AI Prediction Model and Risk Stratification for Lung Metastasis in Colorectal Cancer
Development and Validation of an Artificial Intelligence Prediction Model and a Survival Risk Stratification for Lung Metastasis in Colorectal Cancer From Highly Imbalanced Data
Background:
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.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- patients with pathologic confirmation of a primary CRC diagnosis
Exclusion Criteria:
- (1) patients with multiple primary cancers or other malignancies; (2) patients identified via autopsy or death certificate; and (3) patients with uncertain clinical data values
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
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.
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The location of the patient's treatment
|
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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.
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The location of the patient's treatment
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
lung metastasis
Time Frame: through study completion, an average of 3 month
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diagnosed with lung metastasis
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through study completion, an average of 3 month
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Digestive System Diseases
- Pathologic Processes
- Respiratory Tract Diseases
- Neoplasms
- Lung Diseases
- Neoplasms by Site
- Gastrointestinal Neoplasms
- Digestive System Neoplasms
- Gastrointestinal Diseases
- Respiratory Tract Neoplasms
- Thoracic Neoplasms
- Colonic Diseases
- Intestinal Diseases
- Intestinal Neoplasms
- Rectal Diseases
- Neoplastic Processes
- Lung Neoplasms
- Colorectal Neoplasms
- Neoplasm Metastasis
Other Study ID Numbers
Other Study ID Numbers
- PekingUMC02
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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