Tumor-brain Interface Research Articles

Texture feature differentiation of glioblastoma and solitary brain metastases based on tumor and tumor-brain interface

BackgroundTexture features, derived from both the entire tumor area and the region of the tumor-to-brain interface, are crucial indicators for distinguishing tumor types and their degrees of malignancy. However, the discriminative value of texture features from both regions for identifying glioblastomas and metastatic tumors has not been thoroughly explored. The aim of this study is to develop and validate a diagnostic model that combines texture features from the entire tumor area and a 10mm tumor-to-brain interface region, in an attempt to identify more stable and effective texture features. MethodWe retrospectively collected enhanced T1-weighted imaging data from 97 patients with glioblastoma(GBM) and single brain metastasis(SBM) between 2010 and 2024. Machine learning is used to establish multiple diagnostic models for discriminating GBM and SBM based on texture features of the entire tumor and 10mm tumor-to-brain interface regions. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). ResultsThe AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Relief and autoencoder (AE), the AUC was highest using the “one-standard error” rule. ’10mm_glrlm_GrayLevelNonUniformity’ was considered the most stable and predictive feature. The best models in the training set, test set, and validation set were not the same. In the test set, the Relief19AE model had the highest AUC of 0.869 and an accuracy of 0.857. ConclusionThe texture feature model that combines the overall tumor and the tumor-brain interface is beneficial for distinguishing glioblastoma from solitary metastatic tumors, and the texture features of the tumor interface exhibit higher heterogeneity.

Meningioma grading via diagnostic imaging: A systematic review and meta-analysis

PurposeMeningioma is the most common intracranial tumor, graded on pathology using WHO criteria to predict tumor course and treatment. However, pathological grading via biopsy may not be possible in cases with poor surgical access due to tumor location. Therefore, our systematic review aims to evaluate whether diagnostic imaging features can differentiate high grade (HG) from low grade (LG) meningiomas as an alternative to pathological grading.MethodsThree databases were searched for primary studies that either use routine magnetic resonance imaging (MRI) or computed tomography (CT) to assess pathologically WHO-graded meningiomas. Two investigators independently screened and extracted data from included studies.Results24 studies met our inclusion criteria with 12 significant (p < 0.05) CT and MRI features identified for differentiating HG from LG meningiomas. Cystic changes in the tumor had the highest specificity (93.4%) and irregular tumor-brain interface had the highest positive predictive value (65.0%). Mass effect had the highest sensitivity (81.0%) and negative predictive value (90.7%) of all imaging features. Imaging feature with the highest accuracy for identifying HG disease was irregular tumor-brain interface (79.7%). Irregular tumor-brain interface and heterogenous tumor enhancement had the highest AUC values of 0.788 and 0.703, respectively.ConclusionOur systematic review highlight imaging features that can help differentiate HG from LG meningiomas.

Clear neuroimaging margin at the brain-tumor interface is associated with gross total resection and longer survival in non-enhancing diffuse gliomas.

This study aimed to determine whether the presence of distinct glioma margins on preoperative imaging is correlated with improved intraoperative identification of tumor-brain interfaces and overall improved surgical outcomes of non-enhancing gliomas. This is a retrospective study of all primary glioma resections at our institution between 2000-2020. Tumors with contrast enhancement or with final pathology other than diffuse infiltrative glial neoplasm (WHO II or WHO III) were excluded. Tumors were stratified into two groups: those with distinct radiographical borders between tumor and brain, and those with ill-defined radiographical margins. Multivariate analysis was performed to determine the impact of clear preoperative margins on the primary outcome of gross-total resection. Within the study period, 59 patients met inclusion criteria, of which 31 (53%) had distinct margins. These patients were predominantly younger (37.6 vs. 48.1 years, P=0.007). Tumor and other patient characteristics were similar in both cohorts, including gender, laterality, size, location, tumor type, grade, and surgical adjuncts utilized (P>0.05). Multivariate regression identified that distinct preoperative margins correlated with increased rates of gross total resection (P=0.02). Distinct margins on preoperative neuroimaging also correlated positively with surgeon identification of intra-operative margins (P<0.0001), fewer deaths over the study period (P=0.01), and longer overall survival (P=0.03). Distinct glioma-parenchyma margins on preoperative imaging are associated with improved surgical resection for diffuse gliomas, as distinct margins may correlate with distinguishable glioma-brain interfaces intraoperatively. Further prospective studies may discover additional clinical uses for these findings.

Single-cell transcriptome analysis upon ECM-remodeling meningioma cells.

Meningiomas are the most common tumours that primarily arise in the central nervous system, but their intratumoural heterogeneity has not yet been thoroughly studied. We aimed to investigate the transcriptome characteristics and biological properties of ECM-remodeling meningioma cells. Single-cell RNA sequencing (ScRNA-seq) data from meningioma samples were acquired and used for analyses. We conducted comprehensive bioinformatics analyses, including screening for differentially expressed genes (DEGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and Gene Ontology (GO) term enrichment analyses, Gene Set Enrichment Analysis (GSEA), protein-protein interaction (PPI) analysis, and copy number variation (CNV) analysis on single-cell sequencing data from meningiomas. Eighteen cell types, including six meningioma subtypes, were identified in the data. ECM-remodeling meningioma cells (MGCs) were mainly distributed in brain-tumour interface tissues. KEGG and GO enrichment analyses revealed that 908 DEGs were mainly related to cell adhesion, extracellular matrix organization, and ECM-receptor interaction. GSEA analysis demonstrated that homophilic cell adhesion via plasma membrane adhesion molecules was significantly enriched (NES = 2.375, P < 0.001). CNV analysis suggested that ECM-remodeling MGCs showed considerably lower average CNV scores. ECM-remodeling MGCs predominantly localized at the brain-tumour interface area and adhere stably to the basement membrane with a lower degree of malignancy. This study provides novel insights into the malignancy of meningiomas.

Abstract IA007: Distinct immune landscapes characterize highly and minimally invasive brain metastases

Abstract IA007: Distinct immune landscapes characterize highly and minimally invasive brain metastases

Invasive growth of brain metastases is linked to CHI3L1 release from pSTAT3-positive astrocytes.

Compared to minimally invasive brain metastases (MI BrM), highly invasive (HI) lesions form abundant contacts with cells in the peritumoral brain parenchyma and are associated with poor prognosis. Reactive astrocytes (RAs) labeled by phosphorylated STAT3 (pSTAT3) have recently emerged as a promising therapeutic target for BrM. Here, we explore whether the BrM invasion pattern is influenced by pSTAT3+ RAs and may serve as a predictive biomarker for STAT3 inhibition. We used immunohistochemistry to identify pSTAT3+ RAs in HI and MI human and patient-derived xenograft (PDX) BrM. Using PDX, syngeneic, and transgenic mouse models of HI and MI BrM, we assessed how pharmacological STAT3 inhibition or RA-specific STAT3 genetic ablation affected BrM growth in vivo. Cancer cell invasion was modeled in vitro using a brain slice-tumor co-culture assay. We performed single-cell RNA sequencing of human BrM and adjacent brain tissue. RAs expressing pSTAT3 are situated at the brain-tumor interface and drive BrM invasive growth. HI BrM invasion pattern was associated with delayed growth in the context of STAT3 inhibition or genetic ablation. We demonstrate that pSTAT3+ RAs secrete Chitinase 3-like-1 (CHI3L1), which is a known STAT3 transcriptional target. Furthermore, single-cell RNA sequencing identified CHI3L1-expressing RAs in human HI BrM. STAT3 activation, or recombinant CHI3L1 alone, induced cancer cell invasion into the brain parenchyma using a brain slice-tumor plug co-culture assay. Together, these data reveal that pSTAT3+ RA-derived CHI3L1 is associated with BrM invasion, implicating STAT3 and CHI3L1 as clinically relevant therapeutic targets for the treatment of HI BrM.

Preoperative validation of edema-corrected tractography in neurosurgical practice: translating surgeon insights into novel software implementation.

