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NCT Number: NCT06342622

Young-onset Colorectal Cancer Screening Based on Artificial Intelligence

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.

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Key information

Conditions

Colorectal Cancer Colonic Diseases Colorectal Neoplasms Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Intestinal Diseases Intestinal Neoplasms Neoplasms Neoplasms by Site Rectal Diseases

Age range

18 year–49 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Renmin Hospital of Wuhan University

Wuhan, Hubei, 430060, China

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Primary outcomes

  1. The performance of machine learning screening models

    Time frame: 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).

Sponsors and collaborators

Lead sponsor

Renmin Hospital of Wuhan University

Other

Registry information

Official study title

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

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Apr 2, 2024
Registry last updated
Apr 2, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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