Deep learning-based ensemble model using hematoxylin and eosin (H&E) whole slide images (WSIs) for the prediction of MET mutations in non-small cell lung cancer (NSCLC).

e13578 Background: The MET proto-oncogene is involved in tumorigenesis and progression and has emerged as an attractive targetable genomic alteration in NSCLC. However, it has not been actively tested in NSCLC due to the lack of a cost-effective screening tool. We developed a deep-learning based model to predict genomic profiles from H&E WSI and applied it to MET pathologic mutations in NSCLC. The prediction model is based on an ensemble model trained not solely on H&E images but also on multi-layered semantic contents that were produced by a pre-trained AI analyzer, Lunit SCOPE IO. Methods: The model was trained on the MET aberration (exon 14 skipping, n=191; pathogenic mutation, n=109) and paired wild-type dataset (n=500). To extract deep features from raw H&E images, a self-supervised vision transformer was used, and an AI-based pathology profiling analyzer extracted semantic contents such as the spatial information of tumor cells, lymphocytes, cancer epithelium, and cancer stroma. A set of classifiers was trained based on the two features, and the ensemble of these features was used to improve robustness. Following cross-validation, the model was applied to an independent clinical dataset with MET sequencing results from The Cancer Genome Atlas (TCGA) LUAD and LUSC datasets (n=914), Samsung Medical Center (SMC, n=361), and Chonnam National University Hospital (CNUH, n=54). Results: The best cross-validation performances of the models predicting MET aberration measured by mean area under the receiver operating characteristic curve (AUROC) were 0.772 when trained by only H&E images (HE-only), 0.788 by AI semantic content with MLP classifier (AISC-MLP), and 0.803 by AISC with random forest (AI-RF), respectively. An ensemble of the three models showed an increased AUROC of 0.837 in the training dataset by cross-validation. These models were applied to the external validation dataset (n=1,329), including 20 (1.5%) MET exon 14 skipping and known pathologic mutations. The mean AUROC to predict MET aberration by the ensemble model was 0.817 with 95% sensitivity, 64.7% specificity. The AUROC of TCGA, SMC, and CNUH cohorts were 0.815, 0.802, and 0.812, respectively. Conclusions: An AI-based ensemble model combining H&E images with semantic contents extracted from pre-developed AI models significantly improved the accuracy and robustness of MET pathogenic mutation prediction using an H&E sample in NSCLC. These findings allow for cost-effective screening for MET alterations.