Biomarkers that predict neurologic death may allow for personalization of therapy for high-risk brain metastases patients. Patients with NSCLC who underwent comprehensive genomic profiling were identified in an institutional database. Neurologic death was determined by medical record review. Proportional hazards regression models considering non-neurologic death as a competing risk were used to identify mutations statistically associated with the occurrence of neurologic death (p < 0.1) and to create a risk scoring system for neurologic death. A competing risk proportional hazards regression model with non-neurologic death as a competing risk was used to assess the association between the risk score and neurologic death, and to calculate hazard ratios predicting neurologic death between risk groups. 307 patients were included in the primary analysis and 213 in a cohort of patients with brain metastases. Risk scores were constructed in both populations. Patients with higher risk scores had an increased risk of neurologic death when compared to those in the low-risk group, with respective HRs of 3.76 for the entire cohort and 2.87 for the brain metastasis cohort per unit increase in the risk score. When dividing the risk score into three groups, the cumulative incidence of neurologic death in high, moderate, and low risk groups was 49.0

CONTEXT:Single-fraction 8 Gy palliative radiotherapy (RT) is a standard regimen for the relief of painful bone metastases. However, the delivery of single-fraction RT may result in higher retreatment rates compared to longer courses. OBJECTIVES:To compare 6‑month retreatment rates after 8 Gy x 1 versus 8 Gy x 2 and evaluate pain response, quality of life, and adverse events (AEs). METHODS:In this prospective, randomized, multi-center phase 2 trial, adults with painful bone metastases were randomized to 8 Gy x 1 (arm 1) or 8 Gy x 2 (arm 2). The primary endpoint was the 6-month cumulative incidence of retreatment, with death as a competing risk. Secondary endpoints included pain response, adverse events, and quality of life using multiple patient-reported outcome measures. RESULTS:A total of 102 patients were randomized and treated (51 per arm). The 6-month retreatment rates were 11.5% in arm 1 v. 10.3% in arm 2 (p = 1.00). The cause-specific hazard ratio of retreatment in arm 2 (v. arm 1) was 0.87 (p = .83). Rates of death without retreatment at 6-months were 23.3% and 29.4%, respectively. Pain response rates at 3 months were similar between arms: 55% v. 72% in arms 1 v. 2, respectively (p = .20). No substantial differences in QOL were observed between groups. Grade 2+ AEs occurred in 13.7% and 15.7% (p = .78). CONCLUSION:No differences were observed in retreatment rates, pain response, or QOL between 8 Gy x 1 and 8 Gy x 2. Single-fraction palliative RT remains a standard for patients with painful bone metastases.

BACKGROUND AND PURPOSE:Rib fracture is a recognized clinical complication in medically inoperable patients with non-small cell lung cancer (NSCLC) undergoing stereotactic body radiotherapy (SBRT), leading to diminished quality of life and delayed recovery. There remains an unmet need for reliable tools to predict rib fracture risk to support individualized prognosis. This study aimed to develop and validate a deep learning model for predicting post-SBRT rib fractures using time-series CT radiomics. MATERIAL AND METHODS:This retrospective study collected CT scans from three timepoints in 67 NSCLC patients, comprising over 1600 individual ribs. We proposed a novel Knowledge-aware Temporal Mixture of Experts (KA-TMoE) model that integrates longitudinal CT radiomics with radiomic grouping knowledge to estimate fracture risk at the rib level. Model performance and interpretability were evaluated. RESULTS:The KA-TMoE model demonstrated favorable predictive performance in the validation cohort, achieving an area under the receiver operating characteristic curve (AUC) of 0.792. Exploratory DeLong testing was generally consistent with the observed performance differences between KA-TMoE and the ablation variants, suggesting that both longitudinal information and radiomics-grouping knowledge contributed to model performance. Mann-Whitney U tests demonstrated significant differences in model output distributions across cohorts. Time-to-event analysis showed that the model-predicted high-risk group had a higher risk of fracture than the low-risk group (hazard ratio = 10.82; p < 0.001). Multivariable logistic analysis showed that the KA-TMoE output remained independently associated with fracture risk in the validation cohort (odds ratio = 12.05; p = 0.002). Decision curve analysis demonstrated potential net benefit across clinically relevant thresholds. Features from all three timepoints contributed to the model's decision-making, highlighting the importance of temporal information. CONCLUSION:KA-TMoE showed potential as a preliminary rib-level risk-stratification framework for predicting post-SBRT rib fractures in NSCLC patients. It may support earlier personalized risk stratification, closer surveillance, and timely supportive evaluation.

