Young-onset Colorectal Cancer Screening Based on Artificial Intelligence

Wuhan University

Status

Completed

Conditions

Colorectal Cancer

Treatments

Diagnostic Test: Using routine clinical data and machine learning models.

Study type

Observational

Funder types

Other

Identifiers

NCT06342622

Weiguo Dong

Details and patient eligibility

About

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.

Full 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.

Enrollment

11,000 patients

Sex

All

Ages

18 to 49 years old

Volunteers

Accepts Healthy Volunteers

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