Abstract
Patients with gliomas, particularly glioblastomas (GBM), have a higher risk of developing venous thromboembolism (VTE), correlating with overall survival (OS). Artificial intelligence (AI) approaches that employ VTE as a clinical feature in brain tumor patients is understudied due to the difficulty in analyzing electronic health records (EHR). Data expansion by creating a word lexicon for natural language processing (NLP) of free-text clinical reports will allow exposure of VTE for the classification of large-scale data sets, NLP, and AI. Patients with a pathologic diagnosis of GBM (2005-2021) were screened for the development of VTE based on radiology free-text reports (ultrasound (US) of extremities and Computed Topography-pulmonary angiogram (CT)). Kaplan-Meier survival analyses about overall survival (OS) and progression-free survival (PFS) were generated for VTE. 163 patients (mean age = 56.1 ± 12.1, 65% male) were included, 48 (29.4%) were screened for VTE following clinical suspicion on history or physical exam, and 15 (9.2%) were found to have a VTE. Screening methods were ultrasound 83.3% (40) or CT 13.9% (6), or both 4.6% (2). 28.6% (12) of US and 37.5% (3) of CT resulted in a positive VTE diagnosis. The words “partial”, “residual”, “complete”, “critical” or “clotted” when used as an “or” boolean statement applied to US and CT radiology reports identified ~93% of the patients with VTE. Patients with VTE had worse OS (median 14 vs. 19 months, p = .0189) and PFS (median 6 vs. 9 months, p = .0239) than patients without VTE, indicating underlying pathology associated with both prevalence of VTE and tumor burden. US and CT yield a similar percentage of positive VTE findings while employing different terms to characterize VTE. We confirm that patients with VTE have poorer outcomes and present a word combination that identifies patients with VTE in large-scale radiology report data.