Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation

Objective: This study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model is essential for accurate treatment outcome prediction. Our study were established 176 discrimination models constructed with 22 feature selection methods and 8 classifiers. The predictive performance of each model was evaluated using the AUC, ACC,sensitivity and specificity,where the optimal model was identified. Results: : There were totally 107 radiomics features and 7 clinical features obtained from each patient. After feature selection, the top-ten most relevant features were fed to train 176 models.Significant performance variations were observed in the established models, with the best performance achieved by the logistic regression model using gini-index feature selection (AUC=0.797, ACC=0.722, sensitivity=0.758, specificity=0.693).The median R-score was 0.518 (IQR,0.023-0.987), and the patients were divided into high-risk and low-risk groups based on this cut-off value. The KM survival curves of the two groups demonstrated evident stratification results ( p =0.000).