Pan-cancer integrative histology-genomic analysis via multimodal deep learning.

ABSTRACT: The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how these data sources can be integrated to develop joint image-omic prognostic models. Additionally, identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We use multimodal deep learning to jointly examine pathology whole-slide images and molecular profile data from 14 cancer types. Our weakly supervised, multimodal deep-learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and discover prognostic features that correlate with poor and favorable outc

SUBMITTER: Chen RJ 

PROVIDER: S-EPMC10397370 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature