Predictors of overall survival in patients with brain metastases from HER2+ breast cancer.

Abstract

2037 Background: Predictors of overall survival (OS) after brain metastasis (BM) in HER2-positive breast cancer (BC) are not well characterized. This study aimed to identify clinical and imaging-derived (radiomic) features that predict OS and develop a combined model for better prognostic performance. Methods: Our retrospective study analyzed 289 patients initially diagnosed with non-metastatic HER2-positive BC who later developed BM. We used 25 clinical characteristics and 12 treatment parameters to develop a Clinical model. We developed an Imaging model using a subset of 120 patients, who possessed evaluable pre-treatment brain MRI for delineating tumor segmentations on all brain metastatic lesions. We extracted 1078 radiomic features from each tumor segmentation using PyRadiomics, generating 8 feature sets based on 2 segmentation strategies (largest tumor per patient versus all tumors combined) and 4 tumor feature types (entire tumor, solid component, necrotic component, combined solid and necrotic features with statistical transformations). Morphological features, including lesion number, total size/volume, and necrotic-to-solid ratios, were also incorporated, along with tumor intracranial location. Cox proportional hazards regression model with Coxnet, integrating LASSO and Elastic Net regularization, was used to predict OS. For fair comparison, we randomly selected 30% (n = 31) of the smallest subset (n = 103, largest brain metastasis with both necrotic and solid components), all of which overlap with other model subsets, as validation cohort. Three model types—Clinical, Imaging and Combined—were compared using the concordance index (C-index) to assess performance based on validation cohort. Results: Clinical model, built on the whole cohort (286 women, 3 men; mean age 54.52 ± 12.79 years), identified 3 predictors of OS. Imaging model, built on a subset of 120 patients with brain MRI data, identified a radiomic signature (RS) consisting of 4 radiomic features most predictive of OS. Using the same subset, the Combined model (C-index: 0.728 [95% CI: 0.590–0.855]) outperformed Clinical (C-index: 0.62 [95% CI: 0.44–0.78]) and Imaging (C-index: 0.62 [95% CI: 0.46–0.77]) models in the held-out validation cohort (n = 31). Significant features associated with increased mortality risk in the Combined model included a higher RS, absence of tucatinib treatment for the primary BC prior to BM development, elevated Ki-67 expression, Black race, higher N stage, and brainstem metastases. Among these factors, RS, with the largest absolute coefficient in the Combined model (0.38), emerged as the most important predictor of OS (hazard ratio: 20.03 [95% CI: 4.92–81.48], p < 0.005). Conclusions: A distinct RS from brain MRI is the strongest predictor of OS in patients with BM from HER2-positive BC, surpassing clinical factors. RS may refine risk stratification and guide treatment or clinical trial prioritization.