Peritumoral edema alters diffusion anisotropy, resulting in false negatives in tractography reconstructions negatively impacting surgical decision-making. With supratotal resections tied to survival benefit in glioma patients, advanced diffusion modeling is critical to visualize fibers within the peritumoral zone to prevent eloquent fiber transection thereafter. A preoperative assessment paradigm is therefore warranted to systematically evaluate multi-subject tractograms along clinically meaningful parameters. We propose a novel noninvasive surgically-focused survey to evaluate the benefits of a tractography algorithm for preoperative planning, subsequently applied to Synaptive Medical's free-water correction algorithm developed for clinically feasible single-shell DTI data. Ten neurosurgeons participated in the study and were presented with patient datasets containing histological lesions of varying degrees of edema. They were asked to compare standard (uncorrected) tractography reconstructions overlaid onto anatomical images with enhanced (corrected) reconstructions. The raters assessed the datasets in terms of overall data quality, tract alteration patterns, and the impact of the correction on lesion definition, brain-tumor interface, and optimal surgical pathway. Inter-rater reliability coefficients were calculated, and statistical comparisons were made. Standard tractography was perceived as problematic in areas proximal to the lesion, presenting with significant tract reduction that challenged assessment of the brain-tumor interface and of tract infiltration. With correction applied, significant reduction in false negatives were reported along with additional insight into tract infiltration. Significant positive correlations were shown between favorable responses to the correction algorithm and the lesion-to-edema ratio, such that the correction offered further clarification in increasingly edematous and malignant lesions. Lastly, the correction was perceived to introduce false tracts in CSF spaces and - to a lesser degree - the grey-white matter interface, highlighting the need for noise mitigation. As a result, the algorithm was modified by free-water-parameterizing the tractography dataset and introducing a novel adaptive thresholding tool for customizable correction guided by the surgeon's discretion. Here we translate surgeon insights into a clinically deployable software implementation capable of recovering peritumoral tracts in edematous zones while mitigating artifacts through the introduction of a novel and adaptive case-specific correction tool. Together, these advances maximize tractography's clinical potential to personalize surgical decisions when faced with complex pathologies.

Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI.

Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively reviewed patients with intracranial meningiomas from two hospitals. Preoperative MRIs were analyzed for tumor and edema volumes, enhancement patterns, margins, and tumor-brain interfaces. Radiomics features were extracted, and machine learning models were employed to predict meningioma grades. A total of 212 patients were included. In the training group (Hospital 1), significant differences were observed between low-grade and high-grade meningiomas in terms of tumor volume (p = 0.012), edema volume (p = 0.004), enhancement (p = 0.001), margin (p < 0.001), and tumor-brain interface (p < 0.001). Five radiomics features were selected for model development. The prediction model for radiomics features demonstrated an average validation accuracy of 0.74, while the model for clinical imaging features showed an average validation accuracy of 0.69. When applied to external test data (Hospital 2), the radiomics model achieved an area under the receiver operating characteristics curve (AUC) of 0.72 and accuracy of 0.69, while the clinical imaging model achieved an AUC of 0.82 and accuracy of 0.81. An improved performance was obtained from the model constructed by combining radiomics and clinical imaging features. In the combined model, the AUC and accuracy for meningioma grading were 0.86 and 0.73, respectively. In conclusion, this study demonstrates the potential value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas. The combination of both radiomics and clinical imaging features achieved the highest AUC among the models. Therefore, the combined model of radiomics and clinical imaging features may offer a more effective tool for predicting clinical outcomes in meningioma patients.

Correlation between Magnetic Resonance Imaging (MRI) Grading with Histopathological Grading of Supratentorial Meningiomas

Background: Meningiomas, common intracranial tumors originating from meningeal cells, critically impact patient outcomes. Their histologic grade influences recurrence risk and treatment decisions, including radiation therapy. Identifying advanced-grade meningiomas via pre-operative MRI is vital for surgical planning, reducing complications, and guiding adjuvant therapies. Objective: To investigate the correlation between pre-operative MRI features and histopathological grade in supratentorial meningiomas. Methods: This cross-sectional study was conducted from July 2019 to December 2020 at the Department of Neurosurgery of Dhaka Medical College and Hospital. A total of 43 patients with supratentorial meningiomas underwent resection and were analyzed in terms of neuroimaging features of pre-operative MRI. The relationships between MRI features and WHO histopathological grade were analyzed and scored quantitatively. Results: The mean age of the study population was 37±11.86 (SD) years. Male female ratio was 1:2.7. Main clinical presentation was headache (90.7%). The most common location was convexity meningioma (37.2%). Out of all patients, high, grade and low-grade meningioma were diagnosed by MRI, 9 (20.93%) and 34 (79.09%), respectively; on the other hand, high, grade and low-grade meningiomas diagnosed by histopathology 7 (16.3%) and 36 (83.7%) respectively. A positive correlation was found between MRI grading and histopathological grading of supratentorial meningiomas and statistical significance (Spearman rho-0.547, p-value &lt;0.001). A significant association was also observed between MRI grading and histopathological grading of supratentorial meningiomas (p-value 0.002). Unclear tumor brain interface, capsular enhancement, and irregular tumor margin in the MRI were identified factors in predicting advanced histopathological grade of meningiomas as p-values 0.002, 0.008, and 0.001, respectively. Conclusion: This scoring approach may be useful for neurosurgeons ...

