Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.
Nat Commun
; 14(1): 6359, 2023 10 11.
Article
em En
| MEDLINE
| ID: mdl-37821431
ABSTRACT
Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Encefálicas
/
Aprendizado Profundo
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Glioma
Tipo de estudo:
Prognostic_studies
Limite:
Adult
/
Humans
Idioma:
En
Revista:
Nat Commun
Assunto da revista:
BIOLOGIA
/
CIENCIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China