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Cancer Cell DOI:10.1016/j.ccell.2022.07.004

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

Publication TypeJournal Article
Year of Publication2022
AuthorsChen, RJ, Lu, MY, Williamson, DFK, Chen, TY, Lipkova, J, Noor, Z, Shaban, M, Shady, M, Williams, M, Joo, B, Mahmood, F
JournalCancer Cell
Volume40
Issue8
Pages865-878.e6
Date Published2022 Aug 08
ISSN1878-3686
KeywordsAlgorithms, Deep Learning, Genomics, Humans, Neoplasms, Prognosis
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 outcomes. We present all analyses for morphological and molecular correlates of patient prognosis across the 14 cancer types at both a disease and a patient level in an interactive open-access database to allow for further exploration, biomarker discovery, and feature assessment.

DOI10.1016/j.ccell.2022.07.004
Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/35944502?dopt=Abstract

Alternate JournalCancer Cell
PubMed ID35944502
Grant ListR35 GM138216 / GM / NIGMS NIH HHS / United States