Classification Of Central Nervous System Tumors Research Articles

Diffuse glioneuronal tumor with oligodendroglioma-like features and nuclear clusters (DGONC), new name and new problems: an illustration of one case with atypical morphology and biology

A novel histomolecular tumor of the central nervous system (CNS), the “diffuse glioneuronal tumor with oligodendroglioma-like features and nuclear clusters (DGONC),” has recently been identified, based on a distinct DNA methylation profile and has been added to the 2021 World Health Organization Classification of CNS Tumors. This glioneuronal tumor mainly affects the supratentorial area in children and recurrently presents with a monosomy of chromosome 14. Herein, we report the case of a DNA-methylation based diagnosis of DGONC having atypical features, such as pseudo-rosettes and the absence of a chromosome 14 monosomy, thus rendering its diagnosis very challenging. Because of the wide variety of morphologies harbored by DGONC, a large range of differential diagnoses may be hypothesized from benign to malignant. Interestingly, the current case, like one previously reported, exhibited a co-expression of OLIG2, synaptophysin and SOX10, without GFAP immunopositivity. This particular immunophenotype seems to be a good indicator for a DGONC diagnosis. The classification of DGONC amongst glioneuronal or embryonal tumors is still debated. The clinical (a pediatric supratentorial tumor), morphological (from a benign oligodendroglioma-like tumor with microcalcifications and possible neuropil-like islands to a malignant embryonal tumor with a possible spongioblastic pattern), and immunohistochemical (co-expression of OLIG2 and synaptophsyin) profiles resemble CNS, neuroblastoma, FOXR2-activated and may potentially bring them together in a future classification. Further comprehensive studies are needed to conclude the cellular origin of DGONC and its prognosis.

General Applicability of Existing College of American Pathologists Accreditation Requirements to Clinical Implementation of Machine Learning-Based Methods in Molecular Oncology Testing.

The College of American Pathologists (CAP) accreditation requirements for clinical laboratory testing help ensure laboratories implement and maintain systems and processes that are associated with quality. Machine learning (ML)-based models share some features of conventional laboratory testing methods. Accreditation requirements that specifically address clinical laboratories' use of ML remain in the early stages of development. To identify relevant CAP accreditation requirements that may be applied to the clinical adoption of ML-based molecular oncology assays, and to provide examples of current and emerging ML applications in molecular oncology testing. CAP accreditation checklists related to molecular pathology and general laboratory practices (Molecular Pathology, All Common and Laboratory General) were reviewed. Examples of checklist requirements that are generally applicable to validation, revalidation, quality management, infrastructure, and analytical procedures of ML-based molecular oncology assays were summarized. Instances of ML use in molecular oncology testing were assessed from literature review. Components of the general CAP accreditation framework that exist for traditional molecular oncology assay validation and maintenance are also relevant for implementing ML-based tests in a clinical laboratory. Current and emerging applications of ML in molecular oncology testing include DNA methylation profiling for central nervous system tumor classification, variant calling, microsatellite instability testing, mutational signature analysis, and variant prediction from histopathology images. Currently, much of the ML activity in molecular oncology is within early clinical implementation. Despite specific considerations that apply to the adoption of ML-based methods, existing CAP requirements can serve as general guidelines for the clinical implementation of ML-based assays in molecular oncology testing.

Pediatric neuropathology practice in a low- and middle-income country: capacity building through institutional twinning.

Accurate and precise diagnosis is central to treating central nervous system (CNS) tumors, yet tissue diagnosis is often a neglected focus in low- and middle-income countries (LMICs). Since 2016, the WHO classification of CNS tumors has increasingly incorporated molecular biomarkers into the diagnosis of CNS tumors. While this shift to precision diagnostics promises a high degree of diagnostic accuracy and prognostic precision, it has also resulted in increasing divergence in diagnostic and management practices between LMICs and high-income countries (HICs). Pathologists and laboratory professionals in LMICs lack the proper training and tools to join the molecular diagnostic revolution. We describe the impact of a 7-year long twinning program between Canada and Pakistan on pathology services. During the study period, 141 challenging cases of pediatric CNS tumors initially diagnosed at Aga Khan University Hospital (AKUH), Karachi, were sent to the Hospital for Sick Children in Toronto, Canada (SickKids), for a second opinion. Each case received histologic review and often immunohistochemical staining and relevant molecular testing. A monthly multidisciplinary online tumor board (MDTB) was conducted to discuss the results with pathologists from both institutions in attendance. Diagnostic discordance was seen in 30 cases. Expert review provided subclassification for 53 cases most notably for diffuse gliomas and medulloblastoma. Poorly differentiated tumors benefited the most from second review, mainly because of the resolving power of specialized immunohistochemical stains, NanoString, and targeted gene panel next-generation sequencing. Collaboration with expert neuropathologists led to validation of over half a dozen immunostains at AKUH facilitating diagnosis of CNS tumors. LMIC-HIC Institutional twinning provides much-needed training and mentorship to pathologists and can help in infrastructure development by adopting and validating new immunohistochemical stains. Persistent unresolved cases indicate that molecular techniques are indispensable in for diagnosis in a minority of cases. The development of affordable alternative molecular techniques may help with these histologically unresolved cases.

New CNS tumor classification: The importance in pediatric neurosurgical practice.

The management of the central nervous system (CNS) tumors in the pediatric population is crucial in neurosurgical practice. The World Health Organization (WHO) has evolved its classification of CNS tumors from the 19th century to the 5th edition, published in 2021, incorporating molecular advancements. This transition from morphology to molecular characterization is ongoing. This manuscript analyzes the modifications introduced in the 5th edition of WHO's CNS tumor classification, particularly focusing on pediatric tumor families. The paper integrates clinical, morphological, and molecular information, aiming to guide pediatric neurosurgeons in their daily practice and interdisciplinary discussions. The 5th edition of the WHO classification introduces a hybrid taxonomy that incorporates both molecular and histological components. The terminology shifts from "entity" to "type" and "subtype," aiming to standardize terminology. Tumor grading experiences changes, integrating molecular biomarkers for prognosis. The concept of integrated layered diagnosis is emphasized, where molecular and histological information is combined systematically. The 5th edition of the WHO CNS classification signifies a paradigm shift toward molecular characterization. The incorporation of molecular advances, the layered diagnostic approach, and the inclusion of clinical, morphological, and molecular information aim to provide comprehensive insights into pediatric CNS tumors. This classification offers valuable guidance for pediatric neurosurgeons, aiding in precise diagnosis and treatment planning for these complex neoplasms.

What is Under the Tent? Posterior Fossa Tumors in Children, the Usual Suspects—Part 1

Pediatric posterior fossa tumors are a major cause of morbidity and mortality in children. In this 2-part series, we discuss the spectrum of posterior fossa tumors and their clinical and molecular characteristics. In Part 1, the authors discuss the 5 primary differential diagnoses of posterior fossa tumors: medulloblastoma, pilocytic astrocytoma, ependymoma, diffuse midline glioma, H3K27-altered, and atypical teratoid/rhabdoid tumors. Each posterior fossa tumor is clinically and histologically defined. Additionally, relevant epidemiology data, current genetics/molecular classification according to the fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System, and imaging findings are reviewed. The new version of the international standard for the classification of central nervous system tumors introduces major changes that advance the role of molecular diagnostics in the pediatric central nervous system tumor classification, particularly in posterior fossa tumors, which may impact management and outcomes.Learning Objective: To recognize the imaging appearance of the most common posterior fossa tumors in children.

Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning.

Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the IDH mutation status. In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data. Among the different approaches we examined, the T1-weighted contrast-enhanced sequence was found to be the most suitable for predicting IDH mutations status using radiomics-based machine learning models. Using contrast-enhanced T1-weighted MRI images, our seven-feature model developed with Lasso regression achieved a mean area under the curve (AUC) of 0.846, a mean accuracy of 0.792, a mean sensitivity of 0.847, and a mean specificity of 0.681. The administration of contrast agents resulted in a significant increase in the achieved discriminatory power. Our analyses show that for the prediction of the IDH mutation status using radiomics-based machine learning models, among the MRI images acquired with the commonly used MRI sequences, the contrast-enhanced T1-weighted images are the most suitable.

Pediatric CNS tumors and 2021 WHO classification: what do oncologists need from pathologists?

The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, established new approaches to both CNS tumor nomenclature and grading, emphasizing the importance of integrated diagnoses and layered reports. This edition increased the role of molecular diagnostics in CNS tumor classification while still relying on other established approaches such as histology and immunohistochemistry. Moreover, it introduced new tumor types and subtypes based on novel diagnostic technologies such as DNA methylome profiling. Over the past decade, molecular techniques identified numerous key genetic alterations in CSN tumors, with important implications regarding the understanding of pathogenesis but also for prognosis and the development and application of effective molecularly targeted therapies. This review summarizes the major changes in the 2021 fifth edition classification of pediatric CNS tumors, highlighting for each entity the molecular alterations and other information that are relevant for diagnostic, prognostic, or therapeutic purposes and that patients' and oncologists' need from a pathology report.

