A biomarker basing on radiomics for the prediction of overall survival in non-small cell lung cancer patients.

ABSTRACT:

Background

This study aimed at predicting the survival status on non-small cell lung cancer patients with the phenotypic radiomics features obtained from the CT images.

Methods

A total of 186 patients' CT images were used for feature extraction via Pyradiomics. The minority group was balanced via SMOTE method. The final dataset was randomized into training set (n = 223) and validation set (n = 75) with the ratio of 3:1. Multiple random forest models were trained applying hyperparameters grid search with 10-fold cross-validation using precision or recall as evaluation standard. Then a decision threshold was searched on the selected model. The final model was evaluated through ROC curve and prediction accuracy.

Results

From those segmented images of 186 patients, 1218 f

SUBMITTER: He B 

PROVIDER: S-EPMC6180390 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature