Abstract 868: Prediction of clinical benefit to checkpoint blockade in advanced NSCLC patients using radiomics of PET/CT images

Abstract

Abstract Immunotherapy has emerged as a one of the most successful treatment approach in advanced Non-small Cell Lung Cancer (NSCLC) patients. However, only 25-50% patients obtain durable clinical benefit (DCB), and 7-43% patient may suffer from immune-related severe adverse events. Therefore robust biomarkers that are predictive of response immune-checkpoint blockades at baseline are needed. Though PD-L1 expression by immunohistochemistry (IHC) is the only clinically-approved biomarker to trigger treatment decisions, sampling bias and the dynamic nature of PD-L1 expression can affect the final results. In this study, we investigated the value of PET/CT images in the predicting progression-free survival (PFS) and overall survival (OS) of immunotherapy in advanced NSCLC patients. The retrospective arm of this study included 400 patients with histologically-confirmed NSCLC, and these were split into training (N=284) and test (N=116) cohorts. A residual-convolutional-network model was developed to generate a deep learning score (PDL1-DLS) to predict the PD-L1 expression status with PET/CT images. Another 146 retrospective patients with histologically confirmed stage IIIB-IV NSCLC treated with immunotherapy (IO) were split into training (N=99) and test (N=47) cohorts, and a multiparametric radiomics signature (mpRS) generated from conventional PET and CT radiomics features was developed to predict DCB using an improved least absolute shrinkage and selection operator (LASSO) method. Subsequently, multivariable Cox regression models for PFS and OS prediction were trained with the mpRS, PDL1-DLS, and clinical characteristics, and these were further validated with 48 prospective IO-treated NSCLC patients. The PDL1-DLS significantly discriminated PD-L1 positive and negative patients with AUCs of 0.89 (95%CI:0.84-0.94) and 0.84 (95%CI:0.76-0.92) for training and test cohorts. The mpRS could predict DCB with AUCs of 0.86 (95%CI:0.79-0.94) and 0.83 (95%CI:0.71-0.94) in the training and test cohorts. In both retrospective and prospective IO-treated cohorts, the combination of binarized PDL1-DLS and binarized mpRS significantly improved the prediction of PFS and OS compared to single binarized PDL1-DLS (PFS: p<0.001, OS: p<0.001) or single binarized mpRS (PFS: p<0.040, OS: p<0.10). Subsequent multivariable Cox regression models identified binarized mpRS, binarized PDL1-DLS, ECOG, and histology as significant independent variables, and could achieve significant higher C-indices of 0.75 (95%CI:0.70-0.79) and 0.79 (95%CI:0.73-0.86) in prediction of PFS, 0.79 (95%CI:0.73-0.85) and 0.76 (95%CI:0.61-0.92) in prediction of OS in the retrospective and prospective IO-treated cohorts. We have therefore observed that, prior to therapy initiation, PET/CT fusion images can be used independently to predict PD-L1 status or DCB of immunotherapy in advanced NSCLC patients. Both of these models can be leveraged to improve more precise and individualized decision support in the treatment of patients with advanced NSCLC. Citation Format: Wei Mu, Ilke Tunali, Jhanelle E. Gray, Jin Qi, Matthew B. Schabath, Robert J. Gillies. Prediction of clinical benefit to checkpoint blockade in advanced NSCLC patients using radiomics of PET/CT images [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 868.