Timeline

• Study Start: 2023-12-01
• Status Verified: 2024-01
• Primary Completion: 2024-01-10
• Study Completion: 2024-01-25
• Study First Submitted: 2024-03-15
• Study First Submitted QC: 2024-03-28
• Last Update Submitted: 2024-03-28
• Study First Posted: 2024-04-02
• Last Update Posted: 2024-04-02

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 11000

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Patients with young-onset colorectal cancer	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	Using routine clinical data and machine learning models.	Patients were ruled out young-onset colorectal cancer after receiving colonoscopy examination.

Primary Outcome Measures

Name	Time	Method
The performance of machine learning screening models	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).

Trial Locations

Locations (1)

Renmin Hospital of Wuhan University; 🇨🇳 Wuhan, Hubei, China