UNASSIGNED: Glioblastoma multiforme (GBM), particularly the IDH-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches.
UNASSIGNED: This study utilizes a Support Vector Machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (< 12 Months) and long (>=12 Months) survivors. A dataset comprising multi-parametric MRI (mpMRI) scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, FLAIR, and T1-Gd sequences. Low variance features were removed, and Recursive Feature Elimination (RFE) was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT promoter methylation status were integrated to enhance prediction accuracy.
UNASSIGNED: The model showed reasonable results in terms of cross-validated AUC of 0.84 (95% CI: 0.80-0.90) with (p-value < 0.001) effectively categorizing patients into short and long survivors. Log-rank test (Chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen\'s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value<0.0001.
UNASSIGNED: The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Background Oligodendrogliomas, rare brain tumors in the frontal lobe\'s white matter, are reshaped by molecular markers like isocitrate dehydrogenase mutations and 1p/19q co-deletion, influencing treatment outcomes. Despite the initial indolence, these tumors pose a significant risk, with a median survival of 10-12 years. Non-invasive alternatives, such as magnetic resonance imaging (MRI) for assessing T2-fluid-attenuated inversion recovery (FLAIR) mismatch and calcifications, provide insights into molecular subtypes and aid prognosis. Our study explored these features to predict the oligodendroglioma status and refine patient management to improve outcomes. Methods In this retrospective study, patient data identified patients with suspected central nervous system tumors undergoing MRI, revealing low-grade gliomas. Surgical biopsy and 1p/19q fluorescence in situ hybridization confirmed the co-deletion status. MRI was used to assess various morphological features. Statistical analyses included x2 tests, Fisher\'s exact tests, Kruskal-Wallis tests, and binary logistic regression models, with significance set at p < 0.05. Results Seventy-three patients (median age, 37 years) were stratified according to 1p/19q co-deletion. Most (61.6%) were 18-40 years old and mostly male (67.1%). Co-deletion cases, primarily frontal lobe lesions (67.6%), were unilateral (88.2%), with 55.9% non-circumscribed margins and 58.8% ill-defined contours. Smooth contrast enhancement and no necrosis were observed in 48.1% of 1p/19q co-deletion cases. Logistic regression analysis showed a significant association between ill-defined/irregular contours and 1p/19q co-deletion. Fisher\'s exact test confirmed this but raised concerns about the small sample size influencing the conclusions. Conclusions This study established a significant link between glioma tumor contour characteristics, particularly irregular and ill-defined contours, and the likelihood of 1p/19q co-deletion. Our findings underscore the clinical relevance of using tumor contours in treatment decisions and prognosis assessments.

The World Health Organization Classification of Tumors of the Central Nervous System recently incorporated histological features, immunophenotypes, and molecular characteristics to improve the accuracy of glioblastoma (GBM) diagnosis. FGFR3::TACC3 (F3T3) fusion has been identified as an oncogenic driver in IDH-wildtype GBMs. Recent studies have demonstrated the potential of using FGFR inhibitors in clinical trials and TACC3-targeting agents in preclinical models for GBM treatment. However, there is limited information on the clinicopathological and genetic features of IDH-wildtype GBMs with F3T3 fusion. The aim of this study was to comprehensively investigate the clinical manifestations, histological features, and mutational profiles of F3T3-positive GBMs. Between September 2017 and February 2023, 25 consecutive cases (5.0%) of F3T3-positive GBM were extracted from 504 cases of IDH-wildtype GBM. Clinicopathological information and targeted sequencing results obtained from 25 primary and 4 recurrent F3T3-positive GBMs were evaluated and compared with those from F3T3-negative GBMs. The provisional grades determined by histology only were distributed as follows: 4 (26/29; 89.7%), 3 (2/29; 6.9%), and 2 (1/29; 3.4%). Grade 2-3 tumors were ultimately diagnosed as grade 4 GBMs based on the identification of the TERT promoter mutation and the combined gain of chromosome 7 and loss of chromosome 10 (7+/10-). F3T3-positive GBMs predominantly affected women (2.6 females per male). The mean age of patients with an F3T3-positive GBM at initial diagnosis was 62 years. F3T3-positive GBMs occurred more frequently in the cortical locations compared to F3T3-negative GBMs. Imaging studies revealed that more than one-third (12/29; 41.4%) of F3T3-positive GBMs displayed a circumscribed tumor border. Seven of the seventeen patients (41.2%) whose follow-up periods exceeded 20 months died of the disease. Histologically, F3T3-positive GBMs more frequently showed curvilinear capillary proliferation, palisading nuclei, and calcification compared to F3T3-negative GBMs. Molecularly, the most common alterations observed in F3T3-positive GBMs were TERT promoter mutations and 7+/10-, whereas amplifications of EGFR, PDGFRA, and KIT were not detected at all. Other genetic alterations included CDKN2A/B deletion, PTEN mutation, TP53 mutation, CDK4 amplification, and MDM2 amplification. Our observations suggest that F3T3-positive GBM is a distinct molecular subgroup of the IDH-wildtype GBM. Both clinicians and pathologists should consider this rare entity in the differential diagnosis of diffuse astrocytic glioma to make an accurate diagnosis and to ensure appropriate therapeutic management.