P19.08.B INCREASED CORTICAL EXCITABILITY TO TRANSCRANIAL MAGNETIC STIMULATION CONTRIBUTE TO EPILEPTOGENESIS IN IDH1-MUTANT GLIOMAS

Abstract BACKGROUND In-vitro studies indicate that gliomas with a mutation of the isocitrate dehydrogenase (IDH) increase neuronal excitability of the peritumoral cortex contributing to epileptogenesis in these patients. However, in-vivo evidence is missing. This study evaluates the electric characteristics of the brain-tumor-interface (BTI) relative to the IDH mutation status. MATERIAL AND METHODS To investigate peritumoral cortical excitability (CE), we applied 5258 pulses of transcranial magnetic stimulation (TMS) at the BTI of IDH-mutant (IDH-mt) and IDH-wildtype (IDH-wt) glial tumors in 39 patients. CE was assessed by the resting motor threshold (RMT) and the synchronized electromyographic (EMG) activity (i.e., event-related spectral perturbation, ERSP) after TMS. ERSP values were related to the IDH status, tumor grading, antiepileptic drug intake (AED) and the spatial relationship to the tumor borders. RESULTS There was not significant group difference in RMT. TMS to the BTI triggered an EMG synchronization decreasing linearly with the distance to the functional hotspot. In contrast, IDH-mt gliomas demonstrated an increased cortical output of the peritumoral brain tissue compared to IDH-wt gliomas. This effect was not attributable to AED intake or other histological and molecular characteristics. Notably, cortical hyperexcitability was detectable well beyond the tumor border. CONCLUSION s: This study provides in-vivo evidence of cortical hyperexcitability at the BTI of IDH-mt gliomas which could contribute to the epileptogenesis in these patients.

Meningiomas of the rolandic region: risk factors for motor deficit and role of intra-operative monitoring

ObjectiveMeningiomas of the rolandic region are associated to high risk of postoperative motor deficits. This study discusses the factors affecting motor outcome and recurrences from the analysis of a monoinstitutional case series and eight studies from a literature review.MethodsData of 75 patients who underwent surgery for meningioma of the rolandic region were retrospectively reviewed. The analyzed factors included tumor location and size, clinical presentation, magnetic resonance imaging (MRI) and surgical findings, brain-tumor interface, extent of resection, postoperative outcome and recurrence. Eight studies from literature on rolandic meningiomas treated with or without intraoperative monitoring (IOM) were reviewed with the aim to define the impact of IOM on the extent of resection and motor outcome.ResultsAmong the 75 patients of the personal series, the meningioma was on the brain convexity in 34 (46%), at the parasagittal region in 28 (37%) and at the falx in 13 (17%). The brain-tumor interface was preserved in 53 cases (71%) at MRI and in 56 (75%) at surgical exploration. Simpson grade I resection was obtained in 43% of patients, grade II in 33%, grade III in 15% and grade IV in 9%. The motor function worsened postoperatively in 9 among 32 cases with preoperative deficit (28%) and in 5 among 43 with no preoperative deficit (11.5%); definitive motor deficit was evidenced in overall series at follow-up in 7 (9.3%). Patients with meningioma with lost arachnoid interface had significant higher rates of worsened postoperative motor deficit (p = 0.01) and seizures (p = 0.033). Recurrence occurred in 8 patients (11%). The analysis of the 8 reviewed studies (4 with and 4 without IOM) shows in the group without IOM higher rates of Simpson grades I and II resection (p = 0.02) and lower rates of grades IV resection (p = 0.002); no significant differences in postoperative immediate and long-term motor deficits were evidenced between the two groups.ConclusionsData from literature review show that the use of IOM does not affect the postoperative motor deficit Therefore, its role in rolandic meningiomas resection remains to be determined and will be defined in further studies.

Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model.

