Machine Learning-Based Radiomic Signatures for Predicting Immunotherapy Response in Advanced Non-Small Cell Lung Cancer

Immunotherapy has brought about many advances in treatment options for patients with advanced non-small cell lung cancer (NSCLC), although there is great variability among patients' responses to the treatment; thus, there is a need for accurate predictive methods. This study seeks to analyze the predictive power of machine learning models using computed tomography (CT)-based radiomic features to identify responders to immunotherapy in patients with metastatic NSCLC. The use of pretreatment CT images and responses to treatment was analyzed in a retrospective cohort of 150 patients with advanced non-small cell lung cancer. The responders were those who demonstrated full or partial effectiveness of treatment, while the non-responders were the patients who had cancer stabilisation or progressive illness. Tumor segmentation was performed, and radiomic features of intensity, shape, texture, and higher-order features were extracted. Feature selection and dimensionality reduction were performed before constructing Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) classification models. The predictive performance of the models was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The RF classifier provided the best accuracy at 88.6%, sensitivity at 86.9%, specificity at 89.7%, and an AUC value of 0.92. In feature ablation, the accuracy rate obtained by CT combined radiomic features was 88.6%, and AUC was 0.92, which was superior to the texture features with accuracy and AUC of 83.7% and 0.87, respectively, and intensity features with 78.5% and 0.82, respectively. It is evident that CT radiomic features might be helpful for investigating the immune therapy response. However, further validation is needed.