The pervasive crisis of diminishing radiation therapy access for vulnerable populations in the United States, part 1: African-American patients

Introduction: African Americans experience the highest burden of cancer incidence and mortality in the United States and have been persistently less likely to receive interventional care, even when such care has been proven superior to conservative management by randomized controlled trials. The presence of disparities in access to radiation therapy (RT) for African American cancer patients has rarely been examined in an expansive fashion. Methods and materials: An extensive literature search was performed using the PubMed database to examine studies investigating disparities in RT access for African Americans. Results: A total of 55 studies were found, spanning 11 organ systems. Disparities in access to RT for African Americans were most prominently study in cancers of the breast (23 studies), prostate (7 studies), gynecologic system (5 studies), and hematologic system (5 studies). Disparities in RT access for African Americans were prevalent regardless of organ system studied and often occurred independently of socioeconomic status. Fifty of 55 studies (91%) involved analysis of a population-based database such as Surveillance, Epidemiology and End Result (SEER; 26 studies), SEER-Medicare (5 studies), National Cancer Database (3 studies), or a state tumor registry (13 studies). Conclusions: African Americans in the United States have diminished access to RT compared with Caucasian patients, independent of but often in concert with low socioeconomic status. These findings underscore the importance of finding systemic and systematic solutions to address these inequalities to reduce the barriers that patient race provides in receipt of optimal cancer care

Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy

Objective: We explore whether a knowledge–discovery approach building a Classification and Regression Tree (CART) prediction model for weight loss (WL) in head and neck cancer (HNC) patients treated with radiation therapy (RT) is feasible. Methods and materials: HNC patients from 2007 to 2015 were identified from a prospectively collected database Oncospace. Two prediction models at different time points were developed to predict weight loss ≥5 kg at 3 months post-RT by CART algorithm: (1) during RT planning using patient demographic, delineated dose data, planning target volume–organs at risk shape relationships data and (2) at the end of treatment (EOT) using additional on-treatment toxicities and quality of life data. Results: Among 391 patients identified, WL predictors during RT planning were International Classification of Diseases diagnosis; dose to masticatory and superior constrictor muscles, larynx, and parotid; and age. At EOT, patient-reported oral intake, diagnosis, N stage, nausea, pain, dose to larynx, parotid, and low-dose planning target volume–larynx distance were significant predictive factors. The area under the curve during RT and EOT was 0.773 and 0.821, respectively. Conclusions: We demonstrate the feasibility and potential value of an informatics infrastructure that has facilitated insight into the prediction of WL using the CART algorithm. The prediction accuracy significantly improved with the inclusion of additional treatment-related data and has the potential to be leveraged as a strategy to develop a learning health system

Genomic predictors of patterns of progression in glioblastoma and possible influences on radiation field design

We present a retrospective investigation of the role of genomics in the prediction of central versus marginal disease progression patterns for glioblastoma (GBM). Between August 2000 and May 2010, 41 patients with GBM and gene expression and methylation data available were treated with radiotherapy with or without concurrent temozolomide. Location of disease progression was categorized as within the high dose (60 Gy) or low dose (46 Gy) volume. Samples were grouped into previously described TCGA genomic groupings: Mesenchymal (m), classical (c), proneural (pn), and neural (n); and were also classified by MGMT-Methylation status and G-Cimp methylation phenotype. Genomic groupings and methylation status were investigated as a possible predictor of disease progression in the high dose region, progression in the low dose region, and time to progression. Based on TCGA category there was no difference in OS (p = 0.26), 60 Gy progression (PN: 71 %, N: 60 %, M: 89 %, C: 83 %, p = 0.19), 46 Gy progression (PN: 57 %, N: 40 %, M: 61 %, C: 50 %, p = 0.8) or time to progression (PN: 9 months, N:15 months, M: 9 months, C: 7 months, p = 0.58). MGMT methylation predicted for improved OS (median 25 vs. 13 months, p = 0.01), improved DFS (median 13 vs. 8 months, p = 0.007) and decreased 60 Gy (p = 0.003) and 46 Gy (p = 0.006) progression. There was a cohort of MGMT methylated patients with late marginal disease progression (4/22 patients, 18 %). TCGA groups demonstrated no difference in survival or progression patterns. MGMT methylation predicted for a statistically significant decrease in in-field and marginal disease progression. There was a cohort of MGMT methylated patients with late marginal progression. Validations of these findings would have implications that could affect radiation field size