OBJECTIVE: In 2021, the WHO central nervous system (CNS) tumor classification criteria added the diagnosis of diffuse astrocytic glioma, IDH wild-type, with molecular features of glioblastoma, WHO grade 4 (DAG-G). DAG-G may exhibit the aggressiveness and malignancy of glioblastoma (GBM) despite the lower histological grade, and thus a precise preoperative diagnosis can help neurosurgeons develop more refined individualized treatment plans. This study aimed to establish a predictive model for the non-invasive identification of DAG-G based on preoperative MRI radiomics.
METHODS: Patients with pathologically confirmed glioma in Huashan Hospital, Fudan University, between September 2019 and July 2021 were retrospectively analyzed. Furthermore, two external validation datasets from Wuhan Union Hospital and Xuzhou Cancer Hospital were also utilized to verify the reliability and accuracy of the prediction model. Two regions of interest (ROI) were delineated on the preoperative MRI images of the patients using the semi-automatic tool ITK-SNAP (version 4.0.0), which were named the maximum anomaly region (ROI1) and the tumor region (ROI2), and Pyradiomics 3.0 was applied for feature extraction. Feature selection was performed using a least absolute shrinkage and selection operator (LASSO) filter and a Spearman correlation coefficient. Six classifiers, including Gauss naive Bayes (GNB), K-nearest neighbors (KNN), Random forest (RF), Adaptive boosting (AB), and Support vector machine (SVM) with linear kernel and multilayer perceptron (MLP), were used to build the prediction models, and the prediction performance of the six classifiers was evaluated by fivefold cross-validation. Moreover, the performance of prediction models was evaluated using area under the curve (AUC), precision (PRE), and other metrics.
RESULTS: According to the inclusion and exclusion criteria, 172 patients with grade 2-3 astrocytoma were finally included in the study, and a total of 44 patients met the diagnosis of DAG-G. In the prediction task of DAG-G, the average AUC of GNB classifier was 0.74 ± 0.07, that of KNN classifier was 0.89 ± 0.04, that of RF classifier was 0.96 ± 0.03, that of AB classifier was 0.97 ± 0.02, that of SVM classifier was 0.88 ± 0.05, and that of MLP classifier was 0.91 ± 0.03, among which, AB classifier achieved the best prediction performance. In addition, the AB classifier achieved AUCs of 0.91 and 0.89 in two external validation datasets obtained from Wuhan Union Hospital and Xuzhou Cancer Hospital, respectively.
CONCLUSIONS: The prediction model constructed based on preoperative MRI radiomics established in this study can basically realize the prospective, non-invasive, and accurate diagnosis of DAG-G, which is of great significance to help further optimize treatment plans for such patients, including expanding the extent of surgery and actively administering radiotherapy, targeted therapy, or other treatments after surgery, to fundamentally maximize the prognosis of patients.

