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Deep learning for endometrial cancer subtyping and predicting tumor mutational burden from histopathological slides

21 Dec 2024

Abstract excerpt

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 prediction methods, such as sequencing exomes or whole genomes, are costly and often unavailable in clinical settings. We present the...

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PMID: 39709501
DOI: 10.1038/s41698-024-00766-9

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