Abstract
Testing biomarkers in NSCLC relies on complex sequencing and imaging techniques, leading to financial and operational challenges for health care systems. As such, some testings are not systematically performed, while specific targeted therapies exist for those biomarkers. With a prevalence of 13% in NSCLC patients with lung adenocarcinoma, KRAS G12C mutation is considered among the most essential ones. This study aims at showing that AI techniques applied to digital pathology could offer a fast and cheap mass patient screening solution to complement DNA testing by predicting the KRAS mutation status and its G12C subtype from digital pathology slides used in clinical workflow.
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