A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer.

ABSTRACT:

Purpose

To evaluate the potential of radiomics approach for predicting No. 14v station lymph node metastasis (14vM) in gastric cancer (GC).

Methods

The contrast enhanced CT (CECT) images with corresponding clinical information of 288 GC patients were retrospectively collected. Patients were separated into training set (n = 202) and testing set (n = 86). A total of 1,316 radiomics feature were extracted from portal venous phase images of CECT. Seven machine learning (ML) algorithms including naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. A combined model was establi

SUBMITTER: Ma T 

PROVIDER: S-EPMC11527654 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature