Pan-cancer integrative histology-genomic analysis via multimodal deep learning.
Cancer Cell
; 40(8): 865-878.e6, 2022 08 08.
Article
em En
| MEDLINE
| ID: mdl-35944502
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
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Tipos_de_cancer
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Outros_tipos
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
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Neoplasias
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Cancer Cell
Assunto da revista:
NEOPLASIAS
Ano de publicação:
2022
Tipo de documento:
Article