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

Apaar Sadhwani¹, Huang-Wei Chang², Ali Behrooz², Trissia Brown³, Isabelle Auvigne-Flament³, Hardik Patel², Robert Findlater², Vanessa Velez², Fraser Tan¹, Kamilla Tekiela², Ellery Wulczyn¹, Eunhee S Yi⁴, Craig H Mermel¹, Debra Hanks², Po-Hsuan Cameron Chen¹, Kimary Kulig^2,5, Cory Batenchuk², David F Steiner⁶, Peter Cimermancic²

¹Google Health, Palo Alto, CA, USA.

²Verily Life Sciences, South San Francisco, CA, USA.

³Google Health via Vituity, Emeryville, CA, USA.

⁴Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.

⁵PathPresenter Corp., New York, NY, USA.

⁶Google Health, Palo Alto, CA, USA. davesteiner@google.com.

PubMed PMC DOI PDF

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, time-consuming laboratory processes. Histologic subtype classification represents an established component…

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