Comparing Glioblastoma Surgery Decisions Between Teams Using Brain Maps of Tumor Locations, Biopsies, and Resections

Causal relationship between type 2 diabetes and glioblastoma: bidirectional Mendelian randomization analysis

As the prevalence of Type 2 Diabetes Mellitus (T2DM) and Glioblastoma (GBM) rises globally, the relationship between T2DM and GBM remains controversial. This study aims to investigate whether genetically predicted T2DM is causally associated with GBM. We performed bidirectional Mendelian randomization (MR) analysis using data from genome-wide studies on T2DM (N = 62,892) and GBM (N = 218,792) in European populations. The results of the inverse-variance weighted (IVW) approach served as the primary outcomes. We applied Cochran's Q test and MR-Egger regression for heterogeneity assessment. Leave-one-out analysis was used to evaluate whether any single SNP significantly influenced the observed effect. Our findings reveal a significant causal association between T2DM and an increased risk of GBM (OR [95% CI] 1.70 [1.09, 2.65], P = 0.019). Conversely, the reverse association between T2DM and GBM was insignificant (OR [95% CI] 1.00 [0.99, 1.01], P = 0.408) (P > 0.40). Furthermore, the results from Cochran's Q-test and funnel plots in the MR-Egger method indicated no evidence of pleiotropy between the SNPs and GBM. Additionally, we mapped causal SNPs to genes and identified 10 genes, including MACF1, C1orf185, PTGFRN, NOTCH2, ABCB10, GCKR, THADA, RBMS1, SPHKAP, and PPARG, located on chromosomes 1, 2, and 3. These genes are involved in key biological processes such as the BMP signaling pathway and various metabolic pathways relevant to both conditions. This study provides robust evidence of a significant causal relationship between T2DM and an increased risk of GBM. The identified SNP-mapped genes highlight potential biological mechanisms underlying this association.

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.

Surgical resection of glioblastoma in basal ganglia and utility of exoscope: Technical case reports

Background

Due to the presence of many perforating arteries and the deep location of basal ganglia tumors, dissection of the perforating arteries is critical during tumor resection. However, this is challenging as these arteries are deeply embedded in the cerebrum. Surgeons need to bend their heads for a long time using operative microscope and it is uncomfortable for the operating surgeon. A high-definition (4K-HD) 3D exoscope system can significantly improve the surgeon's posture during resection and widen the operating view field considerably by adjusting the camera angle.

Methods

We report two cases of glioblastoma (GBM) involving basal ganglia. We used a 4K-HD 3D exoscope system for resecting the tumor and analyzed the intraoperative visualization of the operative fields.

Results

We could approach the deeply located feeding arteries before successfully resecting the tumor using a 4K-HD 3D exoscope system which would have been difficult with the sole use of an operative microscope. The postoperative recoveries were uneventful in both cases. However, postoperative magnetic resonance imaging showed infarction around the caudate head and corona radiata in one of the cases.

Conclusion

This study has highlighted using a 4K-HD 3D exoscope system in dissecting GBM involving basal ganglia. Although postoperative infarction is a risk, we could successfully visualize and dissect the tumors with minimal neurological deficits.

Determining Contemporary Barriers to Effective Multidisciplinary Team Meetings in Neurological Surgery: A Review of the Literature

OBJECTIVE

The integration of multidisciplinary team meetings (MDTMs) for neurosurgical care has been accepted worldwide. Our objective was to review the literature for the limiting factors to MDTMs that may introduce bias to patient care.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analysis was used to perform a literature review of MDTMs for neuro-oncology, pituitary oncology, cerebrovascular surgery, and spine surgery and spine oncology. Limiting factors to productive MDTMs and factors that introduce bias were identified, as well as determining whether MDTMs led to improved patient outcomes.

RESULTS

We identified 1264 manuscripts from a PubMed and Ovid Medline search, of which 27 of 500 neuro-oncology, 4 of 279 pituitary, and 11 of 260 spine surgery articles met our inclusion criteria. Of 224 cerebrovascular manuscripts, none met the criteria. Factors for productive MDTMs included quaternary/tertiary referral centers, nonhierarchical environment, regularly scheduled meetings, concise inclusion of nonmedical factors at the same level of importance as patient clinical information, inclusion of nonclinical participants, and use of clinical guidelines and institutional protocols to provide recommendations. Our review did not identify literature that described the use of artificial intelligence to reduce bias and guide clinical care.

CONCLUSIONS

The continued implementation of MDTMs in neurosurgery should be recommended but cautioned by limiting bias.

The value of arterial spin labelling (ASL) perfusion MRI in the assessment of post-treatment progression in adult glioma: A systematic review and meta-analysis

Background

The distinction between viable tumor and therapy-induced changes is crucial for the clinical management of patients with gliomas. This study aims to quantitatively assess the efficacy of arterial spin labeling (ASL) biomarkers, including relative cerebral blood flow (rCBF) and absolute cerebral blood flow (CBF), for the discrimination of progressive disease (PD) and treatment-related effects.

Methods

Eight articles were included in the synthesis after searching the literature systematically. Data have been extracted and a meta-analysis using the random-effect model was subsequently carried out. Diagnostic accuracy assessment was also performed.

Results

This study revealed that there is a significant difference in perfusion measurements between groups with PD and therapy-induced changes. The rCBF yielded a standardized mean difference (SMD) of 1.25 [95% CI 0.75, 1.75] (p < .00001). The maximum perfusion indices (rCBFmax and CBFmax) both showed equivalent discriminatory ability, with SMD of 1.35 [95% CI 0.78, 1.91] (p < .00001) and 1.56 [95% CI 0.79, 2.33] (p < .0001), respectively. Similarly, accuracy estimates were comparable among ASL-derived metrices. Pooled sensitivities [95% CI] were 0.85 [0.67, 0.94], 0.88 [0.71, 0.96], and 0.93 [0.73, 0.98], and pooled specificities [95% CI] were 0.83 [0.71, 0.91], 0.83 [0.67, 0.92], 0.84 [0.67, 0.93], for rCBF, rCBFmax and CBFmax, respectively. Corresponding HSROC area under curve (AUC) [95% CI] were 0.90 [0.87, 0.92], 0.92 [0.89, 0.94], and 0.93 [0.90, 0.95].

Conclusion

These results suggest that ASL quantitative biomarkers, particularly rCBFmax and CBFmax, have the potential to discriminate between glioma progression and therapy-induced changes.

Diabetes Mellitus Management in the Context of Cranial Tumors

The study of the relationship between cancer and diabetes mellitus (DM) has been under investigation for many decades. Particularly in the field of neurology and neurosurgery, increasing emphasis has been put on the examination of comorbid DM in patients with cranial tumors. Namely, as the most common and invasive type of malignant adult brain tumor, glioblastoma (GBS) has been the focus of said research. Several mechanisms have been described in the attempt to elucidate the underlying association between DM and GBS, with the metabolic phenomenon known as the Warburg effect and its consequential downstream effects serving as the resounding culprits in recent literature. Since the effect seen in cancers like GBS exploits an upregulated form of aerobic glycolysis, the role of a sequela of DM, known as hyperglycemia, will be investigated. In particular, in the treatment of GBS, surgical resection and subsequent chemotherapy and/or radiotherapy are used in conjunction with corticosteroid therapy, the latter of which has been linked to hyperglycemia. Unsurprisingly, comorbid DM patients are significantly susceptible to this disposition. Further, this fact is reflected in recent literature that demonstrates the impact of hyperglycemia on cancer advancement and patient outcomes in several preclinical and clinical studies. Thus, this review will aim to underline the significance of diabetes and glycemic control via standard-of-care treatments such as metformin administration, as well as to describe emerging treatments such as the signaling modulation of insulin-like growth factor and the employment of the ketogenic diet.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques

Objective

Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.

Methods

Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).

Results

High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.

Conclusion

Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies

OBJECTIVE

Monitoring biomarkers using machine learning (ML) may determine glioblastoma treatment response. We systematically reviewed quality and performance accuracy of recently published studies.

METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy, we extracted articles from MEDLINE, EMBASE and Cochrane Register between 09/2018-01/2021. Included study participants were adults with glioblastoma having undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide), and follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics, the target condition). Using Quality Assessment of Diagnostic Accuracy Studies Two/Checklist for Artificial Intelligence in Medical Imaging, we assessed bias risk and applicability concerns. We determined test set performance accuracy (sensitivity, specificity, precision, F1-score, balanced accuracy). We used a bivariate random-effect model to determine pooled sensitivity, specificity, area-under the receiver operator characteristic curve (ROC-AUC). Pooled measures of balanced accuracy, positive/negative likelihood ratios (PLR/NLR) and diagnostic odds ratio (DOR) were calculated. PROSPERO registered (CRD42021261965).

RESULTS

Eighteen studies were included (1335/384 patients for training/testing respectively). Small patient numbers, high bias risk, applicability concerns (particularly confounding in reference standard and patient selection) and low level of evidence, allow limited conclusions from studies. Ten studies (10/18, 56%) included in meta-analysis gave 0.769 (0.649-0.858) sensitivity [pooled (95% CI)]; 0.648 (0.749-0.532) specificity; 0.706 (0.623-0.779) balanced accuracy; 2.220 (1.560-3.140) PLR; 0.366 (0.213-0.572) NLR; 6.670 (2.800-13.500) DOR; 0.765 ROC-AUC.

CONCLUSION

ML models using MRI features to distinguish between progression and mimics appear to demonstrate good diagnostic performance. However, study quality and design require improvement.

Predictors for time to awake in patients undergoing awake craniotomies

OBJECTIVE

Awake craniotomies are often characterized by alternating asleep-awake-asleep periods. Preceding the awake phase, patients are weaned from anesthesia and mechanical ventilation. Although clinicians aim to minimize the time to awake for patient safety and operating room efficiency, in some patients, the time to awake exceeds 20 minutes. The goal of this study was to determine the average time to awake and the factors associated with prolonged time to awake (> 20 minutes) in patients undergoing awake craniotomy.

METHODS

Records of patients who underwent awake craniotomy between 2003 and 2020 were evaluated. Time to awake was defined as the time between discontinuation of propofol and remifentanil infusion and the time of extubation. Patient and perioperative characteristics were explored as predictors for time to awake using logistic regression analyses.

RESULTS

Data of 307 patients were analyzed. The median (IQR) time to awake was 13 (10-20) minutes and exceeded 20 minutes in 17% (95% CI 13%-21%) of the patients. In both univariate and multivariable analyses, increased age, nonsmoker status, and American Society of Anesthesiologists (ASA) class III versus II were associated with a time to awake exceeding 20 minutes. BMI, as well as the use of alcohol, drugs, dexamethasone, or antiepileptic agents, was not significantly associated with the time to awake.

CONCLUSIONS

While most patients undergoing awake craniotomy are awake within a reasonable time frame after discontinuation of propofol and remifentanil infusion, time to awake exceeded 20 minutes in 17% of the patients. Increasing age, nonsmoker status, and higher ASA classification were found to be associated with a prolonged time to awake.

Survival of glioblastoma in relation to tumor location: a statistical tumor atlas of a population-based cohort

Purpose

Previous studies on the effect of tumor location on overall survival in glioblastoma have found conflicting results. Based on statistical maps, we sought to explore the effect of tumor location on overall survival in a population-based cohort of patients with glioblastoma and IDH wild-type astrocytoma WHO grade II–III with radiological necrosis.

Methods

Patients were divided into three groups based on overall survival: < 6 months, 6–24 months, and > 24 months. Statistical maps exploring differences in tumor location between these three groups were calculated from pre-treatment magnetic resonance imaging scans. Based on the results, multivariable Cox regression analyses were performed to explore the possible independent effect of centrally located tumors compared to known prognostic factors by use of distance from center of the third ventricle to contrast-enhancing tumor border in centimeters as a continuous variable.

Results

A total of 215 patients were included in the statistical maps. Central tumor location (corpus callosum, basal ganglia) was associated with overall survival < 6 months. There was also a reduced overall survival in patients with tumors in the left temporal lobe pole. Tumors in the dorsomedial right temporal lobe and the white matter region involving the left anterior paracentral gyrus/dorsal supplementary motor area/medial precentral gyrus were associated with overall survival > 24 months. Increased distance from center of the third ventricle to contrast-enhancing tumor border was a positive prognostic factor for survival in elderly patients, but less so in younger patients.

Conclusions

Central tumor location was associated with worse prognosis. Distance from center of the third ventricle to contrast-enhancing tumor border may be a pragmatic prognostic factor in elderly patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00701-021-04802-6.

Extent of resection and survival in patients with glioblastoma multiforme: Systematic review and meta-analysis

BACKGROUND

Glioblastoma multiforme (GBM) owes an ominous prognosis: its mean overall survival is 14 months. The extent of surgical resection (ESR) highlights among factors in which an association has been found to a somewhat better prognosis. However, the association between greater ESR and prolonged overall (OS) survival is not a constant finding nor a proven cause-and-effect phenomenon. To our objective is to establish the strength of association between ESR and OS in patients with GBM through a systematic review and meta-analysis.

METHODS

In accordance with PRISMA-P recommendations, we conducted a systematic literature search; we included studies with adult patients who had undergone craniotomy for GBM. Our primary outcome is overall postoperative survival at 12 and 24 months. We reviewed 180 studies, excluded 158, and eliminated 8; 14 studies that suited our requirements were analyzed.

RESULTS

The initial level of evidence of all studies is low, and it may be degraded to very low according to GRADE criteria because of design issues. The definition of different levels of the extent of resection is heterogeneous and poorly defined. We found a great amount of variation in the methodology of the operation and the adjuvant treatment protocol. The combined result for relative risk (RR) for OS for 12 months analysis is 1.25 [95% confidence interval (95% CI) 1.14-1.36, P < .01], absolute risk reduction (ARR) of 15.7% (95% CI 11.9-19.4), relative risk reduction (RRR) of 0.24 (95% CI 0.18-0.31), number needed to treat (NNT) 6; for 24-month analysis RR is 1.59 (95% CI 1.11-2.26, P < .01) ARR of 11.5% (95% CI 7.7-15.1), relative risk reduction (RRR) of 0.53 (95% CI 0.33-0.76), (NNT) 9. In each term analysis, the proportion of alive patients who underwent more extensive resection is significantly higher than those who underwent subtotal resection.

CONCLUSION

Our results sustain a weak but statistically significant association between the ESR and OS in patients with GBM obtained from observational studies with a very low level of evidence according to GRADE criteria. As a consequence, any estimate of effect is very uncertain. Current information cannot sustain a cause-and-effect relationship between these variables.

Glioblastoma Surgery Imaging-Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software.

Functional maps of direct electrical stimulation-induced speech arrest and anomia: a multicentre retrospective study

Direct electrical stimulation, the transient ‘lesional’ method probing brain function, has been utilized in identifying the language cortex and preserving language function during epilepsy and neuro-oncological surgeries for about a century. However, comparison of functional maps of the language cortex across languages/continents based on cortical stimulation remains unclear. We conducted a retrospective multicentre study including four cohorts of direct electrical stimulation mapping from four centres across three continents, where three indigenous languages (English, French and Mandarin) are spoken. All subjects performed the two most common language tasks: number counting and picture naming during stimulation. All language sites were recorded and normalized to the same brain template. Next, Spearman’s correlation analysis was performed to explore the consistency of the distributions of the language cortex across centres, a kernel density estimation to localize the peak coordinates, and a hierarchical cluster analysis was performed to detect the crucial epicenters. A total of 598 subjects with 917 speech arrest sites (complete interruption of ongoing counting) and 423 anomia sites (inability to name or misnaming) were included. Different centres presented highly consistent distribution patterns for speech arrest (Spearman’s coefficient r ranged from 0.60 to 0.85, all pair-wise correlations P < 0.05), and similar patterns for anomia (Spearman’s coefficient r ranged from 0.37 to 0.80). The combinational speech arrest map was divided into four clusters: cluster 1 mainly located in the ventral precentral gyrus and pars opercularis, which contained the peak of speech arrest in the ventral precentral gyrus; cluster 2 in the ventral and dorsal precentral gyrus; cluster 3 in the supplementary motor area; cluster 4 in the posterior superior temporal gyrus and supramarginal gyrus. The anomia map revealed two clusters: one was in the posterior part of the superior and middle temporal gyri, which peaked at the posterior superior temporal gyrus; and the other within the inferior frontal gyrus, peaked at the pars triangularis. This study constitutes the largest series to date of language maps generated from direct electrical stimulation mapping. The consistency of data provides evidence for common language networks across languages, in the context of both speech and naming circuit. Our results not only clinically offer an atlas for language mapping and protection, but also scientifically provide better insight into the functional organization of language networks.

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.

Hemisphere-Specific Functional Remodeling and Its Relevance to Tumor Malignancy of Cerebral Glioma Based on Resting-State Functional Network Analysis

BACKGROUND

Functional remodeling may vary with tumor aggressiveness of glioma. Investigation of the functional remodeling is expected to provide scientific relevance of tumor characterization and disease management of glioma. In this study, we aimed to investigate the functional remodeling of the contralesional hemisphere and its utility in predicting the malignant grade of glioma at the individual level with multivariate logistic regression (MLR) analysis.

SUBJECTS AND METHODS

One hundred and twenty-six right-handed subjects with histologically confirmed cerebral glioma were included with 80 tumors located in the left hemisphere (LH) and 46 tumors located in the right hemisphere (RH). Resting-state functional networks of the contralesional hemisphere were constructed using the human brainnetome atlas based on resting-state fMRI data. Functional connectivity and topological features of functional networks were quantified. The performance of functional features in predicting the glioma grade was evaluated using area under (AUC) the receiver operating characteristic curve (ROC). The dataset was divided into training and validation datasets. Features with high AUC values in malignancy classification in the training dataset were determined as predictive features. An MLR model was constructed based on predictive features and its classification performance was evaluated on the training and validation datasets with 10-fold cross validation.

RESULTS

Predictive functional features showed apparent hemispheric specifications. MLR classification models constructed with age and predictive functional connectivity features (AUC of 0.853 ± 0.079 and 1.000 ± 0.000 for LH and RH group, respectively) and topological features (AUC of 0.788 ± 0.150 and 0.897 ± 0.165 for LH and RH group, respectively) achieved efficient performance in predicting the malignant grade of gliomas.

CONCLUSION

Functional remodeling of the contralesional hemisphere was hemisphere-specific and highly predictive of the malignant grade of glioma. Network approach provides a novel pathway that may innovate glioma characterization and management at the individual level.

What effects does awake craniotomy have on functional and survival outcomes for glioblastoma patients?

PURPOSE

Neurosurgeons adopt several different surgical approaches to deal with glioblastomas (GB) located in or near eloquent areas. Some attempt maximal safe resection by awake craniotomy (AC), but doubts persist concerning the real benefits of this type of surgery in this situation. We performed a retrospective study to evaluate the extent of resection (EOR), functional and survival outcomes after AC of patients with GB in critical locations.

METHODS

Forty-six patients with primary GB treated with the Stupp regimen between 2004 and 2019, for whom brain mapping was feasible, were included. We assessed EOR, postoperative language and/or motor deficits three months after AC, progression-free survival (PFS) and overall survival (OS).

RESULTS

Complete resection was achieved in 61% of the 46 GB patients. The median PFS was 6.8 months (CI 6.1; 9.7) and the median OS was 17.6 months (CI 14.8; 34.1). Three months after AC, more than half the patients asymptomatic before surgery remained asymptomatic, and one third of patients with symptoms before surgery experienced improvements in language, but not motor functions. The risk of postoperative deficits was higher in patients with preoperative deficits or incomplete resection. Furthermore, the presence of postoperative deficits was an independent predictive factor for shorter PFS.

CONCLUSION

AC is an option for the resection of GB in critical locations. The observed survival outcomes are typical for GB patients in the Stupp era. However, the success of AC in terms of the recovery or preservation of language and/or motor functions cannot be guaranteed, given the aggressiveness of the tumor.

Timing of glioblastoma surgery and patient outcomes: a multicenter cohort study

BACKGROUND

The impact of time-to-surgery on clinical outcome for patients with glioblastoma has not been determined. Any delay in treatment is perceived as detrimental, but guidelines do not specify acceptable timings. In this study, we relate the time to glioblastoma surgery with the extent of resection and residual tumor volume, performance change, and survival, and we explore the identification of patients for urgent surgery.

METHODS

Adults with first-time surgery in 2012-2013 treated by 12 neuro-oncological teams were included in this study. We defined time-to-surgery as the number of days between the diagnostic MR scan and surgery. The relation between time-to-surgery and patient and tumor characteristics was explored in time-to-event analysis and proportional hazard models. Outcome according to time-to-surgery was analyzed by volumetric measurements, changes in performance status, and survival analysis with patient and tumor characteristics as modifiers.

RESULTS

Included were 1033 patients of whom 729 had a resection and 304 a biopsy. The overall median time-to-surgery was 13 days. Surgery was within 3 days for 235 (23%) patients, and within a month for 889 (86%). The median volumetric doubling time was 22 days. Lower performance status (hazard ratio [HR] 0.942, 95% confidence interval [CI] 0.893-0.994) and larger tumor volume (HR 1.012, 95% CI 1.010-1.014) were independently associated with a shorter time-to-surgery. Extent of resection, residual tumor volume, postoperative performance change, and overall survival were not associated with time-to-surgery.

CONCLUSIONS

With current decision-making for urgent surgery in selected patients with glioblastoma and surgery typically within 1 month, we found equal extent of resection, residual tumor volume, performance status, and survival after longer times-to-surgery.

Clinical Risk and Overall Survival in Patients with Diabetes Mellitus, Hyperglycemia and Glioblastoma Multiforme. A Review of the Current Literature

The relationship between type 2 diabetes mellitus (DM2) and hyperglycemia with cancer patients remains controversial also in the setting of patients with glioblastoma multiforme (GBM), the most common and aggressive form of astrocytoma with a short overall survival (OS) and poor prognosis. A systematic search of two databases was performed for studies published up to 19 August 2020, reporting the OS of patients with DM2 or high blood sugar level and GBM and the clinical risk of diabetic patients for development of GBM. According to PRISMA guidelines, we included a total of 20 papers reporting clinical data of patients with GBM and diabetes and/or hyperglycemia. The aim of this review was to investigate the effect of DM2, hyperglycemia and metformin on OS of patients with GBM. In addition, we evaluated the effect of these factors on the risk of development of GBM. This review supports accumulating evidence that hyperglycemia, rather than DM2, and elevated BMI are independent risk factors for poor outcome and shorter OS in patients with GBM. GBM patients with normal weight compared to obese, and diabetic patients on metformin compared to other therapies, seems to have a longer OS. Further studies are needed to understand better these associations.

Partial resection offers an overall survival benefit over biopsy in MGMT-unmethylated IDH-wildtype glioblastoma patients

Background Isocitrate dehydrogenase (IDH)-wildtype glioblastoma patients with O⁶-methylguanine-DNA-methyltransferase (MGMT)-unmethylated tumors have the worst outcome of all glioblastoma patients. The overall survival (OS) benefit of partial resection of glioblastoma compared to biopsy only remains controversial specifically in relation to molecular factors. In this report, we analyzed the effect of incomplete resection on OS compared to biopsy only in a cohort of IDH-wildtype glioblastoma patients who were uniformly treated with temozolomide-based chemoradiotherapy (TMZ-CR) after surgery. Material & Methods A retrospective study was conducted including only glioblastoma patients who were treated with TMZ-CR after surgery from two centers. Surgical groups were defined as biopsy only, partial resection (PR) or gross total resection depending on the presence of contrast-enhancing tumor on postoperative imaging. IDH-mutation was determined using next generation sequencing technique and MGMT-methylation was analyzed with semi-quantitative methylation-specific polymerase chain reaction. Next to descriptive statistics, univariate and multivariate survival analyses were performed using Kaplan-Meier estimates and Cox regression models. Results In total, 159 patients were included. 37 patients underwent biopsy only and 73 partial resections. 99 patients (62.3%) harbored unmethylated tumors. Median OS for the whole patient group was 13.4 months. In the subgroup of patients with unmethylated tumors, PR yielded a median OS of 12.2 months vs 7.6 months for biopsy patients (P = 0.003). PR proved an independent beneficial prognostic factor in multivariate Cox regression model, together with age, Karnofsky Performance Score and MGMT-methylation. Conclusion In IDH-wildtype glioblastoma patients with MGMT-unmethylated tumors, treated with chemoradiotherapy after surgery, PR yields a significant OS benefit compared to biopsy.

Robust Deep Learning-based Segmentation of Glioblastoma on Routine Clinical MRI Scans Using Sparsified Training

PURPOSE

To improve the robustness of deep learning-based glioblastoma segmentation in a clinical setting with sparsified datasets.

MATERIALS AND METHODS

In this retrospective study, preoperative T1-weighted, T2-weighted, T2-weighted fluid-attenuated inversion recovery, and postcontrast T1-weighted MRI from 117 patients (median age, 64 years; interquartile range [IQR], 55-73 years; 76 men) included within the Multimodal Brain Tumor Image Segmentation (BraTS) dataset plus a clinical dataset (2012-2013) with similar imaging modalities of 634 patients (median age, 59 years; IQR, 49-69 years; 382 men) with glioblastoma from six hospitals were used. Expert tumor delineations on the postcontrast images were available, but for various clinical datasets, one or more sequences were missing. The convolutional neural network, DeepMedic, was trained on combinations of complete and incomplete data with and without site-specific data. Sparsified training was introduced, which randomly simulated missing sequences during training. The effects of sparsified training and center-specific training were tested using Wilcoxon signed rank tests for paired measurements.

RESULTS

A model trained exclusively on BraTS data reached a median Dice score of 0.81 for segmentation on BraTS test data but only 0.49 on the clinical data. Sparsified training improved performance (adjusted P < .05), even when excluding test data with missing sequences, to median Dice score of 0.67. Inclusion of site-specific data during sparsified training led to higher model performance Dice scores greater than 0.8, on par with a model based on all complete and incomplete data. For the model using BraTS and clinical training data, inclusion of site-specific data or sparsified training was of no consequence.

CONCLUSION

Accurate and automatic segmentation of glioblastoma on clinical scans is feasible using a model based on large, heterogeneous, and partially incomplete datasets. Sparsified training may boost the performance of a smaller model based on public and site-specific data.Supplemental material is available for this article.Published under a CC BY 4.0 license.

Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma

To summarize the distribution of glioma location within a patient population, registration of individual MR images to anatomical reference space is required. In this study, we quantified the accuracy of MR image registration to anatomical reference space with linear and non-linear transformations using estimated tumor targets of glioblastoma and lower-grade glioma, and anatomical landmarks at pre- and post-operative time-points using six commonly used registration packages (FSL, SPM5, DARTEL, ANTs, Elastix, and NiftyReg). Routine clinical pre- and post-operative, post-contrast T1-weighted images of 20 patients with glioblastoma and 20 with lower-grade glioma were collected. The 2009a Montreal Neurological Institute brain template was used as anatomical reference space. Tumors were manually segmented in the patient space and corresponding healthy tissue was delineated as a target volume in the anatomical reference space. Accuracy of the tumor alignment was quantified using the Dice score and the Hausdorff distance. To measure the accuracy of general brain alignment, anatomical landmarks were placed in patient and in anatomical reference space, and the landmark distance after registration was quantified. Lower-grade gliomas were registered more accurately than glioblastoma. Registration accuracy for pre- and post-operative MR images did not differ. SPM5 and DARTEL registered tumors most accurate, and FSL least accurate. Non-linear transformations resulted in more accurate general brain alignment than linear transformations, but tumor alignment was similar between linear and non-linear transformation. We conclude that linear transformation suffices to summarize glioma locations in anatomical reference space.

The Caudate Nucleus: Its Connections, Surgical Implications, and Related Complications

BACKGROUND

The caudate nucleus is a C-shaped structure that is located in the center of the brain and is divided into 3 parts: the head, body, and tail.

METHODS

We detail the anatomic connections, relationships with other basal ganglia structures, and clinical implications of injury to the caudate nucleus.

RESULTS

Anatomically, the most inferior transcapsular gray matter is the lentiform peduncle, which is the connection between the lentiform nucleus and caudate nucleus as well as the amygdala. The border between the tail and body of the caudate nucleus is the posterior insular point. The tail of the caudate nucleus is extraependymal in some parts and intraependymal in some parts of the roof of the temporal horn of the lateral ventricle. The tail of the caudate nucleus crosses the inferior limiting sulcus (temporal stem), and section of the tail during approaches to lesions involving the temporal stem may cause motor apraxia. The mean distance from the temporal limen point, which is the junction of the limen insula and inferior limiting sulcus, to the tail of the caudate nucleus in the temporal stem is 15.87 ± 3.10 mm.

CONCLUSIONS

Understanding of the functional anatomy and connections of the distinct parts of the caudate nucleus is essential for deciding the extent of resection of lesions involving the caudate nucleus and the types of deficits that may be found postoperatively.

Advancements in predicting outcomes in patients with glioma: a surgical perspective

Introduction: Diffuse glioma is a challenging neurosurgical entity. Although surgery does not provide a cure, it may greatly influence survival, brain function, and quality of life. Surgical treatment is by nature highly personalized and outcome prediction is very complex. To engage and succeed in this balancing act it is important to make best use of the information available to the neurosurgeon.Areas covered: This narrative review provides an update on advancements in predicting outcomes in patients with glioma that are relevant to neurosurgeons.Expert opinion: The classical 'gut feeling' is notoriously unreliable and better prediction strategies for patients with glioma are warranted. There are numerous tools readily available for the neurosurgeon in predicting tumor biology and survival. Predicting extent of resection, functional outcome, and quality of life remains difficult. Although machine-learning approaches are currently not readily available in daily clinical practice, there are several ongoing efforts with the use of big data sets that are likely to create new prediction models and refine the existing models.

Growth pattern of experimental glioblastoma

Glioblastoma multiforme (GBM) is an aggressive primary brain malignancy with a very poor prognosis. Researchers employ animal models to develop potential therapies. It is important that these models have clinical relevance. This means that old models, propagated for decades in cultures, should be questioned. Parameters to be evaluated include whether animals are immune competent or not, the infiltrative growth pattern of the tumor, tumor volume resulting in symptoms and growth rate. We here describe the growth pattern of an experimental glioblastoma model in detail with GFP positive glioblastoma cells in fully immune competent animals and study tumor growth rate and tumor mass as a function of time from inoculation. We were able to correlate findings made with classical immunohistochemistry and MR findings. The tumor growth rate was fitted by a Gompertz function. The model predicted the time until onset of symptoms for 5000 inoculated cells to 18.7±0.4 days, and the tumor mass at days 10 and 14, which are commonly used as the start of treatment in therapeutic studies, were 5.97±0.62 mg and 29.1±3.0 mg, respectively. We want to raise the question regarding the clinical relevance of the outline of glioblastoma experiments, where treatment is often initiated at a very early stage. The approach presented here could potentially be modified to gain information also from other tumor models.

Voxelwise statistical methods to localize practice variation in brain tumor surgery

PURPOSE

During resections of brain tumors, neurosurgeons have to weigh the risk between residual tumor and damage to brain functions. Different perspectives on these risks result in practice variation. We present statistical methods to localize differences in extent of resection between institutions which should enable to reveal brain regions affected by such practice variation.

METHODS

Synthetic data were generated by simulating spheres for brain, tumors, resection cavities, and an effect region in which a likelihood of surgical avoidance could be varied between institutions. Three statistical methods were investigated: a non-parametric permutation based approach, Fisher's exact test, and a full Bayesian Markov chain Monte Carlo (MCMC) model. For all three methods the false discovery rate (FDR) was determined as a function of the cut-off value for the q-value or the highest density interval, and receiver operating characteristic and precision recall curves were created. Sensitivity to variations in the parameters of the synthetic model were investigated. Finally, all these methods were applied to retrospectively collected data of 77 brain tumor resections in two academic hospitals.

RESULTS

Fisher's method provided an accurate estimation of observed FDR in the synthetic data, whereas the permutation approach was too liberal and underestimated FDR. AUC values were similar for Fisher and Bayes methods, and superior to the permutation approach. Fisher's method deteriorated and became too liberal for reduced tumor size, a smaller size of the effect region, a lower overall extent of resection, fewer patients per cohort, and a smaller discrepancy in surgical avoidance probabilities between the different surgical practices. In the retrospective patient data, all three methods identified a similar effect region, with lower estimated FDR in Fisher's method than using the permutation method.

CONCLUSIONS

Differences in surgical practice may be detected using voxel statistics. Fisher's test provides a fast method to localize differences but could underestimate true FDR. Bayesian MCMC is more flexible and easily extendable, and leads to similar results, but at increased computational cost.