While the central nervous system (CNS) tumor classification has increasingly incorporated molecular parameters, there is a paucity of literature reporting molecular alterations found in intraventricular glioblastoma (IVGBM), which are rare. We present a case series of nine IVGBMs, including molecular alterations found in standardized next-generation sequencing (NGS). We queried the clinical charts, operative notes, pathology reports, and radiographic images of nine patients with histologically confirmed IVGBM treated at our institution (1995-2021). Routine NGS was performed on resected tumor tissue of two patients. In this retrospective case series of nine patients (22% female, median (range) age: 64.3 (36-85) years), the most common tumor locations were the atrium of the right lateral ventricle (33%) and the septum pellucidum (33%). Five patients had preoperative hydrocephalus, which was managed with intraoperative external ventricular drains in three patients and ventriculoperitoneal shunts in one patient. Hydrocephalus was managed with subtotal resection of a fourth ventricular IVGBM in one patient. The most common surgical approach was transcortical intraventricular (56%). Gross total resection was achieved in two patients, subtotal resection was achieved in six patients, and one patient received a biopsy only. Immunohistochemistry for IDH1 R132H mutant protein was performed in four cases and was negative in all four. Genetic alterations common in glioblastoma, IDH-wildtype, were seen in two cases with available NGS data, including EGFR gene amplification, TERT promoter mutation, PTEN mutation, trisomy of chromosome 7, and monosomy of chromosome 10. Following surgical resection, four patients received adjuvant chemoradiation. Median survival among our cohort was 4.7 months (IQR: 0.9-5.8 months). Management of IVGBM is particularly challenging due to their anatomical location, presentation with obstructive hydrocephalus, and fast growth, necessitating prompt intervention. Additional studies are needed to better understand the genetic landscape of IVGBM compared to parenchymal glioblastoma and may further elucidate the unique pathophysiology of these rare tumors.

Diffuse paediatric-type high-grade glioma, H3-wildtype and IDH-wildtype (pHGG) is a rare and aggressive brain tumor characterized by a specific DNA methylation profile. It was recently introduced in the 5th World Health Organization classification of central nervous system tumors of 2021. Clinical data on this tumor is scarce. This is a case series, which presents the first clinical experience with this entity. We compiled a retrospective case series on pHGG patients treated between 2015 and 2022 at our institution. Data collected include patients\' clinical course, surgical procedure, histopathology, genome-wide DNA methylation analysis, imaging and adjuvant therapy. Eight pHGG were identified, ranging in age from 8 to 71 years. On MRI tumors presented with an unspecific intensity profile, T1w hypo- to isointense and T2w hyperintense, with inhomogeneous contrast enhancement, often with rim enhancement. Three patients died of the disease, with overall survival of 19, 28 and 30 months. Four patients were alive at the time of the last follow-up, 4, 5, 6 and 79 months after the initial surgery. One patient was lost to follow-up. Findings indicate that pHGG prevalence might be underestimated in the elderly population.

Diffuse gliomas are the most common type of primary central nervous system (CNS) neoplasm to affect the adult population. The diagnosis of adult diffuse gliomas is dependent upon the integration of morphological features of the tumour with its underlying molecular alterations, and the integrative diagnosis has become of increased importance in the fifth edition of the WHO classification of CNS neoplasms (WHO CNS5). The three major diagnostic entities of adult diffuse gliomas are as follows: (1) astrocytoma, IDH-mutant; (2) oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and (3) glioblastoma, IDH-wildtype. The aim of this review is to summarize the pathophysiology, pathology, molecular characteristics, and major diagnostic updates encountered in WHO CNS5 of adult diffuse gliomas. Finally, the application of implementing the necessary molecular tests for diagnostic workup of these entities in the pathology laboratory setting is discussed.

Introduction: Glioblastoma is one of the most lethal cancers and leads to more than 200,000 deaths annually. However, despite lots of researchers devoted to exploring novel treatment regime, most of these attempts eventually failed to improve the overall survival of glioblastoma patients in near 20 years. Immunotherapy is an emerging therapy for cancers and have succeeded in many cancers. But most of its application in glioblastoma have been proved with no improvement in overall survival, which may result from the unique immune microenvironment of glioblastoma. Arginine is amino acid and is involved in many physiological processes. Many studies have suggested that arginine and its metabolism can regulate malignancy of multiple cancers and influence the formation of tumor immune microenvironment. However, there is hardly study focusing on the role of arginine metabolism in glioblastoma. Methods: In this research, based on mRNA sequencing data of 560 IDH-wildtype glioblastoma patients from three public cohorts and one our own cohort, we aimed to construct an arginine metabolism-related genes signature (ArMRS) based on four essential arginine metabolism-related genes (ArMGs) that we filtered from all genes with potential relation with arginine metabolism. Subsequently, the glioblastoma patients were classified into ArMRS high-risk and low-risk groups according to calculated optimal cut-off values of ArMRS in these four cohorts. Results: Further validation demonstrated that the ArMRS was an independent prognostic factor and displayed fine efficacy in prediction of glioblastoma patients\' prognosis. Moreover, analyses of tumor immune microenvironment revealed that higher ArMRS was correlated with more immune infiltration and relatively \"hot\" immunological phenotype. We also demonstrated that ArMRS was positively correlated with the expression of multiple immunotherapy targets, including PD1 and B7-H3. Additionally, the glioblastomas in the ArMRS high-risk group would present with more cytotoxic T cells (CTLs) infiltration and better predicted response to immune checkpoint inhibitors (ICIs). Discussion: In conclusion, our study constructed a novel score system based on arginine metabolism, ArMRS, which presented with good efficacy in prognosis prediction and strong potential to predict unique immunological features, resistance to immunotherapy, and guide the application of immunotherapy in IDH-wild type glioblastoma.

