Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images

Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images

Abstract Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires use of finite tissue specimens and costly...

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Autores principales: Apaar Sadhwani, Huang-Wei Chang, Ali Behrooz, Trissia Brown, Isabelle Auvigne-Flament, Hardik Patel, Robert Findlater, Vanessa Velez, Fraser Tan, Kamilla Tekiela, Ellery Wulczyn, Eunhee S. Yi, Craig H. Mermel, Debra Hanks, Po-Hsuan Cameron Chen, Kimary Kulig, Cory Batenchuk, David F. Steiner, Peter Cimermancic
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/bb85166704e240079299ad3d736a4fbb
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