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Abstract Hematoxylin and eosin (H SCLC n = 50; others n = 798) incorporating comprehensive patient-level clinical data (electronic health records ConcertAI) integrated with genomics (WES and RNA-seq Caris Labs). There are three steps in our approach: (1) image preprocessing and filtering, yielding 30 million image patches; (2) utilizing pretrained SimCLR models from 57 public oncology histopathology datasets with ResNet-18 as a backbone structure to extract 512-dimensional-feature vectors for each patch; (3) using three unsupervised clustering methods (kmeans, DBSCAN, Leiden clustering) to cluster patches and selected Leiden clustering. We identified 635 primary imaging clusters using an elbow method and generated an image feature matrix by calculating correlations between each patch and cluster centroids; these were aggregated and mapped back to source slides. In this proof-of-concept, distinct image feature patterns characterized SCLC and NSCLC samples. For SCLC, one of the salient features was the presence of hemorrhage, which may be associated with higher rates of fine-needle aspiration biopsy procedure for SCLC compared with NSCLC which was confirmed in the EHR data (p = 0. 032). Derived morphological clusters were correlated with tumor-immune genomic features (Tumor Mutational Burden TMB, Immunologic Constant of Rejection ICR, and Miracle scores1) serving as predictors of response to immune-checkpoint inhibitor therapy. By applying linear models, we detected 11, 96 and 249 significantly associated imaging clusters, respectively, highly enriched with immune cells e. g. , plasma cells, macrophages, lymphocytes, and supporting an infiltrated and inflamed tumor-immune microenvironment. In summary, a multimodal, unsupervised deep learning workflow combining H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 2310.
Wu et al. (2024) studied this question.