Background. To examine the feasibility of adding ramipril for prevention of cognitive decline to chemoradiation treatment of glioblastoma (GBM). Methods. This prospective single-arm study (WF-1801) coordinated by the Wake Forest NCI Community Oncology Research Program Research Base (UG1CA189824) assessed feasibility, tolerability, and potential efficacy of ramipril to prevent treatment-induced cognitive decline in patients with GBM. Inclusion criteria and chemoradiotherapeutic paradigms were mirrored to the standard arm of NRG/RTOG 0825, such that cognitive outcomes could be compared. All patients were treated with ramipril during chemoradiation and for 4 weeks after completion of radiotherapy (RT). Major outcomes were retention and the Clinical Trial Battery Composite (CTB COMP) score of the cognitive tests. Results were compared to the corresponding outcomes from NRG/RTOG 0825. Results. A total of 75 participants were accrued between March 25, 2019 and November 14, 2023: median age was 63 years. The NRG/RTOG 0825 cohort was younger (median 57 years, P < .0001). Overall retention rate at 1-month post-RT (defined as compliant with 75% of doses and completion of cognitive testing) was 48% (1-sided 95% CI: 38%-100%); 61% of patients completed more than 75% of doses and 57% had a CTB COMP score through week 10. The median (range) change in the CTB COMP score at 1 month after RT completion was 0.01 (-82.6, 13.0) versus NRG/RTOG 0825 median of 0.10 (-48.2, 8.6); P = .49. Conclusions. The ramipril intervention did not meet the prespecified feasibility endpoint, neither did the cognitive scores differ significantly from the NRG/RTOG 0925 control group. We expect other agents will be the focus of future cytoprotective trials.

Abstract Background and purpose: Rib fracture is a recognized clinical complication in medically inoperable patients with non-small cell lung cancer (NSCLC) undergoing stereotactic body radiotherapy (SBRT), leading to diminished quality of life and delayed recovery. This study aimed to develop and validate a deep learning model for predicting post-radiotherapy rib fracture using time-series CT radiomics. Material and methods: This study retrospectively collected CT scans from 67 NSCLC patients, comprising 1,605 individual ribs as separate instances. We proposed a novel Knowledge-aware Temporal Mixture of Experts (KA-TMoE) model that integrates radiomics from sequential CT scans to estimate fracture risk for each rib. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, and F1 score. Model interpretability was achieved using SHapley Additive exPlanations analysis, which attributed predictive value to each input feature. Results: The KA-TMoE model demonstrated strong predictive performance, achieving favorable AUC in the validation cohort (0.792). The DeLong test confirmed statistically significant improvements over ablation variants, underscoring the importance of integrating temporal data and domain knowledge. High sensitivity (0.85) and specificity (0.78) reflected a well-balanced trade-off, surpassing alternative approaches. Whitney U tests further supported its robustness, which showed significant differences in output distributions across cohorts. Among the top 20 most influential features, half originated from three-month postoperative radiomics, emphasizing the critical role of temporal information. Conclusion: The KA-TMoE model provides a robust, accurate framework for predicting rib fractures after SBRT in NSCLC patients. Its predictive power enables personalized risk assessment, better patient management, and optimized clinical prognosis. Citation Format: Yijun Chen, Michael Farris, Ariel Choi, Nga Thi Thanh Nguyen, Amanda Goetz, Corbin A. Helis, Fei Xing, Liang Liu, Qing Lyu, Christopher T. Whitlow, Christina K. Cramer, Michael D. Chan, Dan Bourland, Michael T. Munley, Jeffrey S Willey, Yuming Jiang. Time-series deep learning radiomics for predicting post-radiotherapy rib fractures in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3732.

