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

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection

Symptomatic popliteal venous aneurysm causing a footdrop

Symptomatic aneurysms of the popliteal vein are uncommon, with the majority resulting in thromboembolic complications. Neurologic symptoms are extremely rare. We present a case of a 53-year-old man with a footdrop resulting from a saccular popliteal venous aneurysm. Compression of the peroneal nerve caused the neurologic deficit. After aneurysmectomy and lateral venorrhaphy, the patient regained full strength of his lower leg muscles. Neurologic complications caused by a popliteal venous aneurysm should be considered in patients with a swelling in the popliteal fossa and a neurologic deficit of the lower extremity

Earliest radiological progression in glioblastoma by multidisciplinary consensus review

Background: Detection of glioblastoma progression is important for clinical decision-making on cessation or initiation of therapy, for enrollment in clinical trials, and for response measurement in time and location. The RANO-criteria are considered standard for the timing of progression. To evaluate local treatment, we aim to find the most accurate progression location. We determined the differences in progression free survival (PFS) and in tumor volumes at progression (Vprog) by three definitions of progression. Methods: In a consecutive cohort of 73 patients with newly-diagnosed glioblastoma between 1/1/2012 and 31/12/2013, progression was established according to three definitions. We determined (1) earliest radiological progression (ERP) by retrospective multidisciplinary consensus review using all available imaging and follow-up, (2) clinical practice progression (CPP) from multidisciplinary tumor board conclusions, and (3) progression by the RANO-criteria. Results: ERP was established in 63 (86%), CPP in 64 (88%), RANO progression in 42 (58%). Of the 63 patients who had died, 37 (59%) did with prior RANO-progression, compared to 57 (90%) for both ERP and CPP. The median overall survival was 15.3 months. The median PFS was 8.8 months for ERP, 9.5 months for CPP, and 11.8 months for RANO. The PFS by ERP was shorter than CPP (HR 0.57, 95% CI 0.38–0.84, p = 0.004) and RANO-progression (HR 0.29, 95% CI 0.19–0.43, p < 0.001). The Vprog were significantly smaller for ERP (median 8.8 mL), than for CPP (17 mL) and RANO (22 mL). Conclusion: PFS and Vprog vary considerably between progression definitions. Earliest radiological progression by retrospective consensus review should be considered to accurately localize progression and to address confounding of lead time bias in clinical trial enrollment

Comparing glioblastoma surgery decisions between teams using brain maps of tumor locations, biopsies, and resections

PURPOSE The aim of glioblastoma surgery is to maximize the extent of resection while preserving functional integrity, which depends on the location within the brain. A standard to compare these decisions is lacking. We present a volumetric voxel-wise method for direct comparison between two multidisciplinary teams of glioblastoma surgery decisions throughout the brain. METHODS Adults undergoing first-time glioblastoma surgery from 2012 to 2013 performed by two neurooncologic teams were included. Patients had had a diagnostic biopsy or resection. Preoperative tumors and postoperative residues were segmented on magnetic resonance imaging in three dimensions and registered to standard brain space. Voxel-wise probability maps of tumor location, biopsy, and resection were constructed for each team to compare patient referral bias, indication variation, and treatment variation. To evaluate the quality of care, subgroups of differentially resected brain regions were analyzed for survival and functional outcome. RESULTS One team included 101 patients, and the other included 174; 91 tumors were biopsied, and 181 were resected. Patient characteristics were largely comparable between teams. Distributions of tumor locations were dissimilar, suggesting referral bias. Distributions of biopsies were similar, suggesting absence of indication variation. Differentially resected regions were identified in the anterior limb of the right internal capsule and the right caudate nucleus, indicating treatment variation. Patients with (n = 12) and without (n = 6) surgical removal in these regions had similar overall survival and similar permanent neurologic deficits. CONCLUSION Probability maps of tumor location, biopsy, and resection provide additional information that can inform surgical decision making across multidisciplinary teams for patients with glioblastoma

Between-hospital variation in time to glioblastoma surgery: a report from the Quality Registry Neuro Surgery in the Netherlands

OBJECTIVE: Patients with glioblastoma are often scheduled for urgent elective surgery. Currently, the impact of the waiting period until glioblastoma surgery is undetermined. In this national quality registry study, the authors determined the wait times until surgery for patients with glioblastoma, the risk factors associated with wait times, and the risk-standardized variation in time to surgery between Dutch hospitals. The associations between time to surgery and patient outcomes were also explored. METHODS: Data from all 4589 patients who underwent first-time glioblastoma surgery between 2014 and 2019 in the Netherlands were collected by 13 hospitals in the Quality Registry Neuro Surgery. Time to surgery comprised 1) the time from first MR scan to surgery (MTS), and 2) the time from first neurosurgical consultation to surgery (CTS). Long MTS was defined as more than 21 days and long CTS as more than 14 days. Potential risk factors were analyzed in multivariable logistic regression models. The standardized rate of long time to surgery was analyzed using funnel plots. Patient outcomes including Karnofsky Performance Scale (KPS) score change, complications, and survival were analyzed by multivariable logistic regression and proportional hazards models. RESULTS: The median overall MTS and CTS were 18 and 9 days, respectively. Overall, 2576 patients (56%) had an MTS within 3 weeks and 3069 (67%) had a CTS within 2 weeks. Long MTS was significantly associated with older age, higher preoperative KPS score, higher American Society of Anesthesiologists comorbidity class, season, lower hospital case volume, university affiliation, and resection. Long CTS was significantly associated with higher baseline KPS score, university affiliation, resection, more recent year of treatment, and season. In funnel plots, considerable practice variation was observed between hospitals in patients with long times to surgery. Fewer patients with KPS score improvement were observed after a long time until resection. Long CTS was associated with longer survival. Complications and KPS score decline were not associated with time to surgery. CONCLUSIONS: Considerable between-hospital variation among Dutch hospitals was observed in the time to glioblastoma surgery. A long time to resection impeded KPS score improvement, and therefore, patients who may improve should be identified for more urgent resection. Longer survival was observed in patients selected for longer time until surgery after neurosurgical consultation (CTS)

Between-hospital variation in time to glioblastoma surgery: a report from the Quality Registry Neuro Surgery in the Netherlands

OBJECTIVE Patients with glioblastoma are often scheduled for urgent elective surgery. Currently, the impact of the waiting period until glioblastoma surgery is undetermined. In this national quality registry study, the authors determined the wait times until surgery for patients with glioblastoma, the risk factors associated with wait times, and the risk-standardized variation in time to surgery between Dutch hospitals. The associations between time to surgery and patient outcomes were also explored. METHODS Data from all 4589 patients who underwent first-time glioblastoma surgery between 2014 and 2019 in the Netherlands were collected by 13 hospitals in the Quality Registry Neuro Surgery. Time to surgery comprised 1) the time from first MR scan to surgery (MTS), and 2) the time from first neurosurgical consultation to surgery (CTS). Long MTS was defined as more than 21 days and long CTS as more than 14 days. Potential risk factors were analyzed in multivariable logistic regression models. The standardized rate of long time to surgery was analyzed using funnel plots. Patient outcomes including Karnofsky Performance Scale (KPS) score change, complications, and survival were analyzed by multivariable logistic regression and proportional hazards models. RESULTS The median overall MTS and CTS were 18 and 9 days, respectively. Overall, 2576 patients (56%) had an MTS within 3 weeks and 3069 (67%) had a CTS within 2 weeks. Long MTS was significantly associated with older age, higher preoperative KPS score, higher American Society of Anesthesiologists comorbidity class, season, lower hospital case volume, university affiliation, and resection. Long CTS was significantly associated with higher baseline KPS score, university affiliation, resection, more recent year of treatment, and season. In funnel plots, considerable practice variation was observed between hospitals in patients with long times to surgery. Fewer patients with KPS score improvement were observed after a long time until resection. Long CTS was associated with longer survival. Complications and KPS score decline were not associated with time to surgery. CONCLUSIONS Considerable between-hospital variation among Dutch hospitals was observed in the time to glioblastoma surgery. A long time to resection impeded KPS score improvement, and therefore, patients who may improve should be identified for more urgent resection. Longer survival was observed in patients selected for longer time until surgery after neurosurgical consultation (CTS)