Genetic and epigenetic instability as an underlying driver of progression and aggressive behavior in IDH-mutant astrocytoma.

In recent years, the classification of adult-type diffuse gliomas has undergone a revolution, wherein specific molecular features now represent defining diagnostic criteria of IDH-wild-type glioblastomas, IDH-mutant astrocytomas, and IDH-mutant 1p/19q-codeleted oligodendrogliomas. With the introduction of the 2021 WHO CNS classification, additional molecular alterations are now integrated into the grading of these tumors, given equal weight to traditional histologic features. However, there remains a great deal of heterogeneity in patient outcome even within these established tumor subclassifications that is unexplained by currently codified molecular alterations, particularly in the IDH-mutant astrocytoma category. There is also significant intercellular genetic and epigenetic heterogeneity and plasticity with resulting phenotypic heterogeneity, making these tumors remarkably adaptable and robust, and presenting a significant barrier to the design of effective therapeutics. Herein, we review the mechanisms and consequences of genetic and epigenetic instability, including chromosomal instability (CIN), microsatellite instability (MSI)/mismatch repair (MMR) deficits, and epigenetic instability, in the underlying biology, tumorigenesis, and progression of IDH-mutant astrocytomas. We also discuss the contribution of recent high-resolution transcriptomics studies toward defining tumor heterogeneity with single-cell resolution. While intratumoral heterogeneity is a well-known feature of diffuse gliomas, the contribution of these various processes has only recently been considered as a potential driver of tumor aggressiveness. CIN has an independent, adverse effect on patient survival, similar to the effect of histologic grade and homozygous CDKN2A deletion, while MMR mutation is only associated with poor overall survival in univariate analysis but is highly correlated with higher histologic/molecular grade and other aggressive features. These forms of genomic instability, which may significantly affect the natural progression of these tumors, response to therapy, and ultimately clinical outcome for patients, are potentially measurable features which could aid in diagnosis, grading, prognosis, and development of personalized therapeutics.

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach.

Glioblastoma is a highly heterogeneous disease, with variations observed at both phenotypical and molecular levels. Personalized therapies would be facilitated by non-invasive in vivo approaches for characterizing this heterogeneity. In this study, we developed unsupervised joint machine learning between radiomic and genomic data, thereby identifying distinct glioblastoma subtypes. A retrospective cohort of 571 IDH-wildtype glioblastoma patients were included in the study, and pre-operative multi-parametric MRI scans and targeted next-generation sequencing (NGS) data were collected. L21-norm minimization was used to select a subset of 12 radiomic features from the MRI scans, and 13 key driver genes from the five main signal pathways most affected in glioblastoma were selected from the genomic data. Subtypes were identified using a joint learning approach called Anchor-based Partial Multi-modal Clustering on both radiomic and genomic modalities. Kaplan-Meier analysis identified three distinct glioblastoma subtypes: high-risk, medium-risk, and low-risk, based on overall survival outcome (p < 0.05, log-rank test; Hazard Ratio = 1.64, 95% CI 1.17-2.31, Cox proportional hazard model on high-risk and low-risk subtypes). The three subtypes displayed different phenotypical and molecular characteristics in terms of imaging histogram, co-occurrence of genes, and correlation between the two modalities. Our findings demonstrate the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities can aid in better understanding the molecular basis of phenotypical signatures of glioblastoma, and provide insights into the biological underpinnings of tumor formation and progression.

Repeated peripheral infusions of anti-EGFRvIII CAR T cells in combination with pembrolizumab show no efficacy in glioblastoma: a phase 1 trial.

