Awake brain tumor resection during pregnancy: Decision making and technical nuances

The co-occurrence of primary brain tumor and pregnancy poses unique challenges to the treating physician. If a rapidly growing lesion causes life-threatening mass effect, craniotomy for tumor debulking becomes urgent. The choice between awake craniotomy versus general anesthesia becomes complicated if the tumor is encroaching on eloquent brain because considerations pertinent to both patient safety and oncological outcome, in addition to fetal wellbeing, are involved. A 31-year-old female at 30 weeks gestation with twins presented to our hospital seeking awake craniotomy to resect a 7 × 6 × 5 cm left frontoparietal brain tumor with 7 mm left-to-right subfalcine herniation on imaging that led to word finding difficulty, dysfluency, right upper extremity paralysis, and right lower extremity weakness. She had twice undergone tumor debulking under general anesthesia during the same pregnancy at an outside hospital at 16 weeks and 28 weeks gestation. There were considerations both for and against awake brain tumor resection over surgery under general anesthesia. The decision-making process and the technical nuances related to awake brain tumor resection in this neurologically impaired patient are discussed. Awake craniotomy benefits the patient who harbors a tumor that encroaches on the eloquent brain by allowing a greater extent of resection while preserving the language and sensorimotor function. It can be successfully done in pregnant patients who are neurologically impaired. The patient should be motivated and well informed of the details of the process. A multidisciplinary and collaborative effort is also crucial

Measure Twice: Promise of Liquid Biopsy in Pediatric High-Grade Gliomas

Purpose To review and critique the current state of liquid biopsy in pHGG. Materials and Methods Published literature was reviewed for articles related to liquid biopsy in pediatric glioma and adult glioma with a focus on high-grade gliomas. Results This review discusses the current state of liquid biomarkers of pHGG and their potential applications for liquid biopsy development. Conclusions While nascent, the progress toward identifying circulating analytes of pHGG primes the field of neuro-oncoogy for liquid biopsy development

Quantifying eloquent locations for glioblastoma surgery using resection probability maps

OBJECTIVE Decisions in glioblastoma surgery are often guided by presumed eloquence of the tumor location. The authors introduce the "expected residual tumor volume" (eRV) and the "expected resectability index" (eRI) based on previous decisions aggregated in resection probability maps. The diagnostic accuracy of eRV and eRI to predict biopsy decisions, resectability, functional outcome, and survival was determined. METHODS Consecutive patients with first-time glioblastoma surgery in 2012-2013 were included from 12 hospitals. The eRV was calculated from the preoperative MR images of each patient using a resection probability map, and the eRI was derived from the tumor volume. As reference, Sawaya's tumor location eloquence grades (EGs) were classified. Resectability was measured as observed extent of resection (EOR) and residual volume, and functional outcome as change in Karnofsky Performance Scale score. Receiver operating characteristic curves and multivariable logistic regression were applied. RESULTS Of 915 patients, 674 (74%) underwent a resection with a median EOR of 97%, functional improvement in 71 (8%), functional decline in 78 (9%), and median survival of 12.8 months. The eRI and eRV identified biopsies and EORs of at least 80%, 90%, or 98% better than EG. The eRV and eRI predicted observed residual volumes under 10, 5, and 1 ml better than EG. The eRV, eRI, and EG had low diagnostic accuracy for functional outcome changes. Higher eRV and lower eRI were strongly associated with shorter survival, independent of known prognostic factors. CONCLUSIONS The eRV and eRI predict biopsy decisions, resectability, and survival better than eloquence grading and may be useful preoperative indices to support surgical decisions

Primary gliosarcoma: key clinical and pathologic distinctions from glioblastoma with implications as a unique oncologic entity

This report presents the historical experience, clinical presentation, treatment, prognosis, and pathogenesis of gliosarcoma described to date in the English literature. PubMed query of term “gliosarcoma” was performed, followed by a rigorous review of cited literature. Articles selected for analysis included: (1) case reports of gliosarcoma, (2) review articles of gliosarcoma, and (3) studies of the pathogenesis or genetics of gliosarcoma in humans. Our review identified 219 cases of gliosarcoma in 34 reports and eight articles addressing the pathogenesis. Survival in larger series ranged 4–11.5 months. Features unique to gliosarcoma compared to glioblastoma (GBM) include their temporal lobe predilection, potential to appear similar to a meningioma at surgery, repeated reports of extracranial metastases, and infrequency of EGFR mutations. Published experience is limited to small case series, and the pathogenesis remains unclear. Clinical and pathologic characteristics distinct from GBM suggest that they may warrant specific treatment, separate from conventional GBM therapy

On the cutting edge of glioblastoma surgery:where neurosurgeons agree and disagree on surgical decisions

OBJECTIVE: The aim of glioblastoma surgery is to maximize the extent of resection while preserving functional integrity. Standards are lacking for surgical decision-making, and previous studies indicate treatment variations. These shortcomings reflect the need to evaluate larger populations from different care teams. In this study, the authors used probability maps to quantify and compare surgical decision-making throughout the brain by 12 neurosurgical teams for patients with glioblastoma. METHODS: The study included all adult patients who underwent first-time glioblastoma surgery in 2012-2013 and were treated by 1 of the 12 participating neurosurgical teams. Voxel-wise probability maps of tumor location, biopsy, and resection were constructed for each team to identify and compare patient treatment variations. Brain regions with different biopsy and resection results between teams were identified and analyzed for patient functional outcome and survival. RESULTS: The study cohort consisted of 1087 patients, of whom 363 underwent a biopsy and 724 a resection. Biopsy and resection decisions were generally comparable between teams, providing benchmarks for probability maps of resections and biopsies for glioblastoma. Differences in biopsy rates were identified for the right superior frontal gyrus and indicated variation in biopsy decisions. Differences in resection rates were identified for the left superior parietal lobule, indicating variations in resection decisions. CONCLUSIONS: Probability maps of glioblastoma surgery enabled capture of clinical practice decisions and indicated that teams generally agreed on which region to biopsy or to resect. However, treatment variations reflecting clinical dilemmas were observed and pinpointed by using the probability maps, which could therefore be useful for quality-of-care discussions between surgical teams for patients with glioblastoma

Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals’ data. All models’ median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74–0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring