Ignite Creation Date:
2024-05-06 @ 6:52 PM
Last Modification Date:
2024-10-26 @ 2:56 PM
Study NCT ID:
NCT05816902
Status:
COMPLETED
Last Update Posted:
2023-04-18
First Post:
2023-04-05
Brief Title:
AI Prediction Model and Risk Stratification for Lung Metastasis in Colorectal Cancer
Sponsor:
Peking Union Medical College
Organization:
Peking Union Medical College
Study Overview
Official Title:
Development and Validation of an Artificial Intelligence Prediction Model and a Survival Risk Stratification for Lung Metastasis in Colorectal Cancer From Highly Imbalanced Data
Status:
COMPLETED
Status Verified Date:
2023-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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
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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None