Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study.
Cell Rep Med
; 4(4): 100980, 2023 04 18.
Article
em En
| MEDLINE
| ID: mdl-36958327
ABSTRACT
Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Colorretais
/
Aprendizado Profundo
Tipo de estudo:
Clinical_trials
/
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Cell Rep Med
Ano de publicação:
2023
Tipo de documento:
Article