H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma
This work addresses a critical bottleneck in cancer treatment by providing a fast, cheap screening tool for EGFR mutations, which could improve patient outcomes globally, though it is incremental as it builds on existing biomarker prediction methods.
The paper tackled the problem of slow and costly EGFR mutation testing in lung adenocarcinoma by developing a multi-modal computational model using pathology images and clinical variables, achieving an AUC of 84% on a large clinical cohort and potentially reducing sub-optimal treatments by 53.1% in China and up to 96.6% in the US.
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.