OBJECTIVE: Spinal cord metastasis arising from an intracranial glioblastoma is a rare and late event during the natural course of the disease. These pathological entities remain poorly characterized. This study aimed to identify and investigate the timeline, clinical and imaging findings, and prognostic factors of spinal cord metastasis from a glioblastoma.
METHODS: Consecutive histopathological cases of spinal cord metastasis from glioblastomas in adults entered in the French nationwide database between January 2004 and 2016 were screened.
RESULTS: Overall, 14 adult patients with a brain glioblastoma (median age 55.2 years) and harboring a spinal cord metastasis were included. The median overall survival as 16.0 months (range, 9.8-22.2). The median spinal cord Metastasis Free Survival (time interval between the glioblastoma diagnosis and the spinal cord metastasis diagnosis) was 13.6 months (range, 0.0-27.9). The occurrence of a spinal cord metastasis diagnosis greatly impacted neurological status: 57.2% of patients were not ambulatory, which contributed to dramatically decreased Karnofsky Performance Status (KPS) scores (12/14, 85.7% with a KPS score ≤ 70). The median overall survival following spinal cord metastasis was 3.3 months (range, 1.3-5.3). Patients with a cerebral ventricle effraction during the initial brain surgery had a shorter spinal cord Metastasis Free Survival (6.6 vs 18.3 months, p = 0.023). Out of the 14 patients, eleven (78.6%) had a brain IDH-wildtype glioblastoma.
CONCLUSIONS: Spinal cord metastasis from a brain IDH-wildtype glioblastoma has a poor prognosis. Spinal MRI can be proposed during the follow-up of glioblastoma patients especially those who have benefited from cerebral surgical resection with opening of the cerebral ventricles.

In newly diagnosed IDH-wildtype glioblastoma, the frequency and prognostic relevance of tumor regrowth between resection and the initiation of adjuvant radiochemotherapy are unclear. In this retrospective single-center study we included 64 consecutive cases, for whom magnetic resonance imaging (MRI) was available for both the volumetric assessment of the extent of resection immediately after surgery as well as the volumetric target delineation before the initiation of adjuvant radiochemotherapy (time interval: 15.5 ± 1.9 days). Overall, a median new contrast-enhancement volume was seen in 21/64 individuals (33%, 1.5 ± 1.5 cm3), and new non-contrast lesion volume in 18/64 patients (28%, 5.0 ± 2.3 cm3). A multidisciplinary in-depth review revealed that new contrast-enhancement was either due to (I) the progression of contrast-enhancing tumor remnants in 6/21 patients or (II) distant contrast-enhancing foci or breakdown of the blood-brain barrier in previously non-contrast-enhancing tumor remnants in 5/21 patients, whereas it was unspecific or due to ischemia in 10/21 patients. For non-contrast-enhancing lesions, three of eighteen had progression of non-contrast-enhancing tumor remnants and fifteen of eighteen had unspecific changes or changes due to ischemia. There was no significant association between findings consistent with tumor regrowth and a less favorable outcome (overall survival: 14 vs. 19 months; p = 0.423). These findings support the rationale that analysis of the postsurgical remaining tumor-volume for prognostic stratification should be carried out on immediate postoperative MRI (<72 h), as unspecific changes are common. However, tumor regrowth including distant foci may occur in a subset of IDH-wildtype glioblastoma patients diagnosed per WHO 2021 classification. Thus, MRI imaging prior to radiotherapy should be obtained to adjust radiotherapy planning accordingly.