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Abstract BACKGROUND Gamma Knife radiosurgery (GKRS; Elekta AB) remains a well-established treatment modality for vestibular schwannomas. Despite highly effective tumor control, further research is needed toward optimizing long-term functional outcomes. Whereas dose-rate effects may impact post-treatment toxicities given tissue dose-response relationships, potential effects remain largely unexplored. OBJECTIVE To evaluate treatment outcomes and potential dose-rate effects following definitive GKRS for vestibular schwannomas. METHODS We retrospectively reviewed 419 patients treated at our institution between 1998 and 2015, characterizing baseline demographics, pretreatment symptoms, and GKRS parameters. The cohort was divided into 2 dose-rate groups based on the median value (2.675 Gy/min). Outcomes included clinical tumor control, radiographic progression-free survival, serviceable hearing preservation, hearing loss, and facial nerve dysfunction (FND). Prognostic factors were assessed using Cox regression. RESULTS The study cohort included 227 patients with available follow-up. Following GKRS 2-yr and 4-yr clinical tumor control rates were 98% (95% CI: 95.6%-100%) and 96% (95% CI: 91.4%-99.6%), respectively. Among 177 patients with available radiographic follow-up, 2-yr and 4-yr radiographic progression-free survival rates were 97% (95% CI: 94.0%-100.0%) and 88% (95% CI: 81.2%-95.0%). The serviceable hearing preservation rate was 72.2% among patients with baseline Gardner-Robertson class I/II hearing and post-treatment audiological evaluations. Most patients experienced effective relief from prior headaches (94.7%), tinnitus (83.7%), balance issues (62.7%), FND (90.0%), and trigeminal nerve dysfunction (79.2%), but not hearing loss (1.0%). Whereas GKRS provided effective tumor control independently of dose rate, GKRS patients exposed to lower dose rates experienced significantly better freedom from post-treatment hearing loss and FND (P = .044). CONCLUSION Whereas GKRS provides excellent tumor control and effective symptomatic relief for vestibular schwannomas, dose-rate effects may impact post-treatment functional outcomes. Further research remains warranted.

Background/Objectives: Brain metastases (BrMs) are a common complication of non-small cell lung cancer (NSCLC), present in up to 50% of patients. While the treatment of BrMs requires a multidisciplinary approach with surgery, radiotherapy (RT), and systemic therapy, the advances in molecular sequencing have improved outcomes in patients with targetable mutations. With a push towards the molecular characterization of cancers, we evaluated the outcomes by treatment modality at our institution with respect to prioritizing RT and targeted therapies. Methods: We identified the patients with NSCLC BrMs treated with surgical resection. The primary endpoints were in-brain freedom from progression (FFP) and overall survival (OS). The secondary endpoint included index lesion recurrence. The tumor molecular profiles were reviewed. The outcomes were evaluated by treatment modality: surgery followed by adjuvant RT and/or adjuvant systemic therapy. Results: In total, 155/272 (57%) patients who received adjuvant therapy with adequate follow-up were included in this analysis. The patients treated with combination therapy vs. monotherapy had a median FFP time of 10.72 months vs. 5.38 months, respectively (p = 0.072). The patients of Hispanic/Latino vs. non-Hispanic/Latino descent had a statistically significant worse OS of 12.75 months vs. 53.15 months, respectively (p = 0.015). The patients who received multimodality therapy had a trend towards a reduction in index lesion recurrences (χ2 test, p = 0.063) with a statistically significant improvement in the patients receiving immunotherapy (χ2 test, p = 0.0018). Conclusions: We found that systemic therapy combined with RT may have an increasing role in delaying the time to progression; however, there was no statistically significant relationship between OS and treatment modality.

The unique cellular and molecular consequences of cytoplasmic damage caused by ionizing radiation were studied using a precision microbeam irradiator. Our results indicated that targeted cytoplasmic irradiation induced metabolic shift from an oxidative to glycolytic phenotype in human small airway epithelial cells (SAE). At 24 h postirradiation, there was an increase in the mRNA expression level of key glycolytic enzymes as well as lactate secretion in SAE cells. Using RNA-sequencing analysis to compare genes that were responsive to cytoplasmic versus nuclear irradiation, we found a glycolysis related gene, Pim-1, was significantly upregulated only in cytoplasmic irradiated SAE cells. Inhibition of Pim-1 activity using the selective pharmaceutic inhibitor Smi-4a significantly reduced the level of lactate production and glucose uptake after cytoplasmic irradiation. In addition, Pim-1 also inhibited AMPK activity, which is a well-characterized negative regulator of glycolysis. Distinct from the glycolysis induced by cytoplasmic irradiation, targeted nuclear irradiation also induced a transient and minimal increase in glycolysis that correlated with increased expression of Hif-1α. In an effort to explore the underline mechanism, we found that inhibition of mitochondria fission using the cell-permeable inhibitor mdivi-1 suppressed the induction of Pim-1, thus confirming Pim-1 upregulation as a downstream effect of mitochondrial dysfunction. Our data show and, for the first time, that cytoplasmic irradiation mediate expression level of Pim-1, which lead to glycolytic shift in SAE cells. Additionally, since glycolysis is frequently linked to cancer cell metabolism, our findings further suggest a role of cytoplasmic damage in promoting neoplastic changes.

For many decades, the management of cancer has utilized radiation therapy, which continues to evolve with technology to improve patient outcomes. However, despite the standardization of treatment plans and the establishment of best clinical practices based on prospective, randomized trials and adherence to National Comprehensive Cancer Network (NCCN) guidelines, the outcomes from radiation therapy are highly variable and dependent on a number of factors, including patient demographics, tumor characteristics/histology, and treatment parameters. In this study, we attempt to use available patient data and treatment parameters at the time of radiation therapy to predict future outcomes using artificial intelligence (AI). Six thousand five hundred ninety-five cases of patients who completed radiation treatment were selected retrospectively and used to train artificial neural networks (ANNs) and baseline models (i.e., logistic regression, random forest, support vector machines [SVMs], gradient boosting [XGBoost]) for binary classification of mortality at multiple time points ranging from six months to five years post-treatment. A hyperparameter grid search was used to identify the optimal network architecture for each time point, using sensitivity as the primary outcome metric. The median age was 75 years (range: 2-102 years). There were 63.8% females and 36.1% males. The results indicate that ANNs were able to successfully perform binary mortality prediction with an accuracy greater than random chance and greater sensitivity than baseline models used. The best-performing algorithm was the ANN, which achieved a sensitivity of 83.00% ± 4.89% for five-year mortality. The neural network was able to achieve higher sensitivity than Logistic Regression, SVM Random Forest, and XGBoost across all output target variables, demonstrating the utility of a neural network model for mortality prediction on the provided dataset.