We previously showed that chimeric antigen receptor (CAR) T-cell therapy targeting epidermal growth factor receptor variant III (EGFRvIII) produces upregulation of programmed death-ligand 1 (PD-L1) in the tumor microenvironment (TME). Here we conducted a phase 1 trial (NCT03726515) of CAR T-EGFRvIII cells administered concomitantly with the anti-PD1 (aPD1) monoclonal antibody pembrolizumab in patients with newly diagnosed, EGFRvIII+ glioblastoma (GBM) (n = 7). The primary outcome was safety, and no dose-limiting toxicity was observed. Secondary outcomes included median progression-free survival (5.2 months; 90% confidence interval (CI), 2.9-6.0 months) and median overall survival (11.8 months; 90% CI, 9.2-14.2 months). In exploratory analyses, comparison of the TME in tumors harvested before versus after CAR + aPD1 administration demonstrated substantial evolution of the infiltrating myeloid and T cells, with more exhausted, regulatory, and interferon (IFN)-stimulated T cells at relapse. Our study suggests that the combination of CAR T cells and PD-1 inhibition in GBM is safe and biologically active but, given the lack of efficacy, also indicates a need to consider alternative strategies.

Mismatch repair protein mutations in isocitrate dehydrogenase (IDH)-mutant astrocytoma and IDH-wild-type glioblastoma.

Background: Mutations in mismatch repair (MMR) genes (MSH2, MSH6, MLH1, and PMS2) are associated with microsatellite instability and a hypermutator phenotype in numerous systemic cancers, and germline MMR mutations have been implicated in multi-organ tumor syndromes. In gliomas, MMR mutations can function as an adaptive response to alkylating chemotherapy, although there are well-documented cases of germline and sporadic mutations, with detrimental effects on patient survival. Methods: The clinical, pathologic, and molecular features of 18 IDH-mutant astrocytomas and 20 IDH-wild-type glioblastomas with MMR mutations in the primary tumor were analyzed in comparison to 361 IDH-mutant and 906 IDH-wild-type tumors without MMR mutations. In addition, 12 IDH-mutant astrocytomas and 18 IDH-wild-type glioblastomas that developed MMR mutations between initial presentation and tumor recurrence were analyzed in comparison to 50 IDH-mutant and 104 IDH-wild-type cases that remained MMR-wild-type at recurrence. Results: In both IDH-mutant astrocytoma and IDH-wild-type glioblastoma cohorts, the presence of MMR mutation in primary tumors was associated with significantly higher tumor mutation burden (TMB) (P < .0001); however, MMR mutations only resulted in worse overall survival in the IDH-mutant astrocytomas (P = .0069). In addition, gain of MMR mutation between the primary and recurrent surgical specimen occurred more frequently with temozolomide therapy (P = .0073), and resulted in a substantial increase in TMB (P < .0001), higher grade (P = .0119), and worse post-recurrence survival (P = .0022) in the IDH-mutant astrocytoma cohort. Conclusions: These results suggest that whether present initially or in response to therapy, MMR mutations significantly affect TMB but appear to only influence the clinical outcome in IDH-mutant astrocytoma subsets.

Assessment of treatment response to dendritic cell vaccine in patients with glioblastoma using a multiparametric MRI-based prediction model.

PURPOSE: Autologous tumor lysate-loaded dendritic cell vaccine (DCVax-L) is a promising treatment modality for glioblastomas. The purpose of this study was to investigate the potential utility of multiparametric MRI-based prediction model in evaluating treatment response in glioblastoma patients treated with DCVax-L. METHODS: Seventeen glioblastoma patients treated with standard-of-care therapy + DCVax-L were included. When tumor progression (TP) was suspected and repeat surgery was being contemplated, we sought to ascertain the number of cases correctly classified as TP + mixed response or pseudoprogression (PsP) from multiparametric MRI-based prediction model using histopathology/mRANO criteria as ground truth. Multiparametric MRI model consisted of predictive probabilities (PP) of tumor progression computed from diffusion and perfusion MRI-derived parameters. A comparison of overall survival (OS) was performed between patients treated with standard-of-care therapy + DCVax-L and standard-of-care therapy alone (external controls). Additionally, Kaplan-Meier analyses were performed to compare OS between two groups of patients using PsP, Ki-67, and MGMT promoter methylation status as stratification variables. RESULTS: Multiparametric MRI model correctly predicted TP + mixed response in 72.7% of cases (8/11) and PsP in 83.3% (5/6) with an overall concordance rate of 76.5% with final diagnosis as determined by histopathology/mRANO criteria. There was a significant concordant correlation coefficient between PP values and histopathology/mRANO criteria (r = 0.54; p = 0.026). DCVax-L-treated patients had significantly prolonged OS than those treated with standard-of-care therapy (22.38 ± 12.8 vs. 13.8 ± 9.5 months, p = 0.040). Additionally, glioblastomas with PsP, MGMT promoter methylation status, and Ki-67 values below median had longer OS than their counterparts. CONCLUSION: Multiparametric MRI-based prediction model can assess treatment response to DCVax-L in patients with glioblastoma.

Taming Glioblastoma in "Real Time": Integrating Multimodal Advanced Neuroimaging/AI Tools Towards Creating a Robust and Therapy Agnostic Model for Response Assessment in Neuro-Oncology.

The highly aggressive nature of glioblastoma carries a dismal prognosis despite aggressive multimodal therapy. Alternative treatment regimens, such as immunotherapies, are known to intensify the inflammatory response in the treatment field. Follow-up imaging in these scenarios often mimics disease progression on conventional MRI, making accurate evaluation extremely challenging. To this end, revised criteria for assessment of treatment response in high-grade gliomas were successfully proposed by the RANO Working Group to distinguish pseudoprogression from true progression, with intrinsic constraints related to the postcontrast T1-weighted MRI sequence. To address these existing limitations, our group proposes a more objective and quantifiable "treatment agnostic" model, integrating into the RANO criteria advanced multimodal neuroimaging techniques, such as diffusion tensor imaging (DTI), dynamic susceptibility contrast-perfusion weighted imaging (DSC-PWI), dynamic contrast enhanced (DCE)-MRI, MR spectroscopy, and amino acid-based positron emission tomography (PET) imaging tracers, along with artificial intelligence (AI) tools (radiomics, radiogenomics, and radiopathomics) and molecular information to address this complex issue of treatment-related changes versus tumor progression in "real-time", particularly in the early posttreatment window. Our perspective delineates the potential of incorporating multimodal neuroimaging techniques to improve consistency and automation for the assessment of early treatment response in neuro-oncology.

CrOssing fiber Modeling in the Peritumoral Area using dMRI (COMPARI).

Visualization of fiber tracts around the tumor is critical for neurosurgical planning and preservation of crucial structural connectivity during tumor resection. Biophysical modeling approaches estimate fiber tract orientations from differential water diffusivity information of diffusion MRI. However, the presence of edema and tumor infiltration presents a challenge to visualize crossing fiber tracts in the peritumoral region. Previous approaches proposed free water modeling to compensate for the effect of water diffusivity in edema, but those methods were limited in estimating complex crossing fiber tracts. We propose a new cascaded multi-compartment model to estimate tissue microstructure in the presence of edema and pathological contaminants in the area surrounding brain tumors. In our model (COMPARI), the isotropic components of diffusion signal, including free water and hindered water, were eliminated, and the fiber orientation distribution (FOD) of the remaining signal was estimated. In simulated data, COMPARI accurately recovered fiber orientations in the presence of extracellular water. In a dataset of 23 patients with highly edematous brain tumors, the amplitudes of FOD and anisotropic index distribution within the peritumoral region were higher with COMPARI than with a recently proposed multi-compartment constrained deconvolution model. In a selected patient with metastatic brain tumor, we demonstrated COMPARI's ability to effectively model and eliminate water from the peritumoral region. The white matter bundles reconstructed with our model were qualitatively improved compared to those of other models, and allowed the identification of crossing fibers. In conclusion, the removal of isotropic components as proposed with COMPARI improved the bio-physical modeling of dMRI in edema, thus providing information on crossing fibers, thereby enabling improved tractography in a highly edematous brain tumor. This model may improve surgical planning tools to help achieve maximal safe resection of brain tumors.

Brain network mapping and glioma pathophysiology.

Adult diffuse gliomas are among the most difficult brain disorders to treat in part due to a lack of clarity regarding the anatomical origins and mechanisms of migration of the tumours. While the importance of studying networks of glioma spread has been recognized for at least 80 years, the ability to carry out such investigations in humans has emerged only recently. Here, we comprehensively review the fields of brain network mapping and glioma biology to provide a primer for investigators interested in merging these areas of inquiry for the purposes of translational research. Specifically, we trace the historical development of ideas in both brain network mapping and glioma biology, highlighting studies that explore clinical applications of network neuroscience, cells-of-origin of diffuse glioma and glioma-neuronal interactions. We discuss recent research that has merged neuro-oncology and network neuroscience, finding that the spatial distribution patterns of gliomas follow intrinsic functional and structural brain networks. Ultimately, we call for more contributions from network neuroimaging to realize the translational potential of cancer neuroscience.

Targeting the IL4 receptor with MDNA55 in patients with recurrent glioblastoma: Results of a phase IIb trial.

BACKGROUND: MDNA55 is an interleukin 4 receptor (IL4R)-targeting toxin in development for recurrent GBM, a universally fatal disease. IL4R is overexpressed in GBM as well as cells of the tumor microenvironment. High expression of IL4R is associated with poor clinical outcomes. METHODS: MDNA55-05 is an open-label, single-arm phase IIb study of MDNA55 in recurrent GBM (rGBM) patients with an aggressive form of GBM (de novo GBM, IDH wild-type, and nonresectable at recurrence) on their 1st or 2nd recurrence. MDNA55 was administered intratumorally as a single dose treatment (dose range of 18 to 240 ug) using convection-enhanced delivery (CED) with up to 4 stereo-tactically placed catheters. It was co-infused with a contrast agent (Gd-DTPA, Magnevist®) to assess distribution in and around the tumor margins. The flow rate of each catheter did not exceed 10µL/min to ensure that the infusion duration did not exceed 48 h. The primary endpoint was mOS, with secondary endpoints determining the effects of IL4R status on mOS and PFS. RESULTS: MDNA55 showed an acceptable safety profile at doses up to 240 µg. In all evaluable patients (n = 44) mOS was 11.64 months (80% one-sided CI 8.62, 15.02) and OS-12 was 46%. A subgroup (n = 32) consisting of IL4R High and IL4R Low patients treated with high-dose MDNA55 (>180 ug) showed the best benefit with mOS of 15 months, OS-12 of 55%. Based on mRANO criteria, tumor control was observed in 81% (26/32), including those patients who exhibited pseudo-progression (15/26). CONCLUSIONS: MDNA55 demonstrated tumor control and promising survival and may benefit rGBM patients when treated at high-dose irrespective of IL4R expression level.Trial Registration: Clinicaltrials.gov NCT02858895.

NCCN Guidelines® Insights: Central Nervous System Cancers, Version 2.2022.

The NCCN Guidelines for Central Nervous System (CNS) Cancers focus on management of the following adult CNS cancers: glioma (WHO grade 1, WHO grade 2-3 oligodendroglioma [1p19q codeleted, IDH-mutant], WHO grade 2-4 IDH-mutant astrocytoma, WHO grade 4 glioblastoma), intracranial and spinal ependymomas, medulloblastoma, limited and extensive brain metastases, leptomeningeal metastases, non-AIDS-related primary CNS lymphomas, metastatic spine tumors, meningiomas, and primary spinal cord tumors. The information contained in the algorithms and principles of management sections in the NCCN Guidelines for CNS Cancers are designed to help clinicians navigate through the complex management of patients with CNS tumors. Several important principles guide surgical management and treatment with radiotherapy and systemic therapy for adults with brain tumors. The NCCN CNS Cancers Panel meets at least annually to review comments from reviewers within their institutions, examine relevant new data from publications and abstracts, and reevaluate and update their recommendations. These NCCN Guidelines Insights summarize the panel's most recent recommendations regarding molecular profiling of gliomas.

Artificial intelligence-based locoregional markers of brain peritumoral microenvironment.

In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients' survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status (t test, Wilcoxon rank sum test, linear regression; p < 0.01). Clustering of patients using the proposed markers reveals distinct survival groups (logrank; p < 10-5, Cox hazard ratio = 1.82; p < 0.005). Our findings provide a panel of markers as surrogates of infiltration that might capture novel insight about underlying biology of peritumoral microstructural heterogeneity, providing potential biomarkers of prognosis pertaining to survival and molecular stratification, with applicability in clinical decision making.

Second window indocyanine green localizes CNS lymphoma in real time in the operating room: report of two cases.

Intraoperative distinction of lesional tissue versus normal brain parenchyma can be difficult in neurosurgical oncology procedures. We report the successful, real-time visualization of central nervous system (CNS) lymphoma using the 'Second Window Indocyanine Green' (SWIG) method for two patients who underwent craniotomy for pathology that was determined to be large B cell lymphoma. Indocyanine green (ICG), when administered intravenously the day prior to cranial surgery, is a re-purposed fluorophore that may afford safe, immediate visual confirmation of on-target tissue resection, thereby providing a valuable adjunct to intraoperative navigation and decreasing reliance on frozen pathology analysis. These first reported cases of SWIG for lymphoma in the CNS indicate that further study of fluorophores to improve biopsy targeting and yield is warranted.

Distinguishing Progression from Pseudoprogression in Glioblastoma Using 18F-Fluciclovine PET.

Accurate differentiation between tumor progression (TP) and pseudoprogression remains a critical unmet need in neurooncology. 18F-fluciclovine is a widely available synthetic amino acid PET radiotracer. In this study, we aimed to assess the value of 18F-fluciclovine PET for differentiating pseudoprogression from TP in a prospective cohort of patients with suspected radiographic recurrence of glioblastoma. Methods: We enrolled 30 glioblastoma patients with radiographic progression after first-line chemoradiotherapy for whom surgical resection was planned. The patients underwent preoperative 18F-fluciclovine PET and MRI. The relative percentages of viable tumor and therapy-related changes observed in histopathology were quantified and categorized as TP (≥50% viable tumor), mixed TP (<50% and >10% viable tumor), or pseudoprogression (≤10% viable tumor). Results: Eighteen patients had TP, 4 had mixed TP, and 8 had pseudoprogression. Patients with TP/mixed TP had a significantly higher 40- to 50-min SUVmax (6.64 + 1.88 vs. 4.11 ± 1.52, P = 0.009) than patients with pseudoprogression. A 40- to 50-min SUVmax cutoff of 4.66 provided 90% sensitivity and 83% specificity for differentiation of TP/mixed TP from pseudoprogression (area under the curve [AUC], 0.86). A maximum relative cerebral blood volume cutoff of 3.672 provided 90% sensitivity and 71% specificity for differentiation of TP/mixed TP from pseudoprogression (AUC, 0.779). Combining a 40- to 50-min SUVmax cutoff of 4.66 and a maximum relative cerebral blood volume of 3.67 on MRI provided 100% sensitivity and 80% specificity for differentiating TP/mixed TP from pseudoprogression (AUC, 0.95). Conclusion: 18F-fluciclovine PET uptake can accurately differentiate pseudoprogression from TP in glioblastoma, with even greater accuracy when combined with multiparametric MRI. Given the wide availability of 18F-fluciclovine, larger, multicenter studies are warranted to determine whether amino acid PET with 18F-fluciclovine should be used in the routine posttreatment assessment of glioblastoma.

Association of Autologous Tumor Lysate-Loaded Dendritic Cell Vaccination With Extension of Survival Among Patients With Newly Diagnosed and Recurrent Glioblastoma: A Phase 3 Prospective Externally Controlled Cohort Trial.

Importance: Glioblastoma is the most lethal primary brain cancer. Clinical outcomes for glioblastoma remain poor, and new treatments are needed. Objective: To investigate whether adding autologous tumor lysate-loaded dendritic cell vaccine (DCVax-L) to standard of care (SOC) extends survival among patients with glioblastoma. Design, Setting, and Participants: This phase 3, prospective, externally controlled nonrandomized trial compared overall survival (OS) in patients with newly diagnosed glioblastoma (nGBM) and recurrent glioblastoma (rGBM) treated with DCVax-L plus SOC vs contemporaneous matched external control patients treated with SOC. This international, multicenter trial was conducted at 94 sites in 4 countries from August 2007 to November 2015. Data analysis was conducted from October 2020 to September 2021. Interventions: The active treatment was DCVax-L plus SOC temozolomide. The nGBM external control patients received SOC temozolomide and placebo; the rGBM external controls received approved rGBM therapies. Main Outcomes and Measures: The primary and secondary end points compared overall survival (OS) in nGBM and rGBM, respectively, with contemporaneous matched external control populations from the control groups of other formal randomized clinical trials. Results: A total of 331 patients were enrolled in the trial, with 232 randomized to the DCVax-L group and 99 to the placebo group. Median OS (mOS) for the 232 patients with nGBM receiving DCVax-L was 19.3 (95% CI, 17.5-21.3) months from randomization (22.4 months from surgery) vs 16.5 (95% CI, 16.0-17.5) months from randomization in control patients (HR = 0.80; 98% CI, 0.00-0.94; P = .002). Survival at 48 months from randomization was 15.7% vs 9.9%, and at 60 months, it was 13.0% vs 5.7%. For 64 patients with rGBM receiving DCVax-L, mOS was 13.2 (95% CI, 9.7-16.8) months from relapse vs 7.8 (95% CI, 7.2-8.2) months among control patients (HR, 0.58; 98% CI, 0.00-0.76; P < .001). Survival at 24 and 30 months after recurrence was 20.7% vs 9.6% and 11.1% vs 5.1%, respectively. Survival was improved in patients with nGBM with methylated MGMT receiving DCVax-L compared with external control patients (HR, 0.74; 98% CI, 0.55-1.00; P = .03). Conclusions and Relevance: In this study, adding DCVax-L to SOC resulted in clinically meaningful and statistically significant extension of survival for patients with both nGBM and rGBM compared with contemporaneous, matched external controls who received SOC alone. Trial Registration: ClinicalTrials.gov Identifier: NCT00045968.

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics.

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma.

Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70-0.85)/0.75 (95% CI 0.64-0.79) and 0.75 (95% CI 0.65-0.84)/0.63 (95% CI 0.52-0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6-0.7) for clinical data improving to 0.75 (95% CI 0.72-0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.

Risk of intracranial hemorrhage with direct oral anticoagulants vs low molecular weight heparin in glioblastoma: A retrospective cohort study.

BACKGROUND: Glioblastoma (GBM) is associated with a high incidence of venous thromboembolism (VTE), but there are little data to guide anticoagulation in patients with GBM, in whom the risks of VTE must be balanced against the risk of intracranial hemorrhage (ICH). METHODS: We performed a single-institution retrospective cohort study of patients with GBM diagnosed with VTE from 2014 to 2021 who were treated with low molecular weight heparin (LMWH) or a direct oral anticoagulant (DOAC). The incidence of ICH was compared between the LMWH and DOAC groups. The primary outcome was clinically relevant ICH within the first 30 days of anticoagulation, defined as any ICH that was fatal, symptomatic, required surgical intervention, and/or led to cessation of anticoagulation. Secondary outcomes included clinically relevant ICH within 6 months, fatal ICH within 30 days and 6 months, and any bleeding within 30 days and 6 months. RESULTS: One hundred twenty-one patients were identified in the cohort for 30-day outcome analyses (DOAC, n = 33; LMWH, n = 88). For 6-month outcome analyses, the cohort included only patients who were maintained on their initial anticoagulant (DOAC, n = 32; LMWH, n = 75). The incidence of clinically relevant ICH at 30 days was 0% in the DOAC group and 9% in the LMWH group (P = .11). The cumulative incidence of clinically relevant ICH at 6 months was 0% in the DOAC group and 24% in the LMWH group (P = .001), with 4 fatal ICHs in the LMWH group. CONCLUSIONS: DOACs are associated with a lower incidence of clinically relevant ICH in patients with GBM-associated VTE compared to LMWH.

Endoscopic fenestration of a giant frontal arachnoid cyst: Operative technique and anatomical nuances.

Endoscopic fenestration is best as it is minimally invasive and does not require hardware in the surgical site (Figure 1). This case shows the safety of endoscopic fenestration and the utility of operative adjuncts (J Korean Med Sci. 1999;14:443; Neurosurg Focus. 2005;19:E7).