Nature Medicine· 2019Q1
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
- 1,429citations
- Q1SCImago
- 2019year
Short summary
Deep learning can predict microsatellite instability (MSI), a key biomarker for immunotherapy response, directly from standard H&E histology slides in gastrointestinal cancer.
AI-generated from the title and abstract; the full text is not read.
Key points
- Deep residual learning predicts MSI directly from H&E histology.
- MSI status is crucial for predicting immunotherapy response in gastrointestinal cancer.
- Histology-based prediction avoids costly and time-consuming genetic/immunohistochemical tests.
- This method could broaden access to immunotherapy for more patients.
AI-generated from the title and abstract; the full text is not read.
Abstract
Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer. A deep residual learning framework identifies microsatellite instability in histology slides from patients with cancer and can be used to guide immunotherapy.
The authors' abstract, as published at the source. Nature Medicine, 2019 · DOI ↗
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Field: Pathology and Forensic Medicine
Pathology and Forensic MedicineMedicine