Predicting the immune therapy response of advanced non-small cell lung cancer based on primary tumor and lymph node radiomics features

Abstract

Objective: To identify imaging biomarkers of primary tumors and lymph nodes in patients with stage III–IV non-small cell lung cancer (NSCLC) and assess their predictive ability for treatment response (response vs. non-response) to immune checkpoint inhibitors (ICIs) after 6 months. Methods: Retrospective analysis of 83 NSCLC patients treated with ICIs. Quantitative imaging features of the maximum primary lung tumors and lymph nodes on contrast-enhanced CT imaging were extracted at baseline (time point 0, TP0) and after 2–3 cycles of immunotherapy (time point 1, TP1). Delta-radiomics features (delta-RFs) were defined as the net changes in radiomics features (RFs) between TP0 and TP1. Interobserver interclass coefficient (ICC) and Pearson correlation analyses were applied for feature selection, and logistic regression (LR) was used to build a model for predicting treatment response. Results: Four and five important delta-RFs were selected to construct the nodal and tumor models, respectively. Δ Tumor diameter was used for constructing the clinical prediction model. The predictive efficacy of the nodal model for the treatment response status was higher than that of the tumor and clinical models. In the training set, the AUC values for the three models were 0.96 (95% CI = 0.90–1.00), 0.86 (95% CI = 0.76–0.95), and 0.82 (95% CI = 0.71–0.93), respectively. In the validation set, the AUC values were 0.94 (95% CI = 0.85–1.00), 0.77 (95% CI = 0.56–0.98), and 0.74 (95% CI = 0.48–1.00), respectively. Conclusion: The nodal model based on delta-RFs performed well in distinguishing responders from non-responders and could identify patients more likely to benefit from immunotherapy. Finally, the nodal model exhibited a higher classification performance than the tumor model.