Objective Because brain metastases are biologically heterogeneous, understanding how specific mutation types in conjunction with the cancer’s tissue of origin respond to treatment can help inform patient outcomes and potential treatment strategies. Gastro-intestinal brain metastases are less common, and their outcomes are not well documented in the current literature, which this research article hopes to ameliorate. Methods In this study, we assessed the clinical outcomes of 44 patients with brain metastases from esophageal primaries, either HER2+ or non-HER2+ mutation types, treated between 2001 to 2024. Using a single-institution retrospective database, we assessed the clinical outcomes of local control, regional brain failure, and overall survival. Results We found that non-HER2+ patients had lower brain metastasis velocity (BMV) and distant brain failure rate. HER2+ primaries spread earlier to the brain but had a longer 2 years survival rate. Conclusion Non-HER2+ patients may be a population where a single application of stereotactic radiosurgery (SRS) represents a cost-effective option, whereas the long-term toxicity implications may be higher for HER2+ patients.

BACKGROUND:Brain metastasis (BM) is a high-mortality complication occurring in 20-40% of cancer patients. While Gamma Knife (GK) radiosurgery is a primary treatment, individualized prognostic prediction remains limited. This study develops and validates a multimodal deep-learning framework for overall survival risk stratification after GK. PATIENTS AND METHODS:This multicenter retrospective study included 875 patients across three centers. A mask-guided multi-scale encoder was applied to extract MRI features. The proposed model integrated full MRI, grid-based MRI patches, radiomics, and consistently available clinical variables to generate a patient-level log-risk score for overall survival. Performance was assessed via time-dependent AUC, C-index, and Decision Curve Analysis (DCA). RESULTS:The model achieved 1-year AUCs of 0.870 (Training), 0.755 (Internal Val), 0.740 (External Val 1), and 0.788 (External Val 2). C-indices remained moderate across validation cohorts (0.655, 0.653, and 0.649). Multivariable Cox regression showed that the model-derived risk score was independently associated with overall survival across all cohorts. Using a training-derived exploratory threshold of 0.17, the model stratified patients into high- and low-risk groups with significant differences in overall survival across all cohorts. DCA suggested potential net benefit at 12 months. CONCLUSION:The proposed multimodal model showed consistent but moderate discrimination for overall survival stratification in BM patients. By integrating multimodal data, the framework may provide incremental prognostic information for post-GK risk stratification. Further recalibration, incorporation of comprehensive clinical variables, and prospective validation are warranted before clinical implementation.

Background/Objectives: No prior studies have attempted to identify a biomarker for initial brain metastasis velocity (iBMV), with limited studies attempting to correlate genomic data with the development of brain metastases. Methods: Patients with non-small-cell lung cancer (NSCLC) who underwent next-generation sequencing (NGS) were identified in our departmental database. iBMV was calculated by dividing the number of BMs by the interval of time between primary cancer and BM diagnosis. Two-sample t-testing was used to identify mutations statistically associated with iBMV (p < 0.1). A value of +1 was assigned to each mutation with a positive association (“deleterious genes”), and a value of −1 to each with an inverse association (“protective genes”). The sum of these values was calculated to define iBMV risk scores of −1, 0 and 1. Pearson correlation test was used to determine the association between iBMV risk score and calculated iBMV, and a competing risk analysis assessed for death as a competing risk to the development of BMs. Results: A total of 312 patients were included in the analysis, 218 of whom (70%) developed brain metastases. “Deleterious genes” included ARID1A, BRAF, CDK4, GNAQ, MLH1, MSH6, PALB2, RAD51D, RB1 and TSC1; “protective genes” included ARAF, IDH1, MYC, and PTPN11. iBMV risk scores of 1, 0 and −1, predicted an 88%, 61% and 65% likelihood of developing a BM (p < 0.01). A competing risk analysis found a significant association between iBMV risk scores of 1 vs. 0 and 1 vs. −1, and the likelihood of developing a BM using death as a competing risk. Overall survival (OS) at 1 and 2 years for patients with iBMV risk scores of 1, 0 and −1 was 72% vs. 84% vs. 85% and 46% vs. 69% vs. 70% (p < 0.02). Conclusions: Development of a genomic signature for iBMV via non-invasive liquid biopsy appears feasible in NSCLC patients. Patients with a positive iBMV risk score were more likely to develop brain metastases. Validation of this signature could lead to a biomarker with the potential to guide treatment recommendations and surveillance schedules.

Stereotactic radiosurgery (SRS) has been used to manage patients with intracranial meningioma with contraindications to resection. Limitations to SRS traditionally include tumors > 3 cm due to the risk of posttreatment toxicity. Hypofractionated SRS (hSRS) has been proposed as an alternative for tumors exceeding volume constraints for single-fraction SRS, although how hypofractionation affects the volume versus toxicity relationship has not been reported. Thus, the authors conducted a single-institution retrospective analysis of the medical records of patients receiving single-fraction SRS or multifraction hSRS for large (> 2 cm) meningiomas to assess the effect of hypofractionation on the likelihood of posttreatment toxicity. Patients were identified using the Wake Forest University Department of Radiation Oncology prospectively administered Gamma Knife database. Patients were included if they had single-fraction SRS or multifraction hSRS for a diagnosis of meningioma that was > 2 cm. Analysis was limited to tumor volumes between 2.7 and 49.3 cm3, the overlapping range shared by those undergoing hSRS or SRS. Electronic medical records were used to determine patient and tumor characteristics and clinical outcomes. A total of 121 SRS cases with a median dose of 12 Gy and 51 hSRS cases with a median dose of 20 Gy with tumor volumes between 2.7 and 49.3 cm3 were identified and included in the analysis. The probabilities of freedom from local failure at 1, 3, and 5 years were 87.0%, 79.0%, and 63.6%, respectively, for patients receiving single-fraction SRS and 96.0%, 91.0%, and 91.0%, respectively, for patients receiving multifraction hSRS. The probabilities of overall survival at 1, 3, and 5 years were 97.5%, 79.7%, and 72.6%, respectively, for patients receiving single-fraction SRS and 85.5%, 80.9%, and 76.4%, respectively, for patients receiving multifraction hSRS. Eighteen (14.9%) of 121 patients receiving single-fraction SRS experienced Common Terminology Criteria for Adverse Events (CTCAE) grade ≥ 2 toxicity, and 12 (23.5%) of 51 patients receiving multifraction hSRS experienced CTCAE grade ≥ 2 toxicity. When controlling for tumor volume, despite higher treatment doses in the hSRS group relative to the SRS group, posttreatment toxicity was not significantly different between the groups, and freedom from local failure was improved in the hSRS group. For patients with larger meningiomas, multifraction hSRS may help to limit the risk of posttreatment edema and toxicity, while maintaining acceptable freedom from local failure.

This study aims to apply an ensemble model integrating MRI-based radiomics and clinical information as a reliable tool for precisely predicting radiation necrosis, a severe consequence of radiation therapy for brain metastases that requires accurate early prediction to improve patient management. We retrospectively collected and analyzed MRI images and clinical information from 209 stereotactic radiosurgery sessions involving 130 patients with brain metastasis. Radiomic features were extracted from MRI using PyRadiomics and selected via L2 regularization and coefficient analysis. An ensemble model integrating gradient boosting, random forest, decision tree, and support vector machine as base regressors was developed using a soft voting approach to generate the final prediction of the likelihood of necrosis. Performance was assessed and compared with other machine-learning algorithms using metrics including the area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) analyses were applied to explain the model's prediction. The soft-voting ensemble model demonstrated strong performance in the validation cohort, achieving the highest AUC of 0.873 (95%CI: 0.672 - 1.000). It consistently outperformed individual models and the stacking ensemble model, exhibiting superior accuracy, generalizability, and reliability in predicting radiation necrosis. Both univariate and multivariate logistic regression analyses were performed and confirmed the model as the strongest predictor. SHAP and LIME analyses were employed to interpret the predictive model for radiation necrosis, identifying metastasis volume and the radiomic feature, log-sigma-1-mm_glcd_ldmn, as key predictors. Both analyses highlighted similar significant factors, enhancing the understanding of prediction dynamics. This MRI-based radiomics ensemble model exhibited high accuracy and robustness in predicting radiation necrosis. It has the potential to serve as a novel and valuable tool to facilitate radiotherapy for patients with brain metastasis. Yijun Chen, Corbin A. Helis, Christina K. Cramer, Michael T. Munley, Fei Xing, Qing Lyu, Christopher T. Whitlow, Jeffrey Willey, Michael D. Chan, Yuming Jiang. MRI-based radiomics ensemble model for predicting radiation necrosis in brain metastasis patients treated with stereotactic radiosurgery and immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4676.

