Deep learning for endometrial cancer subtyping and predicting tumor mutational burden from histopathological slides
Abstract Endometrial cancer (EC) diagnosis traditionally relies on tumor morphology and nuclear grade, but personalized therapy demands a deeper understanding of tumor mutational burden (TMB), i.e., a key biomarker for immune checkpoint inhibition and immunotherapy response. Traditional TMB predicti...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-024-00766-9 |
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