Abstract SY05-03: Radiomics of immune therapy in NSCLC

Checkpoint blockade immunotherapy provides improved long-term survival in a subset of advanced stage NSCLC patients. Currently, predictive biomarkers of immunotherapy response are an unmet clinical need. CT and PET/CT images are routinely obtained during workup of NSCLC patients, and we hypothesize that quantitative analysis of these images (radiomics) can fill this need. In a study of CT radiomics, 332 NSCLC patients treated with checkpoint blockade were curated into independent training (N = 180), validation (N = 90) and test cohorts (N = 62) for radiomic analyses. The most informative clinical and radiomic features were subjected to Classification and Regression Tree (CART) analysis which identified novel cut-points to stratified patients into four different survival risk-groups. The identified very-high risk group was associated with extremely poor OS in both test ([HR] = 5.35, 95% CI= 2.14 - 13.36) and validation (HR = 13.81 95% CI: 2.58 - 73.93) cohorts when compared to the low-risk group (HRs = 1.00). Similar findings were observed for 3-year PFS (0% HR = 1.00 vs. 29.8% HR = 3.95). The most predictive radiomic feature was the GLCM inverse difference, which was representative of a set of nine features that were related to tumor heterogeneity. For further validation, this feature was then tested against four additional independent NSCLC data sets (total N = 446) who were treated with surgery, radio- or chemo-therapies, showing that it was significantly associated with OS suggesting a pan-marker. The biological underpinnings of the GLCM inverse difference were investigated using gene expression data and was associated with the hypoxia-related carbonic anhydrase, CA-IX, and confirmed by immunohistochemistry. In a separate study of PET/CT radiomics, we curated data sets from 837 NSCLC patients from four international institutions. Two residual-convolutional-network (ResCNN) models were generated to predict EGFR mutation and PDL1 positive status in training (N=429) and validation (N=187) cohorts and were subsequently tested on external cohorts to predict EGFR mutation status (N=65) and PDL1 expression status (N=85). The EGFR and PDL1 scores demonstrated high accuracy to predict EGFR mutation status with AUCs of 0.86, 0.83, and 0.81, and in prediction of PDL1 positive status with AUCs of 0.89, 0.84, and 0.82 for training, validation, and external test cohorts, respectively. The generated EGFR and PDL1 deep learning scores were subsequently used to predict PFS in patients treated with EGFR TKI (N=67) and chekpoint blockade (N=149). For patients with high EGFR score, EGFR-TKI treatment was significantly associated with longer PFS in patients with a low PDL1 score (p=0.013); for patients with a high PDL1 score, checkpoint blockade was significantly associated with longer PFS (p Citation Format: Wei Mu, Ilke Tunali, Matthew B. Schabath, Robert J. Gillies. Radiomics of immune therapy in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr SY05-03.