60. AI-guided histopathology predicts brain metastasis in lung cancer patients

Abstract

Brain metastases can occur in nearly half of patients with early and locally advanced (stage I-III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine hematoxylin and eosin (H&E) stained primary tumor tissue sections from Stage I-III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with Stage I to III NSCLC followed for at least 5 years for development of brain metastases (Met+, 65 patients) versus no progression (Met-, 93 patients) were subjected to whole slide imaging. Three separate iterations of DL were performed by first selecting 118 cases (45 Met+, 73 Met-) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met-) were used as the test set. DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish eventual development of brain metastases with an accuracy of 87% (p<0.0001) compared to an average of 57.3% by the four pathologists, and appears to be particularly useful in predicting brain metastases in Stage I patients. DL-based algorithms using routine H&E-stained slides may identify patients likely to develop brain metastases from those that will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy.