Predicting acquired T790M mutations in non-small cell lung cancer after EGFR-TKI therapy: integration of delta-radiomics and clinical characteristics with SHAP interpretation

The aim of this study was to evaluate the value of combining delta-radiomics and clinical characteristics in predicting the risk of EGFR T790M resistance mutation in patients with non-small cell lung cancer (NSCLC) prior to first-line treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (EGFR-TKIs). This retrospective study included non-small cell lung cancer (NSCLC) patients whose disease progressed after first-line treatment with an EGFR tyrosine kinase inhibitor (TKI) between January 2013 and September 2019. The patients were randomly assigned to the training and test cohorts. Four predictive models for the acquisition of the T790M mutation were developed on the basis of clinical characteristics, non-contrast-enhanced computed tomography (NECT) radiomics features, delta-radiomics features, and a combination of clinical and delta-radiomics features. The optimal model was selected using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Additionally, the Shapley additive explanations (SHAP) interpretability framework was applied to visualize and interpret the model’s decision-making process. A total of 285 patients were included in this study (213 in the training set and 72 in the test set). In the test set, the AUCs for the clinical model, NECT radiomics model, delta-radiomics model, and combined model were 0.70, 0.63, 0.75, and 0.82, respectively. The corresponding accuracies were 66.7%, 57.0%, 72.2%, and 79.2%, respectively. The SHAP tool identified 10 significant features, including 2 clinical features and 8 radiomic features. A delta-radiomics-based model combined with clinical features showed potential for predicting acquired T790M mutation status and provided preliminary interpretability, further prospective multicentre validation is needed before clinical application.