Mishra, Mark V.

Abstract Purpose: The optimal salvage treatment for malignant glioma after prior radiotherapy (RT) is not well established. Proton therapy (PT) is an attractive treatment modality for reirradiation (ReRT) of recurrent disease; however, data are limited.Methods: The prospective, multi-institutional Proton Collaborative Group (PCG) registry was queried for patients with malignant glioma who previously received RT and underwent PT-ReRT between 7/2011-10/2018. Overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox proportional hazards regression analysis. Results: 109 consecutive patients were identified. Median follow-up was 11.2 months and median time interval (TI) from prior RT (median 59.3 Gy) to PT-ReRT (median 49.6 Gy) was 45.7 months. Primary histology was glioblastoma (n=62), oligodendroglioma (n=26), and astrocytoma (n=20). Median PFS and OS was 6.1 and 11.1 months, respectively. On UVA, improved PFS was associated with oligodendroglioma (HR 0.39) and astrocytoma (HR 0.55) histologies, WHO Grade III (HR 0.36), TI (HR 0.989), surgical resection (HR 0.39), and PT-ReRT dose ≥50 Gy (HR 0.59) (all p<0.03), while improved OS was associated with oligodendroglioma histology (HR 0.35), WHO grade III (HR 0.33), ECOG 0 (HR 0.47), TI (HR 0.985), and PT-ReRT dose ≥50 Gy (HR 0.58) (all p<0.04). Late Grade 3 toxicities occurred in 5.6% of patients with no attributable acute or late Grade 4-5 events.Conclusion: In the largest series of glioma PT-ReRT reported to date, retreatment appears well tolerated with acceptable outcomes and favorable toxicities compared to photon historical controls. Dose escalation achievable with PT may improve outcomes for well-selected patients.

60 Background: Mapping algorithms informing economic evaluations are often derived using baseline data from clinical trials. It is unclear if these algorithms can predict health utilities accurately in post-intervention data. Thus, this study examines the longitudinal predictive ability of mapping algorithms derived from baseline trial data and explores the factors associated with prediction errors. Methods: This methodological study utilized data from an international, multicenter, randomized controlled trial of patients with low-risk prostate cancer (PC), conducted by NRG Oncology (NCT00331773). In addition to patient demographic and clinical data, this study utilized PRO data collected at baseline and 6, 12 and 24 months post-intervention. The Expanded Prostate Cancer Index Composite (EPIC) questionnaire measures health-related quality-of-life (HRQoL) and has four domains (urinary, sexual, hormonal, and bowel) and two subdomains per domain (function and bother); EuroQOL-5D-3L (EQ5D) captures health utilities. Ordinary Least Squares (OLS) regression models were used to map EPIC scores to EQ5D utilities in the baseline data through 5-fold cross-validation. Predictive performance was tested in the post-intervention data; predicted and reported utilities were compared using t-tests, and the absolute prediction error was modeled using fixed effects, as a function of baseline demographic and clinical covariates, as well as observed and predicted EQ5D utilities. Results: A total of 267 (199) patients had complete EQ5D and EPIC domain (or subdomain) data at baseline and all subsequent assessments. In the EPIC domain sample, mean ± standard deviation observed EQ5D utility was 0.90±0.13 at baseline, 0.92±0.11 at 6 months, 0.90±0.13 at 12 months and 0.89±0.14 at 24 months. Mean absolute differences (MDs) between reported and predicted were lower for models using EPIC subdomain data compared to EPIC domain data, and generally decreased as the time of assessment increased. The mapping functions over-predicted utilities for patients in perfect health while the prediction errors were increasingly negative for lower reported EQ5D scores. According to the fixed effects model for EPIC domain data, lower observed and predicted baseline EQ5D scores, and time of assessment were significant predictors of the absolute prediction error; for EPIC subdomain data, lower observed and predicted baseline EQ5D scores, hormonal bother and function, and bowel function significantly predicted the absolute prediction error. Conclusions: This study is the first to demonstrate the longitudinal validity of EPIC questionnaire, and builds upon existing research on longitudinal validity of mapping functions. The low MDs in prediction errors in post-intervention data indicate that the mapping functions are sensitive to treatment effect, thereby increasing confidence in their use in economic evaluations in PC.

Abstract The incidence of brain metastasis is increasing as improvements in systemic therapy lead to increased survival. This provides new and challenging clinical decisions for patients who are trying to balance the risk of recurrence or progression with treatment-related side effects, and it requires appropriate management strategies from multidisciplinary teams. Improvements in prognostic assessment and systemic therapy with increasing activity in the brain allow for individualized care to better guide the use of local therapies and/or systemic therapy. Here, we review the current landscape of brain-directed therapy for the treatment of brain metastasis in the context of recent improved systemic treatment options. We also discuss emerging treatment strategies including targeted therapies for patients with actionable mutations, immunotherapy, modern whole-brain radiation therapy, radiosurgery, surgery, and clinical trials.

55 Background: To compare the predictive ability of health utility mapping algorithms derived using cross-sectional and longitudinal data specific to the Expanded Prostate Cancer Index Composite (EPIC). Methods: This mapping study utilized data from an international, multicenter, randomized controlled trial of patients with low-risk prostate cancer conducted by NRG Oncology (NCT00331773). Health-related quality-of-life (HRQoL) data were collected using EPIC, and health utilities were obtained using EuroQOL-5D (EQ5D) at baseline and 6, 12 and 24 months post-intervention. Data were split into an estimation sample (70%) and a validation sample (30%). Ordinary Least Squares (OLS) regression models were estimated using baseline cross-sectional data as well as pooled data from all assessment periods. Random effects (RE) specifications that explicitly model the longitudinal nature of the data were also estimated. Candidate models were selected based on root mean square error (RMSE). Results: A total of 196 (147) patients in the estimation sample had complete EQ5D and EPIC domain (subdomain) data at all time points. OLS models using combined data outperformed the counter-part RE models as well as OLS models using baseline data in the five-fold cross-validation. Addition of covariates to the models resulted in improved predictive ability. In the external validation, when only EPIC domain/ subdomain data are available, the OLS model using combined data predicted EQ5D utilities better than the counterpart RE model and OLS model using baseline data (RMSE=0.121108 & 0.111345). OLS model using baseline data outperformed other model types for algorithms with EPIC domains and demographics (RMSE=0.121757), while RE models outperformed the other two model types for algorithms with EPIC subdomains and demographic data, (0.112782) and for algorithms with EPIC domains/ subdomains, demographics, and clinical covariates (RMSE=0.123589 & 0.163093). Conclusions: While algorithms using pooled data outperformed other model types in internal validation, RE models showed better predictive ability in external validation for algorithms with covariates. Clinical trial information: NCT00331773.