To investigate the utility of preoperative multiparametric magnetic resonance imaging (mpMRI)-based clinical-radiomic analysis combined with machine learning (ML) algorithms in predicting the expression of the Ki-67 proliferative index and p53 tumor suppressor protein in patients with meningioma. This multicenter retrospective study included 483 and 93 patients from two centers. The Ki-67 index was classified into high (Ki-67≥5%) and low (Ki-67<5%)-expressed groups, and the p53 index was classified into positive (p53≥5%) and negative (p53<5%)-expressed groups. Clinical and radiological features were analyzed using univariate and multivariate statistical analyses. Six ML models were performed with different types of classifiers to predict Ki-67 and p53 status. In the multivariate analysis, larger tumor volumes (p<0.001), irregular tumor margin (p<0.001), and unclear tumor-brain interface (p<0.001) were independently associated with a high Ki-67 status, whereas the presence of both necrosis (p=0.003) and the dural tail sign (p=0.026) were independently associated with a positive p53 status. A relatively better performance was yielded from the model constructed by combined clinical and radiological features. The area under the curve (AUC) and accuracy of high Ki-67 were 0.820 and 0.867 in the internal test, and 0.666 and 0.773 in the external test, respectively. Regarding p53 positivity, the AUC and accuracy were 0.858 and 0.857 in the internal test, and 0.684 and 0.718 in the external test. The present study developed clinical-radiomic ML models to non-invasively predict Ki-67 and p53 expression in meningioma using mpMRI features, and provides a novel non-invasive strategy for assessing cell proliferation.

Abstract 69: Minimally invasive brain metastases are characterized by elevated immune cell infiltrate

Abstract Background: Cancer metastasis to the brain is a common complication of advanced disease with limited therapeutic options. Inefficient treatment is influenced, in part, by the unique composition of the brain microenvironment. Brain metastases (BrM) grow in two distinct patterns, either as minimally invasive (MI) masses with well-defined borders, or as tumors with highly invasive (HI) growth into surrounding brain tissue. HI BrM are associated with poor prognoses compared to MI BrM; however, differences in the tumor immune microenvironments (TIME) between these two lesion types remain largely unknown. Here, we investigate how the TIME differs between HI and MI BrM. Methods: We use Nanostring Digital Spatial Profiling coupled with the Cancer Transcriptome Atlas panel on 5 MI and 15 HI BrM patient samples (lung and breast cancer). This technique enables specific isolation of cancer cells at the tumor-brain interface and quantification of 1,825 cancer-specific RNA targets. Additionally, we perform imaging mass cytometry (IMC) on 119 BrM samples (lung cancer, breast cancer, melanoma, other) from 46 patients, encompassing over 350,000 cells. Samples represent BrM from various primary sites, and include patient-matched samples from the brain-tumor interface (‘margin’) or the centre of the metastatic lesion (‘core’). Results: The Nanostring Digital Spatial Profiling revealed a list of 106 and 73 differentially expressed genes in MI vs HI breast and lung BrM, respectively. Gene set enrichment analyses revealed that an interferon gamma (IFNγ) pathway signature was enriched in MI BrM and lost in HI BrM, which was confirmed by immunohistochemical staining for pSTAT1, consistent with an “immune hot” TIME in MI BrM when compared to HI lesions. Using IMC technology, we identified 20 different cell types, activation states, and spatially-defined cellular neighbourhoods across our BrM patient samples. In comparison to MI samples, HI BrM have lower numbers of B cells, CD4+ T cells, and CD4- CD8- T cells in both the core and margin samples, and lower numbers of CD8+ T cells and regulatory T cells in the margin samples only. Discussion: These data suggest that HI BrM invade into an immunosuppressed microenvironment while MI BrM are characterized by an active anti-tumor immune infiltrate. Together, this work suggests potential immune regulation of BrM invasion, which warrants further investigation. Citation Format: Sarah M. Maritan, Elham Karimi, Matthew Dankner, Miranda W. Yu, Aldo Hernandez-Corchado, Morteza Rezanejad, Parvaneh Fallah, Benoit Fiset, Yuhong Wei, Stephanie Lam, Ali Nehme, Ian R. Watson, Morag Park, Yasser Riazalhosseini, Hamed Najafabadi, Kevin Petrecca, Marie-Christine Guiot, Daniela F. Quail, Logan A. Walsh, Peter M. Siegel. Minimally invasive brain metastases are characterized by elevated immune cell infiltrate [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 69.

The Role of Pre-Operative MRI for Prediction of High-Grade Intracranial Meningioma: A Retrospective Study.