Cancer-related cognitive impairment is a broad term encompassing subtle cognitive problems to more severe impairment. The severity of this impairment is influenced by host, disease, and treatment factors, and the impairment affects patients before, during, and following cancer treatment. The National Cancer Institute (NCI) Symptom Management and Health-Related Quality of Life Steering Committee (SxQoL SC) convened a clinical trial planning meeting to review the state of the science on cancer-related cognitive impairment and develop phase II/III intervention trials aimed at improving cognitive function in cancer survivors with non-central nervous system disease and longitudinal studies to understand the trajectory of cognitive impairment and contributing factors. Participants included experts in the field of cancer-related cognitive impairment, members of the SxQoL SC, patient advocates, representatives from all 7 NCI Community Oncology Research Program research bases, and the NCI. Presentations focused on the following topics: measurement, lessons learned from pediatric and geriatric oncology, biomarker and mechanism endpoints, longitudinal study designs, and pharmacological and behavioral intervention trials. Panel discussions provided guidance on priority cognitive assessments, considerations for remote assessments, inclusion of relevant biomarkers, and strategies for ensuring broad inclusion criteria. Three clinical trial planning meeting working groups (longitudinal studies as well as pharmacological and behavioral intervention trials) convened for 1 year to discuss and report on top priorities and to design studies. The meeting experts concluded that sufficient data exist to advance phase II/III trials using selected pharmacological and behavioral interventions for the treatment of cancer-related cognitive impairment in the non-central nervous system setting, with recommendations included herein.

Objective:We previously presented a genomic signature predictive of both oligometastatic disease state and oligoprogression in patients with brain metastases. We sought to validate this oligometastatic genomic signature using an independent dataset of patients without brain metastases. Methods:Patients with non-small cell lung cancer (NSCLC) and liquid biopsy-based genomic profiling (Guardant Health) were identified in our departmental database. Those with brain metastases were excluded. Patients were assigned an oligometastatic risk score based on the previously derived genomic signature. Oligometastatic disease was defined as ≤5 metastases without diffuse single-organ involvement. For oligometastatic patients, we performed a competing risk analysis for the cumulative incidence of oligoprogression. Cox regression was used to determine association between oligometastatic risk score and oligoprogression. Results:A total of 225 patients met inclusion criteria for this validation dataset. 158 patients (70%) had oligometastatic disease. Patients with positive and neutral/negative risk scores had a 70 vs 48% likelihood of oligometastatic disease, respectively (p = 0.03 from Fisher's exact test). Patients with positive risk scores more frequently experienced oligoprogression with a hazard ratio of 1.72 (p = 0.11). Overall survival for patients with positive and neutral/negative risk scores was 92% vs 79% at 6 months; 46% vs 60% at 12 months; 23% vs 34% at 24 months (p = 0.25). Conclusion:In this independent dataset, our genomic signature predicted oligometastatic disease state and showed a trend towards prediction of oligoprogression. With further study, our findings may suggest a biomarker with future potential to direct local therapies in oligometastatic NSCLC patients.

