Frameless stereotactic biopsy for precision neurosurgery : diagnostic value, safety, and accuracy

BACKGROUND: Stereotactic biopsy is consistently employed to characterize cerebral lesions in patients who are not suitable for microsurgical resection. In the past years, technical improvement and neuroimaging advancements contributed to increase the diagnostic yield, the safety, and the application of this procedure. Currently, in addition to histological diagnosis, the molecular analysis is considered essential in the diagnostic process to properly select therapeutic and prognostic algorithms in a personalized approach. The present study reports our experience with frameless stereotactic brain biopsy in this molecular era. METHODS: One hundred forty consecutive patients treated from January 2013 to September 2018 were analyzed. Biopsies were performed using the Brainlab Varioguide\uae frameless stereotactic system. Patients' clinical and demographic data, the time of occupation of the operating room, the surgical time, the morbidity, and the diagnostic yield in providing a histological and molecular diagnosis were recorded and evaluated. RESULTS: The overall diagnostic yield was 93.6% with nine procedures resulting non-diagnostic. Among 110 patients with glioma, the IDH-1 mutational status was characterized in 108 cases (98.2%), resulting wild-type in all subjects but 3; MGMT methylation was characterized in 96 cases (87.3%), resulting present in 60 patients, and 1p/19q codeletion was founded in 6 of the 20 cases of grade II-III gliomas analyzed. All the specimens were apt for molecular analysis when performed. Bleeding requiring surgical drainage occurred in 2.1% of the cases; 8 (5.7%) asymptomatic hemorrhages requiring no treatment were observed. No biopsy-related mortality was recorded. Median length of hospital stay was 5 days (IQR 4-8) with mean surgical time of 60.77 min (\ub1\u200923.12) and 137.44\u2009\ub1\u200924.1 min of total occupation time of the operative room. CONCLUSIONS: Stereotactic frameless biopsy is a safe, feasible, and fast procedure to obtain a histological and molecular diagnosis

Preserving Visual Functions During Gliomas Resection : Feasibility and Efficacy of a Novel Intraoperative Task for Awake Brain Surgery

Objective: The intraoperative identification and preservation of optic radiations (OR) during tumor resection requires the patient to be awake. Different tasks are used. However, they do not grant the maintenance of foveal vision during all testing, limiting the ability to constantly monitor the peripheral vision and to inform about the portion of the peripheral field that is encountered. Although hemianopia can be prevented, quadrantanopia cannot be properly avoided. To overcome these limitations, we developed an intra-operative Visual field Task (iVT) to monitor the foveal vision, alerting about the likelihood of injuring the OR during task administration, and to inform about the portion of the peripheral field that is explored. Data on feasibility and efficacy in preventing visual field deficits are reported, comparing the outcome with the standard available task (Double-Picture-Naming-Task, DPNT). Methods: Patients with a temporal and/or parietal lobe tumor in close morphological relationship with the OR, or where the resection can involve the OR at any extent, without pre-operative visual-field deficits (Humphrey) were enrolled. Fifty-four patients were submitted to iVT, 38 to DPNT during awake surgery with brain mapping neurophysiological techniques. Feasibility was assessed as ease of administration, training and mapping time, and ability to alert about the loss of foveal vision. Type and location of evoked interferences were registered. Functional outcome was evaluated by manual and Humphrey test; extent of resection was recorded. Tractography was performed in a sample of patients to compare patient anatomy with intraoperative stimulation site(s). Results: The test was easy to administer and detected the loss of foveal vision in all cases. Stimulation induced visual-field interferences, detected in all patients, classified as detection or discrimination errors. Detection was mostly observed in temporal tumors, discrimination in temporo-parietal ones. Immediate visual disturbances in DPNT group were registered in 84 vs. 24% of iVT group. At 1-month Humphrey evaluation, 26% of iVT vs. 63% of DPNT had quadrantanopia (32% symptomatic); 10% of DPNT had hemianopia. EOR was similar. Detection errors were induced for stimulation of OR; discrimination also for other visual processing tract (ILF). Conclusion: iVT was feasible and sensitive to preserve the functional integrity of the OR

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

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

Preoperative Brain Tumor Imaging:Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports

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.Comment: 13 pages, 4 figures, 4 table

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.publishedVersio

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports

Ethanol enhances collective dynamics of lipid membranes

From inelastic neutron-scattering experiments and all atom molecular dynamics simulations we present evidence for a low-energy dynamical mode in the fluid phase of a 1,2-dimyristoyl-sn-glycero-3-phoshatidylcholine (DMPC) bilayer immersed in a 5% water/ethanol solution. In addition to the well-known phonon that shows a liquidlike dispersion with energies up to 4.5 meV, we observe an additional mode at smaller energies of 0.8 meV, which shows little or no dispersion. Both modes show transverse properties and might be related to molecular motion perpendicular to the bilayer