SDPS-09 MIXED RESPONSE OF BRAIN METASTASES TREATED WITH OSIMERTINIB PREDICTS INFERIOR SURVIVAL OUTCOMES: AN ARTIFICIAL INTELLIGENCE-BASED LESION-LEVEL ASSESSMENT FOR CRANIAL CONTROL OF EGFRMUTANT NONSMALL CELL LUNG CANCER

Osimertinib has been found to be effective for intracranial control in patients with metastatic EGFR-mutant non-small cell lung cancer (NSCLC). However, the lesion-level response of brain metastases to osimertinib has not been thoroughly evaluated. This retrospective study included 73 patients with EGFR-mutant NSCLC and brain metastases who were treated with osimertinib at a single institution from 2016 to 2021. Patients with the leptomeningeal disease were excluded. The study used an FDA-approved brain tumor management artificial intelligence (AI) platform, VBrain, to identify, track, and measure brain metastases on the baseline and follow-up MRI brain scans. Mixed response (MiR) was defined as the occurrence of progressive or new lesions along with synchronous responsive shrinking intracranial lesions at the first follow-up scan. K-means grouping was used to partition patients into two clusters according to their response heterogeneity. With a median follow-up of 23.8 months, a high MiR score (higher than 103%, n=25) was associated with worse cranial-progression-free survival (3.2 vs. 17.9 months, p<0.0001) and significantly inferior overall survival (12.4 vs. 30.1 months, HR=2.35, p=0.016). This poses a similar negative impact on the survivals of 18 patients with pure RANO-BM progression (HR=2.52, p=0.025). Eight patients had the highest MiR score (MiR(max)), in whom synchronous new lesions and completely responsive tumors were observed. In a multivariate Cox proportional-hazards regression involving performance status, extracranial failure, tumor volume, lesion number, and RANO-BM classification as variables, MiR(max) remained an independent prognosticator for inferior survival (HR=5.20, 95%CI; 2.21–12.23, p=0.002). Our study demonstrates that MiR in brain metastases from EGFR-mutant NSCLC treated with osimertinib is associated with inferior survival outcomes and a higher risk of local progression. Lesion-level response assessment using AI may provide important prognostic information and aid in treatment decision-making for these patients.