Bathla, Girish

Abstract BACKGROUND Neurofibromatosis type 2-related schwannomatosis (NF2) is an autosomal dominant tumor-predisposition syndrome characterized by bilateral vestibular schwannomas (VS) as well as other nervous system tumors. Management decisions for NF2-related VS are typically predicated on tumor size, associated symptoms, patient age, and comorbidities. Observation, surgical resection, and stereotactic radiosurgery can be viable options. In patients with VS associated with NF2, vascular endothelial growth factor A (VEGF-A) inhibitor, bevacizumab, is a systemic treatment option. In this single institution study, we review our clinical experience in patients with NF2 who received bevacizumab for VS and evaluate the effect of bevacizumab treatment on VS growth and symptom progression. MATERIAL AND METHODS This is a retrospective analysis of patients seen at the Mayo Clinic Rochester Multidisciplinary NF2 Clinic. Three-dimensional volumetric data for VS were measured using manual segmentation with Visage PACS software under the direct supervision of a board-certified neuroradiologist. RESULTS Out of 76 patients with NF2 evaluated in the past 3 years, we identified 19 that received treatment with bevacizumab. Median age of NF2 diagnosis was 19 years (range 0.5-61) with a median NF2 phenotype severity score of 2 (range 1-3). Thirteen of these patients have discontinued bevacizumab, accounting for a median duration of 12.2 months (range 3.3-88 months) of bevacizumab treatment. Bevacizumab was discontinued due to hypertension (n=5), proteinuria and renal dysfunction (n=3), tumor progression (n=2), fatigue or other intolerance (n=2), or non-compliance (n=1). The remaining 6 patients are currently receiving bevacizumab treatment for a median duration of 9.4 months as of March 2023 (range 3.7-48.3 months). Fifteen patients had brain MRI data available to evaluate VS change during bevacizumab treatment, with partial response observed in 5 patients, stable disease in 8, and progression in 2. Ten of the 13 patients who stopped bevacizumab had MRI data to evaluate VS change following treatment termination. Within 6 months of bevacizumab discontinuation, 6 patients had rebound growth of their VS between 21-95% from their previous tumor volume, while 4 did not. Three patients with rebound growth went on to have surgery or irradiation for VS management. CONCLUSION Our single institution experience confirms that bevacizumab can slow progression of VS and symptoms associated with VS growth. However, we note that there is the potential for rapid VS growth following bevacizumab discontinuation, for which we propose heightened surveillance imaging and symptom monitoring for at least 6 months upon stopping anti-VEGF therapy.

Introduction Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging–derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS). Methodology A retrospective, institutional review board–approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models. Results For all patients combined, the clinical feature–derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case. Conclusions When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature–derived model for predicting PFS.