Clinical trial NCT06342622: Young-onset Colorectal Cancer Screening Based on Artificial Intelligence
Official title: Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Colorectal Cancer
Keywords: Young-onset colorectal cancer
Cancer screening
Artificial intelligence
Machine learning
Lead sponsor: Renmin Hospital of Wuhan University
Sponsor class: OTHER
First posted: 2024-04-02
Enrollment: 11000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 49
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: RETROSPECTIVE
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).

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.

Eligibility criteria: 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

Primary outcomes:
measure: The performance of machine learning screening models
timeFrame: through study completion, an average of 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Renmin Hospital of Wuhan University
state: Hubei
Study officials:
affiliation: Renmin Hospital of Wuhan University
name: Dong Weiguo, PhD
role: STUDY_CHAIR
References: