Deep multiple instance learning-enabled gene mutation prediction of lung cancer from histopathology images

Accurate detection of driver gene mutations is crucial for treatment planning and prognosis prediction for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time- and resource-consuming, and as a result, is not available for most patients especially those in low-resource settings. Herein, we aimed to develop an annotation-free Deep learning-enabled artificial intelligence method (DeepGEM) to predict Gene Mutations from routinely acquired histology slides.