Cancer Immunology Research current issue

Cancer Immunology Research current issue

No predictive biomarkers can robustly identify patients with non–small cell lung cancer (NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes (“delta”) in the radiomic texture (DelRADx) of CT patterns both within and outside tumor nodules before and after two to three cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 patients with NSCLC at two institutions, who were divided into a discovery set (D 1 = 50) and two independent validation sets (D 2 = 62, D 3 = 27). Intranodular and perinodular texture descriptors were extracted, and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST-derived response. Association of delta-radiomic risk score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies ( n = 36) was also evaluated. The LDA classifier yielded an AUC of 0.88 ± 0.08 in distinguishing responders from nonresponders in D 1 , and 0.85 and 0.81 in D 2 and D 3 . DRS was associated with OS [HR: 1.64; 95% confidence interval (CI), 1.22–2.21; P = 0.0011; C-index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in patients with NSCLC. Footnotes Note: Supplementary data for this article are available at Cancer Immunology Research Online (http://cancerimmunolres.aacrjournals.org/). Prior presentations: Initial results of this work were presented at SITC 2018 and WCLC 2018. Cancer Immunol Res 2020;8:108–19 Received June 24, 2019. Revision received September 4, 2019. Accepted November 5, 2019. Published first November 12, 2019. ©2019 American Association for Cancer Research. Log in using your username and password Username * Forgot your user name or password? Purchase access You may purchase access to this article. This will require you to create an account if you don't.



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