Radiogenomic System for Non-Invasive Identification of Multiple Actionable Mutations and PD-L1 Expression in Non-Small Cell Lung Cancer Based on CT Images.

Abstract

Simple SummaryActional mutations and PD-L1 expression are of paramount importance for the precision treatment of lung cancer. Radiogenomics is a promising field that integrated radiologic images and genomic data through artificial intelligence technology. This approach enables non-invasive assessment of genes, but the vast majority of studies are limited to single gene mutation prediction. Our study aimed to propose a multi-label multi-task deep learning (MMDL) system to predict molecular status based on routinely acquired computed tomography (CT) images using deep learning and radiomics. A dataset of CT images from 1096 non-small cell lung cancer (NSCLC) patients with molecular tests was curated to train, validate and test. The MMDL model achieved superior performance on the classification task of simultaneous identification of eight genes or even ten molecules. This system has the potential to be an auxiliary support tool to advance precision oncology.Purpose: Personalized treatments such as targeted therapy and immunotherapy have revolutionized the predominantly therapeutic paradigm for non-small cell lung cancer (NSCLC). However, these treatment decisions require the determination of targetable genomic and molecular alterations through invasive genetic or immunohistochemistry (IHC) tests. Numerous previous studies have demonstrated that artificial intelligence can accurately predict the single-gene status of tumors based on radiologic imaging, but few studies have achieved the simultaneous evaluation of multiple genes to reflect more realistic clinical scenarios. Methods: We proposed a multi-label multi-task deep learning (MMDL) system for non-invasively predicting actionable NSCLC mutations and PD-L1 expression utilizing routinely acquired computed tomography (CT) images. This radiogenomic system integrated transformer-based deep learning features and radiomic features of CT volumes from 1096 NSCLC patients based on next-generation sequencing (NGS) and IHC tests. Results: For each task cohort, we randomly split the corresponding dataset into training (80%), validation (10%), and testing (10%) subsets. The area under the receiver operating characteristic curves (AUCs) of the MMDL system achieved 0.862 (95% confidence interval (CI), 0.758–0.969) for discrimination of a panel of 8 mutated genes, including EGFR, ALK, ERBB2, BRAF, MET, ROS1, RET and KRAS, 0.856 (95% CI, 0.663–0.948) for identification of a 10-molecular status panel (previous 8 genes plus TP53 and PD-L1); and 0.868 (95% CI, 0.641–0.972) for classifying EGFR / PD-L1 subtype, respectively. Conclusions: To the best of our knowledge, this study is the first deep learning system to simultaneously analyze 10 molecular expressions, which might be utilized as an assistive tool in conjunction with or in lieu of ancillary testing to support precision treatment options.