  Lung cancer is the leading cause of cancer death worldwide, with lung
adenocarcinoma being the most prevalent form of lung cancer. EGFR positive lung
adenocarcinomas have been shown to have high response rates to TKI therapy,
underlying the essential nature of molecular testing for lung cancers. Despite
current guidelines consider testing necessary, a large portion of patients are
not routinely profiled, resulting in millions of people not receiving the
optimal treatment for their lung cancer. Sequencing is the gold standard for
molecular testing of EGFR mutations, but it can take several weeks for results
to come back, which is not ideal in a time constrained scenario. The
development of alternative screening tools capable of detecting EGFR mutations
quickly and cheaply while preserving tissue for sequencing could help reduce
the amount of sub-optimally treated patients. We propose a multi-modal approach
which integrates pathology images and clinical variables to predict EGFR
mutational status achieving an AUC of 84% on the largest clinical cohort to
date. Such a computational model could be deployed at large at little
additional cost. Its clinical application could reduce the number of patients
who receive sub-optimal treatments by 53.1% in China, and up to 96.6% in the
US.
