A systematic review of tumor treating fields therapy for high-grade gliomas.

INTRODUCTION: Tumor treating fields (TTF) is a unique treatment modality that utilizes alternating electric fields to deliver therapy. Treatment effects have been assessed in patients with newly diagnosed and recurrent glioblastoma in clinical trials and retrospective studies. While the results of these studies led to FDA approval for both populations, a portion of the neuro-oncology and neurosurgery community remains skeptical of TTF. Thus, this review aims to systematically summarize and evaluate prior studies investigating the efficacy and safety of TTF in patients with high-grade gliomas. METHODS: A systematic review of the literature was performed according to PRISMA guidelines from database inception through February 2019. To be included, studies must have investigated the efficacy of TTF in adult high-grade glioma patients. RESULTS: In total, 852 studies were initially identified, 9 of which met final inclusion criteria. In total, 1191 patients were identified who received TTF. Included studies consisted of two pilot clinical trials, two randomized clinical trials, and five retrospective studies. In randomized clinical trials, TTF improved survival for newly diagnosed glioblastoma patients but not for recurrent glioblastoma patients. Adverse skin reactions were the primary adverse effect associated with TTF. CONCLUSION: While TTF has been evaluated for safety and efficacy in a number of studies, concerns remain regarding study design, quality of life, and cost of therapy. Further investigation is needed regarding the therapy, and ongoing trials are already underway to provide more data regarding therapy outcomes and interactions in combination regimens.

Predictive Model and Online Calculator for Discharge Disposition in Brain Tumor Patients.

BACKGROUND: In the era of value-based payment models, it is imperative for neurosurgeons to eliminate inefficiencies and provide high-quality care. Discharge disposition is a relevant consideration with clinical and economic ramifications in brain tumor patients. We developed a predictive model and online calculator for postoperative non-home discharge disposition in brain tumor patients that can be incorporated into preoperative workflows. METHODS: We reviewed all brain tumor patients at our institution from 2017 to 2019. A predictive model of discharge disposition containing preoperatively available variables was developed using stepwise multivariable logistic regression. Model performance was assessed using receiver operating characteristic curves and calibration curves. Internal validation was performed using bootstrapping with 2000 samples. RESULTS: Our cohort included 2335 patients who underwent 2586 surgeries with a 16% non-home discharge rate. Significant predictors of non-home discharge were age >60 years (odds ratio [OR], 2.02), African American (OR, 1.73) or Asian (OR, 2.05) race, unmarried status (OR, 1.48), Medicaid insurance (OR, 1.90), admission from another health care facility (OR, 2.30), higher 5-factor modified frailty index (OR, 1.61 for 5-factor modified frailty index ≥2), and lower Karnofsky Performance Status (increasing OR with each 10-point decrease in Karnofsky Performance Status). The model was well calibrated and had excellent discrimination (optimism-corrected C-statistic, 0.82). An open-access calculator was deployed (https://neurooncsurgery.shinyapps.io/discharge_calc/). CONCLUSIONS: A strongly performing predictive model and online calculator for non-home discharge disposition in brain tumor patients was developed. With further validation, this tool may facilitate more efficient discharge planning, with consequent improvements in quality and value of care for brain tumor patients.

The Prognostic Impact of Nutritional Status on Postoperative Outcomes in Glioblastoma.

OBJECTIVE: The clinical impact and optimal method of assessing nutritional status (NS) have not been rigorously examined in glioblastoma. We investigated the relationship between NS and postoperative survival (PS) in glioblastoma using 4 nutritional indices and identified which index best modeled PS. METHODS: NS was retrospectively assessed for patients with glioblastoma undergoing surgery at our institution from 2007 to 2019 using the albumin level, albumin/globulin ratio (AGR), nutritional risk index (NRI), and prognostic nutritional index (PNI). Optimal cut points for each index were identified using maximally selected rank statistics and previously established criteria. The predictive value of each index on PS was determined using Cox proportional hazards models adjusted for prognostic variables. The best-performing model was identified using the Akaike Information Criterion. RESULTS: Our analysis included 242 patients (64% male) with a mean age of 57.6 years, Karnofsky Performance Status of 77.6, 5-factor modified frailty index of 0.59, albumin level of 4.2 g/dL, AGR of 1.9, NRI of 105.6, and PNI of 47.4. Median PS after index and repeat surgery was 12.7 and 7.8 months, respectively. On multivariable analysis, low albumin level (hazard ratio [HR], 2.09; 95% confidence interval [CI], 1.52-2.89; P < 0.001), mild NRI (HR, 1.61; 95% CI, 1.04-2.49; P = 0.032), moderate/severe NRI (HR, 2.51; 95% CI, 1.64-3.85; P < 0.001), and low PNI (HR, 2.51; 95% CI, 1.78-3.53; P < 0.001), but not low AGR (HR, 1.17; 95% CI, 0.89-1.54; P = 0.270), predicted decreased PS. PNI had the lowest Akaike Information Criterion. CONCLUSIONS: NS predicts PS in glioblastoma. PNI may provide the best model for assessing NS. NS is an important modifiable aspect of brain tumor management that warrants increased attention.

A novel radiographic marker of sarcopenia with prognostic value in glioblastoma.

OBJECTIVE: Sarcopenia is an important prognostic consideration in surgical oncology that has received relatively little attention in brain tumor patients. Temporal muscle thickness (TMT) has recently been proposed as a novel radiographic marker of sarcopenia that can be efficiently obtained within existing workflows. We investigated the prognostic value of TMT in primary and progressive glioblastoma. METHODS: TMT measurements were performed on magnetic resonance images of 384 patients undergoing 541 surgeries for glioblastoma. Relationships between TMT and clinical characteristics were examined on bivariate analysis. Optimal TMT cutpoints were established using maximally selected rank statistics. Predictive value of TMT upon postoperative survival (PS) was assessed using Cox proportional hazards regression adjusted for age, sex, Karnofsky performance status (KPS), Stupp protocol completion, extent of resection, and tumor molecular markers. RESULTS: Average TMT for the primary and progressive glioblastoma cohorts was 9.55 mm and 9.40 mm, respectively. TMT was associated with age (r = -0.14, p = 0.0008), BMI (r = 0.29, p < 0.0001), albumin (r = 0.11, p = 0.0239), and KPS (r = 0.11, p = 0.0101). Optimal TMT cutpoints for the primary and progressive cohorts were ≤ 7.15 mm and ≤ 7.10 mm, respectively. High TMT was associated with increased Stupp protocol completion (p = 0.001). On Cox proportional hazards regression, high TMT predicted increased PS in progressive [HR 0.47 (95% confidence interval (CI)) 0.25-0.90), p = 0.023] but not primary [HR 0.99 (95% CI 0.64-1.51), p = 0.949] glioblastoma. CONCLUSIONS: TMT correlates with important prognostic variables in glioblastoma and predicts PS in patients with progressive, but not primary, disease. TMT may represent a pragmatic neurosurgical biomarker in glioblastoma that could inform treatment planning and perioperative optimization.