AI Prediction Model and Risk Stratification for Lung Metastasis in Colorectal Cancer
NCT ID: NCT05816902
Last Updated: 2023-04-18
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
2779 participants
OBSERVATIONAL
2016-01-01
2020-12-31
Brief Summary
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To assist clinicians with diagnosis and optimal treatment decision-making, we attempted to develop and validate an artificial intelligence prediction model for lung metastasis (LM) in colorectal cancer (CRC) patients.
Method:
The clinicopathological characteristics of 46037 CRC patients from the Surveillance, Epidemiology, and End Results (SEER) database and 2779 CRC patients from a multi-center external validation set were collected retrospectively. After feature selection by univariate and multivariate analyses, six machine learning (ML) models, including logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest, and balanced random forest (BRF), were developed and validated for the LM prediction. The optimization model with best performance was compared to the clinical predictor. In addition, stratified LM patients by risk score were utilized for survival analysis.
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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validation set 1
The validation set 1 was comprised of patients with CRC diagnosed and treated between January 1, 2016, and December 31, 2020, at the Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College.
The location of the patient's treatment
The location of the patient's treatment
validation set 2
The validation set 2 was comprised of patients with CRC diagnosed and treated between January 1, 2016, and December 31, 2020, at the Second Affiliated Hospital of Harbin Medical University.
The location of the patient's treatment
The location of the patient's treatment
Interventions
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The location of the patient's treatment
The location of the patient's treatment
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
ALL
No
Sponsors
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The Second Affiliated Hospital of Harbin Medical University
OTHER
Peking Union Medical College
OTHER
Responsible Party
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Xishan Wang
the Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College
Other Identifiers
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PekingUMC02
Identifier Type: -
Identifier Source: org_study_id
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