Metastatic non-small cell lung cancer (NSCLC) and brain edema: a topographical and clinicopathological investigation utilizing deep learning-based artificial intelligence (DLBAI)

Abstract

Abstract Tumor-associated vasogenic brain edema is a well-known contributor of morbidity and mortality in patients with metastatic disease to the brain. It is widely accepted that brain metastases (BM) is associated with extensive edema and can cause increased symptomatology such as pain, neurologic deficit, and elevated intracranial pressure depending on extent and location. We present a proof-of-concept retrospective analysis utilizing DLBAI to segment and detect radiological and topographical patterns of peritumoral edema and assess for clinicopathological correlates in 84 patients with NSCLC and BM who underwent surgical resection and were not previously on steroids. We found that overall, tumors in all locations demonstrated a mean 10:1 edema to tumor ratio (ETR) and an occipital tumor location was associated with a significantly elevated ETR. Within our cohort there were no other factors that were significantly associated with ETR. This study demonstrates a proof-of-concept that DLBAI is an efficient and accurate method of radiographic analysis that can be applied to detect and potentially predict clinicopathological data and prognostic determinants. Clinically, we demonstrate that NSCLC is associated with significant peritumoral edema and that topographical factors may be associated with increased extent of edema.