Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep Learning

When designing individualized treatment protocols for cancer patients, clinicians must synthesize the information from multiple data modalities into a single parsimonious description of the patient9s personal disease. However, such a description of a patient is never observed. In this work, we propose to model these patient descriptions as latent \emph{discriminative subtypes}---sample representations which can be learned from one data modality and used to contextualize predictions based on another data modality. We apply contextual deep learning to learn these sample-specific discriminative subtypes from lung cancer histopathology imagery. Based on these subtypes, we produce sample-specific transcriptomic models which accurately classify samples as adenocarcinoma, squamous cell carcinoma, or healthy tissue (F1 score of 0.97, outperforming previous state-of-the-art multimodal approaches). Combining these data modalities in a single pipeline not only improves the predictive accuracy, but also gives biological interpretations of the discriminative subtypes and ties the phenotypic patterns present in histopathology images to biological processes.