Multimodal prediction of tyrosine kinase inhibitors therapy outcomes in advanced EGFR-mutated NSCLC patients - Xiaodong Chai; Hongxia Li; Moxuan Yang; Junyang Zeng; Genxiu Chen; Yongmeng Li; Wei Wang; Zichen Liu; Kun Li; Tongmei Zhang; Shuhao Wang; Nanying Che (Journal of Translational Medicine)
Abstract Background Tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR) are effective first-line treatments for advanced non-small-cell lung cancer (NSCLC) patients with EGFR mutations. However, some patients do not respond well, and some experience rapid progression despite initial benefit. This study aims to develop multimodal models integrating pre-treatment histopathological images and clinical variables to predict EGFR-TKIs therapy response and progression-free survival (PFS) in stage IV NSCLC patients. Methods This retrospective study collected hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) and clinical variables, including clinical characteristics and laboratory indicators, from 247 treatment-naïve, stage IV EGFR-mutated NSCLC patients treated with EGFR-TKIs at Beijing Chest Hospital between 2017 and 2020. For the prediction of treatment response (TR) and progression-free survival (PFS) time, both unimodal and multimodal models were developed. Unimodal models were based on whole-slide images (WSIs) or clinical variables alone, while multimodal models integrated both data types. Model performance was evaluated using distinct metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity for TR prediction, and mean absolute error (MAE), mean squared error (MSE), and Kaplan–Meier analysis for PFS time estimation. Results The multimodal model for predicting therapy response (TR-M) achieved AUC, accuracy, sensitivity and specificity of 0.943, 0.841, 0.800 and 1.000 in test set, respectively, significantly outperforming the two unimodal models. The multimodal PFS model (PFS-M) directly estimated PFS time after EGFR-TKIs therapy with a MAE of 2.690 months and a MSE of 12·829 in the test set, significantly outperforming unimodal models and effectively stratifying patients into high and low risk groups (HR: 10.034, 95% CI 3.879–25.956; p < 0.0001). Furthermore, our models revealed that an increased aggregation of lymphocytes within the patient tissues was indicative of a more favorable therapy response, whereas a higher percentage of lymphocytes in the peripheral blood was associated with prolonged PFS. Conclusion The multimodal models (TR-M and PFS-M) demonstrated high accuracy in predicting the therapy response and directly estimating of PFS following EGFR-TKIs treatment in stage IV NSCLC patients, revealing significant potential for aiding clinical decisions and facilitating personalized treatment strategies.