P2.07-01 Deep-Learning Based Prediction of c-MET Status from Digitized H&E-Stained Non-Small Cell Lung Cancer Tissue Samples

نویسندگان

چکیده

Genetic profiling of non-small cell lung cancer (NSCLC) tissue has identified alterations in a number genes, including c-MET. Dysregulation c-MET, receptor tyrosine kinase mesenchymal epithelial transition factor, is associated with worse prognosis. Therapeutics targeting c-MET such as the antibody drug conjugate (ADC) Telisotuzimab vedotin (Teliso-V) may be benefit. Currently, immunohistochemical staining used to determine whether protein overexpressed. Here we report development machine learning (ML)-powered method that identifies features tumor microenvironment (TME) predictors overexpression status (positive vs.

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ژورنال

عنوان ژورنال: Journal of Thoracic Oncology

سال: 2022

ISSN: ['1556-0864', '1556-1380']

DOI: https://doi.org/10.1016/j.jtho.2022.07.227