OBJECTIVE:Gamma Knife radiosurgery (GKRS) is a treatment option for refractory trigeminal neuralgia (TN). However, there is a paucity of data regarding the effectiveness of GKRS for relapsing TN following microvascular decompression (MVD). The aim of this study was to characterize the response rate, complications, pain relief durability, and predictors of pain relapse for salvage GKRS following MVD for TN. METHODS:A retrospective study of all patients who received GKRS for Burchiel type 1 TN (TN1) or type 2 TN (TN2) pain at Wake Forest University School of Medicine was conducted. Pain was measured using the Barrow Neurological Institute (BNI) pain intensity score. After an initial pain response of BNI scores I-III, a BNI score of IV or V constituted relapse. Durability of pain relief was characterized using the Kaplan-Meier estimator. Predictors of relapse were investigated using Cox regression models. Statistical significance was set at p < 0.05. RESULTS:Of 2065 patients with TN1 or TN2, 59 had GKRS post-MVD. Forty-nine (83.1%) of these patients experienced a BNI pain score of I-III at the first follow-up post-GKRS. The median time to relapse was 1.75 years; freedom rates from relapse were 77%, 45.9%, and 30.7% at 1, 2, and 5 years, respectively. Radiofrequency ablation prior to MVD significantly decreased the likelihood of an initial response to salvage GKRS (Fisher's exact test, p = 0.02). After controlling for baseline and clinical characteristics, facial numbness significantly decreased the likelihood of pain relapse (Cox regression, HR 0.15, 95% CI 0.03-0.73; p = 0.01). Conversely, a worse initial pain response significantly increased the likelihood of pain relapse (Cox regression, HR 3.64, 95% CI 1.02-12.95; p = 0.04). Pain relapse within 24 months of the original MVD did not predict durability of pain relief following salvage GKRS (Cox regression, HR 0.94, 95% CI 0.40-2.22; p = 0.89). The overall toxicity rate of salvage GKRS was 35.6%. CONCLUSIONS:Salvage GKRS presents an effective, noninvasive option for recurring TN after MVD, with a comparable response rate to primary GKRS or MVD, and a favorable complications profile relative to salvage MVD. Patients with postoperative facial numbness and a better initial pain response may experience more durable pain relief following salvage GKRS.

Purpose/objective(s)Biomarkers for extracranial oligometastatic disease remain elusive and few studies have attempted to correlate genomic data to the presence of true oligometastatic disease.MethodsPatients with non-small cell lung cancer (NSCLC) and brain metastases were identified in our departmental database. Electronic medical records were used to identify patients for whom liquid biopsy-based comprehensive genomic profiling (Guardant Health) was available. Extracranial oligometastatic disease was defined as patients having ≤5 non-brain metastases without diffuse involvement of a single organ. Widespread disease was any spread beyond oligometastatic. Fisher’s exact tests were used to screen for mutations statistically associated (p<0.1) with either oligometastatic or widespread extracranial disease. A risk score for the likelihood of oligometastatic disease was generated and correlated to the likelihood of having oligometastatic disease vs widespread disease. For oligometastatic patients, a competing risk analysis was done to assess for cumulative incidence of oligometastatic progression. Cox regression was used to determine association between oligometastatic risk score and oligoprogression.Results130 patients met study criteria and were included in the analysis. 51 patients (39%) had extracranial oligometastatic disease. Genetic mutations included in the Guardant panel that were associated (p<0.1) with the presence of oligometastatic disease included ATM, JAK2, MAP2K2, and NTRK1, while ARID1A and CCNE1 were associated with widespread disease. Patients with a positive, neutral and negative risk score for oligometastatic disease had a 78%, 41% and 11.5% likelihood of having oligometastatic disease, respectively (p<0.0001). Overall survival for patients with positive, neutral and negative risk scores for oligometastatic disease was 86% vs 82% vs 64% at 6 months (p=0.2). Oligometastatic risk score was significantly associated with the likelihood of oligoprogression based on the Wald chi-square test. Patients with positive, neutral and negative risk scores for oligometastatic disease had a cumulative incidence of oligometastatic progression of 77% vs 35% vs 33% at 6 months (p=0.03).ConclusionsElucidation of a genomic signature for extracranial oligometastatic disease derived from non-invasive liquid biopsy appears feasible for NSCLC patients. Patients with this signature exhibited higher rates of early oligoprogression. External validation could lead to a biomarker that has the potential to direct local therapies in oligometastatic patients.

