The Hydrogel Endovascular Aneurysm Treatment Trial (HEAT): A Randomized Controlled Trial of the Second-Generation Hydrogel Coil

© 2020 Congress of Neurological Surgeons 2020. BACKGROUND: Aneurysm recurrence after coiling has been associated with aneurysm growth, (re)hemorrhage, and a greater need for follow-up. The second-generation HydroCoil Embolic System (HES; MicroVention, Inc) consists of a platinum core with integrated hydrogel and was developed to reduce recurrence through enhancing packing density and healing within the aneurysm. OBJECTIVE: To compare recurrence between the second-generation HES and bare platinum coil (BPC) in the new-generation Hydrogel Endovascular Aneurysm Treatment Trial (HEAT). METHODS: HEAT is a randomized, controlled trial that enrolled subjects with ruptured or unruptured 3- to 14-mm intracranial aneurysms amenable to coiling. The primary endpoint was aneurysm recurrence using the Raymond-Roy scale. Secondary endpoints included minor and major recurrence, packing density, adverse events related to the procedure and/or device, mortality, initial complete occlusion, aneurysm retreatment, hemorrhage from target aneurysm during follow-up, aneurysm occlusion stability, and clinical outcome at final follow-up. RESULTS: A total of 600 patients were randomized (HES, n = 297 and BPC, n = 303), including 28% with ruptured aneurysms. Recurrence occurred in 11 (4.4%) subjects in the HES arm and 44 (15.4%) subjects in the BPC arm (P =. 002). While the initial occlusion rate was higher with BPC, the packing density and both major and minor recurrence rates were in favor of HES. Secondary endpoints including adverse events, retreatment, hemorrhage, mortality, and clinical outcome did not differ between arms. CONCLUSION: Coiling of small-to-medium aneurysms with second-generation HES resulted in less recurrence when compared to BPC, without increased harm. These data further support the use of the second-generation HES for the embolization of intracranial aneurysms. Video Abstract: 10.1093/neuros/nyaa006 nyaa006Media1 613226478400

Functional and Physical Outcomes following Use of a Flexible CO2 Laser Fiber and Bipolar Electrocautery in Close Proximity to the Rat Sciatic Nerve with Correlation to an In Vitro Thermal Profile Model

This study compared functional and physical collateral damage to a nerve when operating a Codman MALIS Bipolar Electrosurgical System CMC-III or a CO2 laser coupled to a laser, with correlation to an in vitro model of heating profiles created by the devices in thermochromic ink agarose. Functional damage of the rat sciatic nerve after operating the MALIS or CO2 laser at various power settings and proximities to the nerve was measured by electrically evoked nerve action potentials, and histology of the nerve was used to assess physical damage. Thermochromic ink dissolved in agarose was used to model the spatial and temporal profile of the collateral heating zone of the electrosurgical system and the laser ablation cone. We found that this laser can be operated at 2 W directly above the nerve with minimal damage, while power settings of 5 W and 10 W resulted in acute functional and physical nerve damage, correlating with the maximal heating cone in the thermochromic ink model. MALIS settings up to 40 (11 W) did not result in major functional or physical nerve damage until the nerve was between the forceps tips, correlating with the hottest zone, localized discretely between the tips

Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma.

BackgroundTemozolomide (TMZ) has been the standard-of-care chemotherapy for glioblastoma (GBM) patients for more than a decade. Despite this long time in use, significant questions remain regarding how best to optimize TMZ therapy for individual patients. Understanding the relationship between TMZ response and factors such as number of adjuvant TMZ cycles, patient age, patient sex, and image-based tumor features, might help predict which GBM patients would benefit most from TMZ, particularly for those whose tumors lack O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.Methods and findingsUsing a cohort of 90 newly-diagnosed GBM patients treated according to the standard of care, we examined the relationships between several patient and tumor characteristics and volumetric and survival outcomes during adjuvant chemotherapy. Volumetric changes in MR imaging abnormalities during adjuvant therapy were used to assess TMZ response. T1Gd volumetric response is associated with younger patient age, increased number of TMZ cycles, longer time to nadir volume, and decreased tumor invasiveness. Moreover, increased adjuvant TMZ cycles corresponded with improved volumetric response only among more nodular tumors, and this volumetric response was associated with improved survival outcomes. Finally, in a subcohort of patients with known MGMT methylation status, methylated tumors were more diffusely invasive than unmethylated tumors, suggesting the improved response in nodular tumors is not driven by a preponderance of MGMT methylated tumors.ConclusionsOur finding that less diffusely invasive tumors are associated with greater volumetric response to TMZ suggests patients with these tumors may benefit from additional adjuvant TMZ cycles, even for those without MGMT methylation

Accurate Patient-Specific Machine Learning Models Of Glioblastoma Invasion Using Transfer Learning

BACKGROUND AND PURPOSE: MR imaging–based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient’s own histologic data. MATERIALS AND METHODS: We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS: Tumor cell density significantly correlated with relative CBV (r 0.33, P .001), and T1-weighted postcontrast (r 0.36, P .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r 0.53, mean absolute error 15.19%) compared with one-model-fits-all (r 0.27, mean absolute error 17.79%). With multivariate modeling, transfer learning further improved performance (r 0.88, mean absolute error 5.66%) compared with one-model-fits-all (r 0.39, mean absolute error 16.55%). CONCLUSIONS: Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma