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Background: Here, we aimed to assess the association of ALK variants and alterations with ensartinib response duration in NSCLC, and explore the potential value of computed tomography (CT) radiomic features in predicting progression-free survival (PFS)., Methods: We enrolled 88 patients with identified ALK variant NSCLC in a multicenter phase 2 trial, and assessed the impact of ALK variants and secondary ALK alterations on the clinical outcome (response duration) of patients receiving ensartinib. We also established a multifactorial model of clinicopathological and quantitative CT radiomic features to predict PFS and risk stratification. Kaplan-Meier analysis was conducted to identify risk factors for tumor progression., Results: Univariate analysis indicated a statistical difference (p = 0.035) in PFS among ALK variants in three classifications (V1, V3, and other variants). Secondary ALK alterations were adversely associated with PFS both in univariate (p = 0.008) and multivariate (p = 0.04) analyses and could identify patients at high risk for early progression in the Kaplan-Meier analysis (p = 0.002). Additionally, response duration to crizotinib <1 year and liver metastasis were adversely associated with PFS. The combined model, composed of clinicopathological signature and CT radiomic signature, showed good prediction ability with the area under the receiver operating characteristic curve being 0.85, and 0.89 in the training and validation dataset respectively., Conclusions: Our study showed that secondary ALK alterations were adversely associated with ensartinib efficacy, and that ALK variants might not correlate with PFS. The quantitative radiomic signature provided added prognostic prediction value to the clinicopathological features., (© 2021 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.)

Background: The prognosis of lung cancer varies widely, even in cases wherein the tumor stage, genetic mutation, and treatment regimens are the same. Thus, an effective means for risk stratification of patients with lung cancer is needed. Purpose: To develop and validate a combined model for predicting progression-free survival and risk stratification in patients with advanced anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) treated with ensartinib. Material and Methods: We analyzed 203 tumor lesions in 114 patients and evaluated average radiomic feature measures from all lesions at baseline and changes in these features after early treatment (Δradiomic features). Combined models were developed by integrating clinical with radiomic features. The prediction performance and clinical value of the proposed models were evaluated using receiver operating characteristic analysis, calibration curve, decision curve analysis (DCA), and Kaplan–Meier survival analysis. Results: Both the baseline and delta combined models achieved predictive efficacy with a high area under the curve. The calibration curve and DCA indicated the high accuracy and clinical usefulness of the combined models for tumor progression prediction. In the Kaplan–Meier analysis, the delta and baseline combined models, Δradiomic signature, and two selected clinical features could distinguish patients with a higher progression risk within 42 weeks. The delta combined model had the best performance. Conclusion: The combination of clinical and radiomic features provided a prognostic value for survival and progression in patients with NSCLC receiving ensartinib. Radiomic-signature changes after early treatment could be more valuable than those at baseline alone. [ABSTRACT FROM AUTHOR]