Abstract BACKGROUND Patients with glioblastoma (GBM) experience cognitive decline after chemoradiation. Radiation therapy (RT) to the brain creates chronic inflammation believed to contribute to cognitive decline. Ramipril counteracts neuroinflammatory changes in pre-clinical models when given during RT. METHODS This prospective single arm study (WF-1801) coordinated by the Wake Forest NCI Community Oncology Research Program (NCORP) Research Base assessed feasibility, tolerability, and potential efficacy of using ramipril in patients with GBM. Eligibility and treatment paradigms mirrored NRG/RTOG 0825 allowing for outcomes from the placebo arm to be used for comparison. Participants took ramipril during 6 weeks of chemoradiation with temozolomide and for 3 months after RT. Patients completed cognitive testing (HVLT-R, TMT, and COWA) and patient-reported outcomes at baseline, RT completion, and 1 and 4 months after RT. Co-primary endpoints were retention rate (compliance with 75% doses and completion of cognitive testing) and change in cognitive composite score 1 month after RT. RESULTS 75 participants were accrued between 3/25/2019 and 11/14/23: 65% were male, 89% White, median age 63 years. The NRG/RTOG 0825 placebo cohort (n=247) was younger (median 57, p<0.0001) with more White participants (95%, p=0.04). The overall retention rate in WF-1801 was 48% (one-sided 95% CI: 38%-100%); 61% completed >=75% of doses; 57% completed cognitive testing at all time points. The median (range) change in cognitive composite score 1 month after RT was 0.01 (-82.6, 13.0) reflecting an improvement from baseline. However, this was not significantly different from the NRG/RTOG 0825 placebo group median of 0.10 (-48.2, 8.6); p=0.49. CONCLUSION The overall retention rate fell short of our pre-specified endpoint of > 65%; however, medication compliance was high and well-tolerated in retained patients. Future investigations could identify patient subsets who may benefit more from concurrent ramipril. WF-1801 (NCT03475186) funded by UG1CA189824. NRG/RTOG-0825 (NCT00884741) funded by U10CA180868 and U10CA021661.

OBJECTIVE:Opportunity exists for improved local control rates of grade 2 meningiomas that recur despite maximal surgical resection and adjuvant fractionated radiotherapy (RT). We describe a dose escalation strategy of increasing the total tumor radiation dose by adding a stereotactic radiosurgery (SRS) boost targeting gross disease to fractionated RT. METHODS:A single-institution retrospective cohort of patients from 2009-2023 with grade 2 meningioma treated with surgical resection, fractionated RT, and SRS boost were evaluated for baseline characteristics, local disease control, and adverse events (AE). RESULTS:Fourteen meningioma patients were included. Ten patients (71.4%) underwent radiosurgery at initial diagnosis, while 4 patients (28.6%) were treated for recurrent disease. The median fractionated dose was 54 Gy, while the median dose for SRS was 7.5 Gy. Among the 13 patients with follow-up available, median follow-up was 34 months. Three patients (23%) had treatment failures; however, none occurred within the SRS volume and 2 thirds occurred in patients treated for recurrent disease. Eighteen-month progression-free survival was 92.3%, while 18-month overall survival was 100%. Most patients experienced no or mild AEs; however, 3 patients (23%) experienced a high-grade (Common Terminology Criteria for Adverse Events v5.0 grade ≥3) AE including radiation necrosis, seizures, and cognitive decline. CONCLUSIONS:We found 100% in-field local control at 3 years from an SRS boost to fractionated RT targeting gross disease with an acceptable toxicity profile, suggesting this may be an effective and improved adjuvant treatment strategy in patients with grade 2 meningioma.

OBJECTIVE:The objective of this study was to examine survival outcomes in 136 patients with renal cell carcinoma with metastases to the brain who were treated with radiation combined with immunotherapy or tyrosine kinase inhibitor compared to those who were treated with radiation therapy alone.METHODS:The Wake Forest Gamma Knife prospective database was searched for all patients with renal cell carcinoma brain metastases. Outcome measurements included overall survival, determined via the Kaplan-Meier Method, and cumulative incidence of local and distant failure, determined using the Fine Gray competing risks analysis with death as a competing risk for the 136 patients included.RESULTS:Overall survival for the entire population at 6 months, 12 months, and 24 months was 67%, 47% and 30%, respectively. For the TKI (non-immunotherapy-treated) population (n = 37), overall survival was 75%, 61%, and 40% at 6 months, 12 months, and 24 months, respectively. For the immunotherapy-treated population (n = 35), overall survival was 85%, 64%, and 50% at 6 months, 12 months, and 24 months, respectively. Overall survival was significantly increased for patients who received radiation with either immunotherapy or TKI (p < 0.0001).CONCLUSION:Prior series of patients with brain metastases of multiple histologies have demonstrated an improvement in the local efficacy of stereotactic radiosurgery when combined with systemic agents. We found that patients treated with targeted agents and patients treated with immunotherapy demonstrated a trend towards improvement over patients treated in the era prior to the advent of either classes of novel therapies.

To evaluate associations between smoking status, genetic alterations in primary glial neoplasms, and impact on survival.