High histological grade (WHO grade 2 and 3) intracranial meningiomas have been linked to a greater risk for tumor recurrence and worse clinical outcomes compared to low-grade (WHO grade 1) tumors. Preoperative magnetic resonance imaging (MRI) plays a crucial role in tumor evaluation and allows a better understanding of tumor grading, which could potentially alter clinical outcomes. The present study sought to determine whether preoperative MRI features of intracranial meningiomas can serve as predictors of high-grade tumors. This retrospective study reviewed 327 consecutive confirmed cases of intracranial meningiomas, among whom 210 (64.2%) had available preoperative MRI studies. Thereafter, imaging features such as intratumoral signal heterogeneity, venous sinus invasion, necrosis or hemorrhage, mass effect, cystic component, bone invasion, hyperostosis, spiculation, heterogeneous tumor enhancement, capsular enhancement, restricted diffusion, brain edema, and unclear tumor-brain interface were obtained and data were analyzed using univariate and multivariate analyses. 249 (76.1%) patients had low-grade (grade I), and 78 (23.9%) had high-grade (grades 2 and 3) intracranial meningioma. The majority of cases were females (274 cases, 83.3%) and most patients were below 60 years of age (mean age, 52.50 ± 11.51 years). The multivariate analysis with Multiple Logistic regression analysis using factors determined to be significant during univariate analysis via a backward stepwise selection method with statistical significance set at 0.05 identified three MRI features including necrosis or hemorrhage (adjusted OR = 2.94, 95% CI: 1.15-7.48, p = 0.024), hyperostosis (adjusted OR = 0.31, 95% CI: 0.12-0.79, p = 0.014), and brain edema (adjusted OR = 2.33, 95% CI: 1.13-4.81, p = 0.022) as significant independent predictors of high-grade meningioma after adjusting for confounders. Our study suggested that certain preoperative MRI features of intracranial meningiomas including necrosis or hemorrhage and brain edema could potentially predict high-grade tumors while hyperostosis is a predictor for low-grade tumors.

Usefulness of a Rim-Enhancing Pattern on the Contrast-Enhanced 3D-FLAIR Sequence and MRI Characteristics for Distinguishing Meningioma and Malignant Dural-Based Tumor.

Meningiomas are the most common type of extra-axial dural-based tumors; however, malignant dural-based tumors can mimic meningiomas on imaging. The aim of this study was to determine the efficacy of differentiating meningiomas from malignant dural-based tumors by using rim-enhancement patterns on a contrast-enhanced FLAIR sequence and MR imaging characteristics. This retrospective study included 102 patients with meningiomas and 31 patients with malignant dural-based tumors who underwent pretreatment MR imaging. The rim-enhancement patterns on contrast-enhanced FLAIR and MR imaging characteristics, including the dural tail sign, hyperostosis, bony destruction, leptomeningeal enhancement, peritumoral edema, T2-weighted signal intensity, and tumor enhancement were evaluated. Complete rim enhancement of the tumor-brain interface on contrast-enhanced FLAIR (contrast-enhanced-FLAIR rim sign) was present in most meningiomas (91/102, 89.2%) and at significantly greater frequency than in malignant dural-based tumors (2/31, 6.5%) (P < .001). Complete contrast-enhanced FLAIR rim enhancement provided high sensitivity (89.2%), specificity (93.5%), and accuracy (90.2%) for diagnosing meningioma. Additionally, hyperostosis was an MR imaging characteristic that suggested a diagnosis of meningioma. In contrast, bony destruction with cortical breakthrough and leptomeningeal enhancement suggested malignant dural-based tumors. There were limitations of meningiomas of <2.0 cm or at cavernous sinus locations that did not demonstrate contrast-enhanced FLAIR rim enhancement. The rim-enhancement pattern on contrast-enhanced FLAIR could help differentiate meningiomas and malignant dural-based tumors. The presence of complete rim enhancement on contrast-enhanced FLAIR was a robust predictive sign for meningioma.

The efficacy of preoperative MRI features in the diagnosis of meningioma WHO grade and brain invasion.

