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Lung cancer is the leading cause of cancer death worldwide, with lungadenocarcinoma being the most prevalent form of lung cancer. EGFR positive lungadenocarcinomas have been shown to have high response rates to TKI therapy,underlying the essential nature of molecular testing for lung cancers. Despitecurrent guidelines consider testing necessary, a large portion of patients arenot routinely profiled, resulting in millions of people not receiving theoptimal treatment for their lung cancer. Sequencing is the gold standard formolecular testing of EGFR mutations, but it can take several weeks for resultsto come back, which is not ideal in a time constrained scenario. Thedevelopment of alternative screening tools capable of detecting EGFR mutationsquickly and cheaply while preserving tissue for sequencing could help reducethe amount of sub-optimally treated patients. We propose a multi-modal approachwhich integrates pathology images and clinical variables to predict EGFRmutational status achieving an AUC of 84% on the largest clinical cohort todate. Such a computational model could be deployed at large at littleadditional cost. Its clinical application could reduce the number of patientswho receive sub-optimal treatments by 53.1% in China, and up to 96.6% in theUS.

H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma