The importance of considering competing risks in recurrence analysis of intracranial meningioma.

BACKGROUND: The risk of recurrence is overestimated by the Kaplan-Meier method when competing events, such as death without recurrence, are present. Such overestimation can be avoided by using the Aalen-Johansen method, which is a direct extension of Kaplan-Meier that accounts for competing events. Meningiomas commonly occur in older individuals and have slow-growing properties, thereby warranting competing risk analysis. The extent to which competing events are considered in meningioma literature is unknown, and the consequences of using incorrect methodologies in meningioma recurrence risk analysis have not been investigated. METHODS: We surveyed articles indexed on PubMed since 2020 to assess the usage of competing risk analysis in recent meningioma literature. To compare recurrence risk estimates obtained through Kaplan-Meier and Aalen-Johansen methods, we applied our international database comprising ~ 8,000 patients with a primary meningioma collected from 42 institutions. RESULTS: Of 513 articles, 169 were eligible for full-text screening. There were 6,537 eligible cases from our PERNS database. The discrepancy between the results obtained by Kaplan-Meier and Aalen-Johansen was negligible among low-grade lesions and younger individuals. The discrepancy increased substantially in the patient groups associated with higher rates of competing events (older patients with high-grade lesions). CONCLUSION: The importance of considering competing events in recurrence risk analysis is poorly recognized as only 6% of the studies we surveyed employed Aalen-Johansen analyses. Consequently, most of the previous literature has overestimated the risk of recurrence. The overestimation was negligible for studies involving low-grade lesions in younger individuals; however, overestimation might have been substantial for studies on high-grade lesions.

Confounders of intraoperative frozen section pathology during glioma surgery.

Although frozen section pathology (FSP) is commonly performed during surgery for glioma-suspicious lesions, confounders of accuracy are largely unknown. FSP and final diagnosis were compared in 398 surgeries for glioma-suspicious lesions. Diagnostic accuracy, risk factors for diagnostic shift from neoplastic to non-neoplastic tissue and vice versa according to the final diagnosis, and the impact on intraoperative and postoperative decision-making were analyzed. Diagnostic shift occurred in 70 cases (18%), and sensitivity, specificity, and the positive (PPV) and negative (NPV) predictive value of FSP were 82.5%, 77.8%, 99.4%, and 9.3%, respectively. No correlations between shift and patients' age and sex, sample fluorescence or volume, tumor location, correct information on the pathology form, final high- or low-grade histology, or molecular alterations were found (p > .05, each). Shift was more common after irradiation (25% vs 15%; p = .025) or chemotherapy (26% vs 15%; p = .022) than in treatment naïve cases and correlated with the type of surgery (p = .002). FSP altered intraoperative decision-making in 25 cases (6%). Postoperative shift led to repeated surgery in 12 patients (3%). In 45 cases, in which FSP and final diagnosis based on the same tissue, shift occurred in only 5 patients (11%), and sensitivity, specificity, PPV, and NPV for FSP were 77.4%, 78.6%, 88.9%, and 61.1%, respectively. No correlations between diagnostic shift and any of the analyzed variables were found (p > .05, each). Although accuracy of FSP during glioma surgery is sufficient, moderate NPV should be considered during intraoperative decision-making. While confounders are sparse, accuracy might be increased by repeated sampling. Diagnostic shift rarely alters postoperative treatment strategy.

Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas.

PURPOSE: In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment. METHODS: Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting. RESULTS: A total of 117 image sets including training (N = 94) and test data (N = 23) were analyzed. To eliminate random effects and demonstrate the robustness of our approach, data partitioning and subsequent model development and testing were repeated a total of 100 times (each time with repartitioned training and independent test data). The established five- and eight-feature models with both fixed and different radiomics features enabled the prediction of TERT with similar but excellent performance. The five-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 91.8%/94.3%, mean accuracy of 85.5%/88.9%, mean sensitivity of 88.6%/91.4%, mean specificity of 83.2%/87.0%, and a mean Cohen's Kappa of 71.0%/77.7%. The eight-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 92.7%/94.6%, mean accuracy of 87.3%/88.9%, mean sensitivity of 89.6%/90.6%, mean specificity of 85.5%/87.5%, and a mean Cohen's Kappa of 74.4%/77.6%. Of note, the addition of further features of up to N = 8 only slightly increased the performance. CONCLUSIONS: Radiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.

Molecular predictors for decitabine efficacy in meningiomas - a pilot study.

PURPOSE: Effective chemotherapeutical agents for the treatment of meningiomas are still lacking. Previous in-vitro analyses revealed efficacy of decitabine (DCT), a DNA methyltransferase (DNMT) inhibitor established in the treatment of leukemia, in a yet undefined subgroup of meningiomas. METHODS: Effects of DCT on proliferation and viability was analyzed in primary meningioma cells by immunofluorescence and MTT assays, and cases were classified as drug responders and non-responders. Molecular preconditions for efficacy were analyzed using immunofluorescence for Ki67, DNMT1, and five oncogenes (TRIM58, FAM84B, ELOVL2, MAL2, LMO3) previously found to be differentially methylated after DCT exposition, as well as by genome-wide DNA methylation analyses. RESULTS: Efficacy of DCT (10µM) was found in eight (62%) of 13 meningioma cell lines 48 h after drug exposition (p < .05). DCT significantly reduced DNMT1 expression in all but two cell lines, and median ΔDNMT1 reduction 48 h after drug exposition was lower in DCT-resistant (-11.1%) than in DCT-sensitive (-50.5%, p = .030) cells. Rates of cell lines responsive to DCT exposition distinctly decreased to 25% after 72 h. No significant correlation of the patients´ age, sex, histological subtype, location of the paternal tumor, expression of Ki67, DNMT1 or the analyzed oncogenes with treatment response was found (p > .05, each). DCT efficacy was further independent of the methylation class and global DNA methylation of the paternal tumor. CONCLUSION: Early effects of DCT in meningiomas are strongly related with DNMT1 expression, while clinical, histological, and molecular predictors for efficacy are sparse. Kinetics of drug efficacy might indicate necessity of repeated exposition and encourage further analyses.

Iatrogenic lumbosacral infiltration with petroleum (hydrodesulfurized heavy) with secondary intrathecal distribution-a case report.

Petroleum is commonly used as a solvent, and primary intrathecal administration or secondary diffusion and subsequent clinical management has not been reported. We report the case of a male patient with intrathecal petroleum diffusion following accidental lumbar infiltration. After the onset of secondary myeloencephalopathy with coma and tetraparesis, continuous cranio-lumbar irrigation using an external ventricular and a lumbar drain was established. Cranial imaging revealed distinct supra- and infratentorial alterations. The patient improved slowly and was referred to rehabilitation. Intrathecal petroleum leads to myeloencephalopathy and continuous cranio-lumbar irrigation might be a safe treatment option.

Prognosis and histology of sporadic synchronous and metachronous meningiomas and comparative analyses with singular lesions.

Synchronous or metachronous growth of multiple tumors (≥ 2) is found in up to 20% of meningioma patients. However, biological as well as histological features and prognosis are largely unexplored. Clinical and histological characteristics were retrospectively investigated in 95 patients harboring 226 multiple meningiomas (MMs) and compared with 135 cases of singular meningiomas (SM) using uni- and multivariate analyses. In MM, tumors occurred synchronously and metachronously in 62% and 38%, respectively. WHO grade was intra-individually constant in all but two MMs, and histological subtype varied in 13% of grade 1 tumors. MM occurred more commonly in convexity/parasagittal locations, while SM were more frequent at the skull base (p < .001). In univariate analyses, gross total resection (p = .014) and high-grade histology in MM were associated with a prolonged time to progression (p < .001). Most clinical characteristics and rates of high-grade histology were similar in both groups (p ≥ .05, each). Multivariate analyses showed synchronous/metachronous meningioma growth (HR 4.50, 95% CI 2.26-8.96; p < .001) as an independent predictor for progression. Compared to SM, risk of progression was similar in cases with two (HR 1.56, 95% CI .76-3.19; p = .224), but exponentially raised in patients with 3-4 (HR 3.25, 1.22-1.62; p = .018) and ≥ 5 tumors (HR 13.80, 4.06-46.96; p < .001). Clinical and histological characteristics and risk factors for progression do not relevantly differ between SM and MM. Although largely constant, histology and WHO grade occasionally intra-individually vary in MM. A distinctly higher risk of disease progression in MM as compared to SM might reflect different underlying molecular alterations.

Protoporphyrin IX (PpIX) Fluorescence during Meningioma Surgery: Correlations with Histological Findings and Expression of Heme Pathway Molecules.