The preoperative MRI scans of meningiomas were analyzed based on the 2021 World Health Organization (WHO) Central Nervous System (CNS) Guidelines, and the efficacy of MRI features in diagnosing WHO grades and brain invasion was analyzed. The data of 675 patients with meningioma who underwent MRI in our hospital from 2006 to 2022, including 108 with brain invasion, were retrospectively analyzed. Referring to the WHO Guidelines for the Classification of Central Nervous System Tumors (Fifth Edition 2021), 17 features were analyzed, with age, sex and meningioma MRI features as risk factors for evaluating WHO grade and brain invasion. The risk factors were identified through multivariable logistic regression analysis, and their receiver operating characteristic (ROC) curves for predicting WHO grades and brain invasion were generated, and the area under the curve (AUC), sensitivity and specificity were calculated. Univariate analysis showed that sex, tumor size, lobulated sign, peritumoral edema, vascular flow void, bone invasion, tumor-brain interface, finger-like protrusion and mushroom sign were significant for diagnosing meningioma WHO grades, while these features and ADC value were significant for predicting brain invasion (P < 0.05). Multivariable logistic regression analysis showed that the lobulated sign, tumor-brain interface, finger-like protrusion, mushroom sign and bone invasion were independent risk factors for diagnosing meningioma WHO grades, while the above features, tumor size and ADC value were independent risk factors for diagnosing brain invasion (P < 0.05). The tumor-brain interface had the highest efficacy in evaluating WHO grade and brain invasion, with AUCs of 0.779 and 0.860, respectively. Combined, the variables had AUCs of 0.834 and 0.935 for determining WHO grade and brain invasion, respectively. Preoperative MRI has excellent performance in diagnosing meningioma WHO grade and brain invasion, while the tumor-brain interface serves as a key factor. The preoperative MRI characteristics of meningioma can help predict WHO grade and brain invasion, thus facilitating complete lesion resection and improving patient prognosis.

Base MRI Imaging Characteristics of Meningioma Patients to Discuss the WHO Classification of Brain Invasion Otherwise Benign Meningiomas.

Compared and analyzed the MRI imaging features of brain invasion otherwise benign (BIOB) meningiomas and WHO grade 1, grade 2 meningiomas, discussed the WHO grading of BIOB from the perspective of imaging. A retrospective analysis was performed on 675 meningiomas patients who carried on MRI examination from January 2006 to February 2022. Setting the 2022 Central nervous system (CNS) WHO Guidelines as the gold standard for pathological diagnosis. Statistical analysis of age, gender, and MRI features of meningiomas in relation to WHO grade and brain invasion. Among 675 cases meningiomas, 543 (80.4%) were WHO grade 1, 123 (18.2%) were WHO grade 2, and 9 (1.3%) were WHO grade 3. There were 108 cases meningiomas with brain invasion (BI) (16.0%) and 567 cases without BI (84.0%). Among BI cases, 67 cases were BIOB. Compared the MRI features between BIOB and WHO grade 1 meningiomas, multivariate analysis demonstrated that the most strongly factors associated with distinguish them were enhancement degree, peritumoral edema, tumor-brain interface, fingerlike protrusion, mushroom sign, and bone invasion (AUC: 0.925 (0.901∼0.945), sensitivity: 0.925, specificity: 0.801). Compared the MRI features between BIOB and WHO grade 2 meningiomas, multivariate analysis demonstrated that the most strongly factors associated with distinguish them were enhancement degree and the tumor-brain interface (AUC: 0.779 (0.686∼0.841), sensitivity: 0.746, specificity: 0.732), their efficacy was slightly weaker. BIOB is more similar to WHO grade 2 meningiomas in clinical and imaging features than WHO grade 1, so we think that it may be reasonable to classify BIOB as WHO Grade 2 meningiomas in the guidelines.

Photodynamics of Subependymal Giant Cell Astrocytoma (SEGA) with 5-Aminolevulinic Acid (5-ALA/Gliolan©)

Abstract AIMS 5-ALA (Gliolan©) is a valuable surgical tool used predominantly in high grade tumours, which utilises tissue fluorescence to improve the visualisation of the brain-tumour interface. This ensures safe maximal resection, while preserving healthy brain. While Gliolan© use in low grade tumours has previously been reported with variable results, reports of its use and success in the context of SEGA are extremely rare. This report highlights the use of Gliolan© in a patient presenting with a Subependymal Giant Cell Astrocytoma (in a background of tuberous sclerosis), facilitating maximal safe resection and preserving eloquent tissue. METHOD Tumour resection was performed with pre-operative DTI-fiber tracking and mapping. A transsulcal minimal invasive parafascicular approach (tsMIPS) was carried out with assistance of NICO BrainPath© tubular retractor system (60x13.5), neuronaviagtion, Gliolan©, intra-operative neuro monitoring (IONM), and ultrasound guidance RESULTS The tumour was found to have both bright and pale fluorescence in the cystic and solid components respectively. Resection was limited to the soft cystic component only, as the solid tumour component showed anatomical attachment to the subgenual area and the fornix. No fluorescence was perceived at the end of resection. The patient made a good recovery with no post-operative deficits. Histopathology confirmed subependymal giant cell astrocytoma (SEGA, WHO grade I). No adjuvant treatment was required CONCLUSION This reports suggests 5-ALA may be beneficial in the safe resection of SEGAs. Further studies and technological advances in the area of photodynamics, imaging, and intra-operative mapping may be helpful to fully evaluate the efficacy of 5-ALA in SEGAs and other low-grade tumours.

A map of the spatial distribution and tumour-associated macrophage states in glioblastoma and grade 4 IDH-mutant astrocytoma.

Tumour-associated macrophages (TAMs) abundantly infiltrate high-grade gliomas and orchestrate immune response, but their diversity in isocitrate dehydrogenase (IDH)-differential grade 4 gliomas remains largely unknown. This study aimed to dissect the transcriptional states, spatial distribution, and clinicopathological significance of distinct monocyte-derived TAM (Mo-TAM) and microglia-derived TAM (Mg-TAM) clusters across glioblastoma-IDH-wild type and astrocytoma-IDH-mutant-grade 4 (Astro-IDH-mut-G4). Single-cell RNA sequencing was performed on four cases of human glioblastoma and three cases of Astro-IDH-mut-G4. Cell clustering, single-cell regulatory network inference, and gene set enrichment analysis were performed to characterize the functional states of myeloid clusters. The spatial distribution of TAM subsets was determined in human glioma tissues using multiplex immunostaining. The prognostic value of different TAM-cluster specific gene sets was evaluated in the TCGA glioma cohort. Profiling and unbiased clustering of 24,227 myeloid cells from glioblastoma and Astro-IDH-mut-G4 identified nine myeloid cell clusters including monocytes, six Mo/Mg-TAM subsets, dendritic cells, and proliferative myeloid clusters. Different Mo/Mg-TAM clusters manifest functional and transcriptional diversity controlled by specific regulons. Multiplex immunostaining of subset-specific markers identified spatial enrichment of distinct TAM clusters at peri-vascular/necrotic areas in tumour parenchyma or at the tumour-brain interface. Glioblastoma harboured a substantially higher number of monocytes and Mo-TAM-inflammatory clusters, whereas Astro-IDH-mut-G4 had a higher proportion of TAM subsets mediating antigen presentation. Glioblastomas with a higher proportion of monocytes exhibited a mesenchymal signature, increased angiogenesis, and worse patient outcome. Our findings provide insight into myeloid cell diversity and its clinical relevance in IDH-differential grade 4 gliomas, and may serve as a resource for immunotherapy development. © 2022 The Pathological Society of Great Britain and Ireland.

Pediatric pineal region tumors: institutional experience of surgical managements with posterior interhemispheric transtentorial approach.

Resecting pineal region tumors in children is often challenging. Several approaches have been proposed and practiced. A personal series of pediatric pineal region tumors resected through craniotomy with posterior interhemispheric occipital transtentorial (OT) approach are reviewed. We present the surgical techniques, pitfalls, and their results. Eighty patients ranging in age from 3months to 21years old, and treated over 3 decades were reviewed. Hydrocephalus caused the main presenting symptoms and was noted in 74 patients. It was treated prior to the craniotomy for tumor resection with endoscopic third ventriculostomy (ETV) in 33, external ventricular drainage in 26, and precraniotomy shunt in 15. Nine patients had ETV together with endoscopic biopsy. All patients had a parieto-occipital craniotomy in a prone position. Through a tentorial section, a gross total resection of the tumor was attempted except for germinomas. The tumor pathology showed 32 germ cell tumors (GCT), 22 benign astrocytomas, 13 pineal parenchymal tumors, 5 ATRTs, 3 papillary tumors, and 5 others. Of GCTs, 18 were teratomas. The extent of resection consisted of 55 gross total resections, 13 subtotal resections, 10 partial, and 2 biopsies with one postoperative death. Hemiparesis in 2, cerebellar ataxia in another 2, and hemiballismus in 1 were transient and improved over time. One had permanent hemisensory loss and another patient had bilateral oculomotor palsy. Postoperative homonymous hemianopia occurred in 2 patients but subsided over a short period of time. Parinaud's sign was noted in 24 patients, of which 16 were transient. The posterior interhemispheric OT approach provides a safe route and comfortable access to the pineal region inchildren. A great majority of postoperative neurological complications are the results of direct manipulations of the midbrain at tumor resection. Identification and preservation of the tumor-brain interface are of paramount importance. GCTs other than teratomas are treated with neoadjuvant chemotherapy and may eliminate the need for craniotomy. Exophytic midbrain JPAs are amenable to resection.