NCT06342622

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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
11,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Dec 2023

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

December 1, 2023

Completed
1 month until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 10, 2024

Completed
15 days until next milestone

Study Completion

Last participant's last visit for all outcomes

January 25, 2024

Completed
2 months until next milestone

First Submitted

Initial submission to the registry

March 15, 2024

Completed
18 days until next milestone

First Posted

Study publicly available on registry

April 2, 2024

Completed
Last Updated

April 2, 2024

Status Verified

January 1, 2024

Enrollment Period

1 month

First QC Date

March 15, 2024

Last Update Submit

March 28, 2024

Conditions

Colorectal Cancer

Keywords

Young-onset colorectal cancerCancer screeningArtificial intelligenceMachine learning

Outcome Measures

Primary Outcomes (1)

  • 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

Study Arms (2)

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.

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.

Interventions

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.

Patients with young-onset colorectal cancerPatients without young-onset colorectal cancer

Eligibility Criteria

Age18 Years - 49 Years
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64)
Sampling MethodNon-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).

You may qualify if:

  • 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)

You may not qualify if:

  • 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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Renmin Hospital of Wuhan University

Wuhan, Hubei, 430060, China

Location

Related Publications (1)

  • 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.

    PMID: 39438621DERIVED

MeSH Terms

Conditions

Colorectal Neoplasms

Condition Hierarchy (Ancestors)

Intestinal NeoplasmsGastrointestinal NeoplasmsDigestive System NeoplasmsNeoplasms by SiteNeoplasmsDigestive System DiseasesGastrointestinal DiseasesColonic DiseasesIntestinal DiseasesRectal Diseases

Study Officials

  • Dong Weiguo, PhD

    Renmin Hospital of Wuhan University

    STUDY CHAIR

Study Design

Study Type
observational
Observational Model
OTHER
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

March 15, 2024

First Posted

April 2, 2024

Study Start

December 1, 2023

Primary Completion

January 10, 2024

Study Completion

January 25, 2024

Last Updated

April 2, 2024

Record last verified: 2024-01

Locations

CN(1)