Background: The usefulness of 5-ALA-mediated fluorescence-guided resection (FGR) in meningiomas is controversial, and information on the molecular background of fluorescence is sparse. Methods: Specimens obtained during 44 FGRs of intracranial meningiomas were analyzed for the presence of tumor tissue and fluorescence. Protein/mRNA expression of key transmembrane transporters/enzymes involved in PpIX metabolism (ABCB6, ABCG2, FECH, CPOX) were investigated using immunohistochemistry/qPCR. Results: Intraoperative fluorescence was observed in 70 of 111 specimens (63%). No correlation was found between fluorescence and the WHO grade (p = 0.403). FGR enabled the identification of neoplastic tissue (sensitivity 84%, specificity 67%, positive and negative predictive value of 86% and 63%, respectively, AUC: 0.75, p < 0.001), and was improved in subgroup analyses excluding dura specimens (86%, 88%, 96%, 63% and 0.87, respectively; p < 0.001). No correlation was found between cortical fluorescence and tumor invasion (p = 0.351). Protein expression of ABCB6, ABCG2, FECH and CPOX was found in meningioma tissue and was correlated with fluorescence (p < 0.05, each), whereas this was not confirmed for mRNA expression. Aberrant expression was observed in the CNS. Conclusion: FGR enables the intraoperative identification of meningioma tissue with limitations concerning dura invasion and due to ectopic expression in the CNS. ABCB6, ABCG2, FECH and CPOX are expressed in meningioma tissue and are related to fluorescence.

A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma.

The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen's Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms.

Risk factors for preoperative seizures in intracranial meningiomas.

BACKGROUND: About 25% of patients with intracranial meningioma display seizures at the time of initial presentation. Hence, identification of risk factors for preoperative seizures is crucial during perioperative care of meningioma patients. METHODS: Associations of preoperative seizures with clinical, radiological and histological variables were analyzed in 945 patients (689 females, 73% and 256 males, 27%; median age: 58 years) who underwent surgery for primary diagnosed intracranial meningioma. RESULTS: Preoperative seizures were found in 189 patients (20%). In univariate analyses, male gender (OR=1.91, 95% CI: 1.37-2.68; P<0.001), grade II/III histology (OR=2.24, 95% CI: 1.46-3.46; P<0.001), brain invasion (OR=2.59, 95% CI: 1.45-4.63; P=001), non-skull base tumor location (OR=3.07, 95% CI: 2.13-4.41; P<0.001), heterogeneous contrast-enhancement (OR=1.60, 95% CI: 1.04-2.46; P=0.031), intratumoral calcifications (OR=1.91, 95% CI: 1.17-3.10; P=0.009), an irregular shape (OR=2.07, 95% CI: 1.32-3.26; P=0.002) as well as tumor (OR=1.01 per ccm, 95% CI: 1.00-1.02; P=0.001) and edema volumes (OR=1.01 per ccm, 95% CI: 1.00-1.01; P<0.001) were correlated with seizures. Semiology was not related to any of the analyzed variables (P>0.05, each). No associations were found between seizures and histological subtype of 832 grade I meningiomas (P=0.391). In multivariate analyses, only non-skull base tumor location (OR=3.12, 95% CI: 1.74-5.59; P<0.001) and a rising peritumoral edema volume (OR=1.01 per ccm, 95% CI: 1.00-1.01; P<0.001) were identified as independent predictors for preoperative seizures. CONCLUSIONS: Several mostly radiological variables were identified as risk factors for epilepsy. However, multivariate analyses confirmed only peritumoral edema and non-skull base tumor location as independent predictors for preoperative seizures. None of the variables predicts semiology.

Can Currently Suggested Quality Indicators Be Transferred to Meningioma Surgery?-A Single-Center Pilot Study.

BACKGROUND: Risk stratification based on standardized quality measures has become crucial in neurosurgery. Contemporary quality indicators have often been developed for a wide range of neurosurgical procedures collectively. The accuracy of tumor-inherent characteristics of patients diagnosed with meningioma remains questionable. The objective of this study was the analysis of currently applied quality indicators in meningioma surgery and the identification of potential new measures. METHODS: Data of 133 patients who were operated on due to intracranial meningiomas were subjected to a retrospective analysis. The primary outcomes of interest were classical quality indicators such as the 30-day readmission, 30-day reoperation, 30-day mortality, 30-day nosocomial infection, and the 30-day surgical site infection rate. Uni- and multivariate analyses were performed. The occurrence of a new postoperative neurologic deficit was analyzed as a potential new quality indicator. RESULTS: The overall unplanned readmission rate was 3.8%; 13 patients were reoperated within 30 days (9.8%). The 30-day nosocomial infection and surgical site infection rates were 6.8 and 1.5%, respectively. A postoperative new neurologic deficit or neurologic deterioration as a currently assessed quality feature was observed in 12 patients (9.2%). The edema volume on preoperative scans proved to have a significant impact on the occurrence of a new postoperative neurologic deficit (p = 0.023). CONCLUSIONS: Classical quality indicators in neurosurgery have proved to correlate with considerable deterioration of the patient's health in meningioma surgery and thus should be taken into consideration for application in meningioma patients. The occurrence of a new postoperative neurologic deficit is common and procedure specific. Thus, this should be elucidated for application as a complementary quality indicator in meningioma surgery.

Intratumor heterogeneity and T cell exhaustion in primary CNS lymphoma.

BACKGROUND: Primary central nervous system lymphoma (PCNSL) is a rare lymphoma of the central nervous system, usually of diffuse large B cell phenotype. Stereotactic biopsy followed by histopathology is the diagnostic standard. However, limited material is available from CNS biopsies, thus impeding an in-depth characterization of PCNSL. METHODS: We performed flow cytometry, single-cell RNA sequencing, and B cell receptor sequencing of PCNSL cells released from biopsy material, blood, and cerebrospinal fluid (CSF), and spatial transcriptomics of biopsy samples. RESULTS: PCNSL-released cells were predominantly activated CD19+CD20+CD38+CD27+ B cells. In single-cell RNA sequencing, PCNSL cells were transcriptionally heterogeneous, forming multiple malignant B cell clusters. Hyperexpanded B cell clones were shared between biopsy- and CSF- but not blood-derived cells. T cells in the tumor microenvironment upregulated immune checkpoint molecules, thereby recognizing immune evasion signals from PCNSL cells. Spatial transcriptomics revealed heterogeneous spatial organization of malignant B cell clusters, mirroring their transcriptional heterogeneity across patients, and pronounced expression of T cell exhaustion markers, co-localizing with a highly malignant B cell cluster. CONCLUSIONS: Malignant B cells in PCNSL show transcriptional and spatial intratumor heterogeneity. T cell exhaustion is frequent in the PCNSL microenvironment, co-localizes with malignant cells, and highlights the potential of personalized treatments.

Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology.

To investigate the applicability and performance of automated machine learning (AutoML) for potential applications in diagnostic neuroradiology. In the medical sector, there is a rapidly growing demand for machine learning methods, but only a limited number of corresponding experts. The comparatively simple handling of AutoML should enable even non-experts to develop adequate machine learning models with manageable effort. We aim to investigate the feasibility as well as the advantages and disadvantages of developing AutoML models compared to developing conventional machine learning models. We discuss the results in relation to a concrete example of a medical prediction application. In this retrospective IRB-approved study, a cohort of 107 patients who underwent gross total meningioma resection and a second cohort of 31 patients who underwent subtotal resection were included. Image segmentation of the contrast enhancing parts of the tumor was performed semi-automatically using the open-source software platform 3D Slicer. A total of 107 radiomic features were extracted by hand-delineated regions of interest from the pre-treatment MRI images of each patient. Within the AutoML approach, 20 different machine learning algorithms were trained and tested simultaneously. For comparison, a neural network and different conventional machine learning algorithms were trained and tested. With respect to the exemplary medical prediction application used in this study to evaluate the performance of Auto ML, namely the pre-treatment prediction of the achievable resection status of meningioma, AutoML achieved remarkable performance nearly equivalent to that of a feed-forward neural network with a single hidden layer. However, in the clinical case study considered here, logistic regression outperformed the AutoML algorithm. Using independent test data, we observed the following classification results (AutoML/neural network/logistic regression): mean area under the curve = 0.849/0.879/0.900, mean accuracy = 0.821/0.839/0.881, mean kappa = 0.465/0.491/0.644, mean sensitivity = 0.578/0.577/0.692 and mean specificity = 0.891/0.914/0.936. The results obtained with AutoML are therefore very promising. However, the AutoML models in our study did not yet show the corresponding performance of the best models obtained with conventional machine learning methods. While AutoML may facilitate and simplify the task of training and testing machine learning algorithms as applied in the field of neuroradiology and medical imaging, a considerable amount of expert knowledge may still be needed to develop models with the highest possible discriminatory power for diagnostic neuroradiology.

Assessing preoperative risk of STR in skull meningiomas using MR radiomics and machine learning.

Our aim is to predict possible gross total and subtotal resections of skull meningiomas from pre-treatment T1 post contrast MR-images using radiomics and machine learning in a representative patient cohort. We analyse the accuracy of our model predictions depending on the tumor location within the skull and the postoperative tumor volume. In this retrospective, IRB-approved study, image segmentation of the contrast enhancing parts of the tumor was semi-automatically performed using the 3D Slicer open-source software platform. Imaging data were split into training data and independent test data at random. We extracted a total of 107 radiomic features by hand-delineated regions of interest on T1 post contrast MR images. Feature preselection and model construction were performed with eight different machine learning algorithms. Each model was estimated 100 times on new training data and then tested on a previously unknown, independent test data set to avoid possible overfitting. Our cohort included 138 patients. A gross total resection of the meningioma was performed in 107 cases and a subtotal resection in the remaining 31 cases. Using the training data, the mean area under the curve (AUC), mean accuracy, mean kappa, mean sensitivity and mean specificity were 0.901, 0.875, 0.629, 0.675 and 0.933 respectively. We obtained very similar results with the independent test data: mean AUC = 0.900, mean accuracy = 0.881, mean kappa = 0.644, mean sensitivity = 0.692 and mean specificity = 0.936. Thus, our model exposes good and stable predictive performance with both training and test data. Our radiomics approach shows that with machine learning algorithms and comparatively few explanatory factors such as the location of the tumor within the skull as well as its shape, it is possible to make accurate predictions about whether a meningioma can be completely resected by surgery. Complete resections and resections with larger postoperative tumor volumes can be predicted with very high accuracy. However, cases with very small postoperative tumor volumes are comparatively difficult to predict correctly.

Expression of decitabine-targeted oncogenes in meningiomas in vivo.

Treatment of meningiomas refractory to surgery and irradiation is challenging and effective chemotherapies are still lacking. Recently, in vitro analyses revealed decitabine (DCT, 5-aza-2'-deoxycytidine) to be effective in high-grade meningiomas and, moreover, to induce hypomethylation of distinct oncogenes only sparsely described in meningiomas in vivo yet.Expression of the corresponding onco- and tumor suppressor genes TRIM58, FAM84B, ELOVL2, MAL2, LMO3, and DIO3 were analyzed and scored by immunohistochemical staining and RT-PCR in samples of 111 meningioma patients. Correlations with clinical and histological variables and prognosis were analyzed in uni- and multivariate analyses.All analyzed oncogenes were highly expressed in meningiomas. Expression scores of TRIM58 tended to be higher in benign than in high-grade tumors 20 vs 16 (p = .002) and all 9 samples lacking TRIM58 expression displayed WHO grade II/III histology. In contrast, median expression scores for both FAM84B (6 vs 4, p ≤ .001) and ELOVL2 (9 vs 6, p < .001) were increased in high-grade as compared to benign meningiomas. DIO3 expression was distinctly higher in all analyzed samples as compared to the reference decitabine-resistant Ben-Men 1 cell line. Increased ELOVL2 expression (score ≥ 8) correlated with tumor relapse in both uni- (HR: 2.42, 95%CI 1.18-4.94; p = .015) and multivariate (HR: 2.09, 95%CI 1.01-4.44; p = .046) analyses.All oncogenes involved in DCT efficacy in vitro are also widely expressed in vivo, and expression is partially associated with histology and prognosis. These results strongly encourage further analyses of DCT efficiency in meningiomas in vitro and in situ.

Blockade of inhibitory killer cell immunoglobulin-like receptors and IL-2 triggering reverses the functional hypoactivity of tumor-derived NK-cells in glioblastomas.

Killer cell immunoglobulin-like receptors (KIRs) comprise a group of highly polymorphic inhibitory receptors which are specific for classical HLA class-I molecules. Peripheral blood and freshly prepared tumor cell suspensions (n = 60) as well as control samples (n = 32) were investigated for the distribution, phenotype, and functional relevance of CD158ab/KIR2DL1,-2/3 expressing NK-cells in glioblastoma (GBM) patients. We found that GBM were scarcely infiltrated by NK-cells that preferentially expressed CD158ab/KIR2DL1,-2/3 as inhibitory receptors, displayed reduced levels of the activating receptors CD335/NKp46, CD226/DNAM-1, CD159c/NKG2C, and showed diminished capacity to produce IFN-γ and perforin. Functional hypoactivity of GBM-derived NK-cells persisted despite IL-2 preactivation. Blockade with a specific KIR2DL-1,2/3 monoclonal antibody reversed NK-cell inhibition and significantly enhanced degranulation and IFN-γ production of IL-2 preactivated NK-cells in the presence of primary GBM cells and HLA-C expressing but not HLA class-I deficient K562 cells. Additional analysis revealed that significant amounts of IL-2 could be produced by tumor-derived CD4+ and CD8+CD45RA- memory T-cells after combined anti-CD3/anti-CD28 stimulation. Our data indicate that both blockade of inhibitory KIR and IL-2 triggering of tumor-derived NK-cells are necessary to enhance NK-cell responsiveness in GBM.

Combined Fluorescence-Guided Resection and Intracavitary Thermotherapy with Superparamagnetic Iron-Oxide Nanoparticles for Recurrent High-Grade Glioma: Case Series with Emphasis on Complication Management.

BACKGROUND: Concepts improving local tumor control in high-grade glioma (HGG) are desperately needed. The aim of this study is to report an extended series of cases treated with a combination of 5-ALA-fluorescence-guided resection (FGR) and intracavitary thermotherapy with superparamagnetic iron oxide nanoparticles (SPION). METHODS: We conducted a single-center retrospective review of all recurrent HGG treated with FGR and intracavitary thermotherapy (n = 18). Patients underwent six hyperthermia sessions in an alternating magnetic field and received additional adjuvant therapies on a case-by-case basis. RESULTS: Nine patients were treated for first tumor recurrence; all other patients had suffered at least two recurrences. Nine patients received combined radiotherapy and thermotherapy. The median progression-free survival was 5.5 (95% CI: 4.67-6.13) months and median overall survival was 9.5 (95% CI: 7.12-11.79) months. No major side effects were observed during active treatment. Thirteen patients (72%) developed cerebral edema and more clinical symptoms during follow-up and were initially treated with dexamethasone. Six (33%) of these patients underwent surgical removal of nanoparticles due to refractory edema. CONCLUSIONS: The combination of FGR and intracavitary thermotherapy with SPION provides a new treatment option for improving local tumor control in recurrent HGG. The development of cerebral edema is a major issue requiring further refinements of the treatment protocol.

Molecular neuropathology of brain-invasive meningiomas.

Invasion of brain tissue by meningiomas has been identified as one key factor for meningioma recurrence. The identification of meningioma tumor tissue surrounded by brain tissue in neurosurgical samples has been touted as a criterion for atypical meningioma (CNS WHO grade 2), but is only rarely seen in the absence of other high-grade features, with brain-invasive otherwise benign (BIOB) meningiomas remaining controversial. While post-surgery irradiation therapy might be initiated in brain-invasive meningiomas to prevent recurrences, specific treatment approaches targeting key molecules involved in the invasive process are not established. Here we have compiled the current knowledge about mechanisms supporting brain tissue invasion by meningiomas and summarize preclinical models studying targeted therapies with potential inhibitory effects.

Classical and disease-specific quality indicators in glioma surgery-Development of a quality checklist to improve treatment quality in glioma patients.

BACKGROUND: There is a pressing demand for more accurate, disease-specific quality measures in the field of neurosurgery. Aiming at most adequately measuring and reflecting the quality of glioma therapy, we developed a novel quality indicator bundle in form of a checklist for all patients that are treated operatively for glioma. METHODS: On the basis of possible glioma-specific quality indicators retrieved from the literature and quality guidelines, a multidisciplinary team developed a checklist containing 13 patient-need-specific outcome measures. Subsequently, the checklist was prospectively applied to a total of 78 patients compared with a control group consisting of 322 patients. A score was generated based on the maximum of quality measures achieved. RESULTS: Significant improvements in quality after prospectively introducing the checklist were achieved for supplemental physical and occupational therapy during inpatient stay (89.4% vs 100%, P = .002), consultation of a social worker during inpatient stay (64% vs 92.3%, P < .001), psycho-oncological screening (14.3% vs 70.5%, P < .001), psycho-oncological consultation (31.1% vs 82.1%, P < .001), and consultation of the palliative care team (20% vs 40%, P = .031). Overall, after introduction of the checklist one-third (n = 23) of patients reached best-practice measures in all categories, and over half of the patients (n = 44) achieved above 90% with respect to the outcome measures. CONCLUSIONS: Aiming at ensuring comprehensive, consistent, and timely care of glioma patients, the implementation of the checklist for routine use in glioma surgery represents an efficient, easily reproducible, and powerful tool for significant improvements.