Rapid Passive Gamma Mapping as an Adjunct to Electrical Stimulation Mapping for Functional Localization in Resection of Primary Brain Neoplasms.

Objective: We examined the utility of passive high gamma mapping (HGM) as an adjunct to conventional awake brain mapping during glioma resection. We compared functional and survival outcomes before and after implementing intraoperative HGM.

Methods: This was a retrospective cohort study of 75 patients who underwent a first-time, awake craniotomy for glioma resection. Patients were stratified by whether their operation occurred before or after the implementation of a U.S. Food and Drug Administration-approved high-gamma mapping tool in July 2017.

Results: The preimplementation and postimplementation cohorts included 28 and 47 patients, respectively. Median intraoperative time (261 vs. 261 minutes, P = 0.250) and extent of resection (97.14% vs. 98.19%, P = 0.481) were comparable between cohorts. Median Karnofsky performance status at initial follow-up was similar between cohorts (P = 0.650). Multivariable Cox regression models demonstrated an adjusted hazard ratio for overall survival of 0.10 (95% confidence interval: 0.02-0.43, P = 0.002) for the postimplementation cohort relative to the preimplementation cohort. Progression-free survival adjusted for insular involvement showed an adjusted hazard ratio of 1.00 (95% confidence interval: 0.49-2.06, P = 0.999) following HGM implementation. Falling short of statistical significance, prevalence of intraoperative seizures and/or afterdischarges decreased after HGM implementation as well (12.7% vs. 25%, P = 0.150).

Conclusions: Our results tentatively indicate that passive HGM is a safe and potentially useful adjunct to electrical stimulation mapping for awake cortical mapping, conferring at least comparable functional and survival outcomes with a nonsignificant lower rate of intraoperative epileptiform events. Considering the limitations of our study design and patient cohort, further investigation is needed to better identify optimal use cases for HGM.