Young-onset Colorectal Cancer Screening Based on Artificial Intelligence

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

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.

Study Overview

• Status: Completed
• Conditions: Colorectal Cancer
• Intervention / Treatment: Diagnostic test: Using routine clinical data and machine learning models.

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 Type: Observational
• Enrollment (Actual): 11000

Contacts and Locations

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China, 430060
      • Renmin Hospital of Wuhan University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

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).

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?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Patients with young-onset colorectal cancer; Patients were diagnosed with young-onset colorectal cancer after receiving colonoscopy examination.	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.
Patients without young-onset colorectal cancer; Patients were ruled out young-onset colorectal cancer after receiving colonoscopy examination.	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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

Collaborators and Investigators

• Sponsor: Renmin Hospital of Wuhan University
• Investigators: Study Chair: Dong Weiguo, PhD, Renmin Hospital of Wuhan University

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2023
• Primary Completion (Actual): January 10, 2024
• Study Completion (Actual): January 25, 2024

Study Registration Dates

• First Submitted: March 15, 2024
• First Submitted That Met QC Criteria: March 28, 2024
• First Posted (Actual): April 2, 2024

Study Record Updates

• Last Update Posted (Actual): April 2, 2024
• Last Update Submitted That Met QC Criteria: March 28, 2024
• Last Verified: January 1, 2024

More Information

Terms related to this study

• Keywords: Artificial intelligence; Machine learning; Cancer screening; Young-onset colorectal cancer
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms; Neoplasms by Site; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Intestinal Neoplasms; Rectal Diseases; Colorectal Neoplasms
• Other Study ID Numbers: Weiguo Dong

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: Involving patient privacy information

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No