Rare variant of large pediatric glioneuronal tumor with novel MYO5A::NTRK3 fusion: illustrative case.

Glioneuronal tumors (GNTs) comprise a rare class of central nervous system (CNS) neoplasms with varying degrees of neuronal and glial differentiation that predominately affect children and young adults. Within the current 2021 World Health Organization (WHO) classification of CNS tumors, GNTs encompass 14 distinct tumor types. Recently, the use of whole-genome DNA methylation profiling has allowed more precise classification of this tumor group. A 3-year-old male presented with a 3-month history of increasing head circumference, regression of developmental milestones, and speech delay. Magnetic resonance imaging of the brain was notable for a large left hemispheric multiseptated mass with significant mass effect and midline shift that was treated with near-total resection. Histological and molecular assessment demonstrated a glioneuronal tumor harboring an MYO5A::NTRK3 fusion. By DNA methylation profiling, this tumor matched to a provisional methylation class known as "glioneuronal tumor kinase-fused" (GNT kinase-fused). The patient was later started on targeted therapy with larotrectinib. This is the first report of an MYO5A::NTRK3 fusion in a pediatric GNT. GNT kinase-fused is a provisional methylation class not currently included in the WHO classification of CNS tumors. This case highlights the impact of thorough molecular characterization of CNS tumors, especially with the increasing availability of novel gene targeting therapies.

Decoding the DNA methylome of central nervous system tumors: An emerging modality for integrated diagnosis.

The definitive diagnosis and classification of individual cancers are crucial for patient care and cancer research. To achieve a robust diagnosis of central nervous system (CNS) tumors, a genotype-phenotype integrated diagnostic approach was introduced in recent versions of the World Health Organization classification, followed by the incorporation of a genome-wide DNA methylome-based classification. Microarray-based platforms are widely used to obtain DNA methylome data, and the German Cancer Research Center (Deutsches Krebsforschungszentrum [DKFZ]) has a webtool for a DNA methylation-based classifier (DKFZ classifier). Integration of DNA methylome will further enhance the precision of CNS tumor classification, especially in diagnostically challenging cases. However, in the clinical application of DNA methylome-based classification, challenges related to data interpretation persist, in addition to technical caveats, regulations, and limited accessibility. Dimensionality reduction (DMR) can complement integrated diagnosis by visualizing a profile and comparing it with other known samples. Therefore, DNA methylome-based classification is a highly useful research tool for auxiliary analysis in challenging diagnostic and rare disease cases, and for establishing novel tumor concepts. Decoding the DNA methylome, especially by DMR in addition to DKFZ classifier, emphasizes the capability of grasping the fundamental biological principles that provide new perspectives on CNS tumors.

Clinical outcomes of solitary fibrous tumors and hemangiopericytomas and risk factors related to recurrence and survival based on the 2021 WHO classification of central nervous system tumors.

The authors aimed to explore the clinical outcomes and risk factors related to recurrence of and survival from solitary fibrous tumors (SFTs) and hemangiopericytomas (HPCs) that were reclassified according to the 2021 WHO classification of central nervous system (CNS) tumors. The authors retrospectively collected and analyzed the clinical and pathological data of SFTs and HPCs recorded from January 2007 to December 2021. Two neuropathologists reassessed pathological slides and regraded specimens on the basis of the 2021 WHO classification. The prognostic factors related to progression-free survival (PFS) and overall survival (OS) were statistically assessed with univariate and multivariate Cox regression analyses. A total of 146 patients (74 men and 72 women, mean ± SD [range] age 46.1 ± 14.3 [3-78] years) were reviewed, and 86, 35, and 25 patients were reclassified as having grade 1, 2, and 3 SFTs on the basis of the 2021 WHO classification, respectively. The median PFS and OS of the patients with WHO grade 1 SFT were 105 months and 199 months after initial diagnosis; for patients with WHO grade 2 SFT, 77 months and 145 months; and for patients with WHO grade 3 SFT, 44 months and 112 months, respectively. Of the entire cohort, 61 patients experienced local recurrence and 31 died, of whom 27 (87.1%) died of SFT and relevant complications. Ten patients had extracranial metastasis. In multivariate Cox regression analysis, subtotal resection (STR) (HR 4.648, 95% CI 2.601-8.304, p < 0.001), tumor located in the parasagittal or parafalx region (HR 2.105, 95% CI 1.099-4.033, p = 0.025), tumor in the vertebrae (HR 3.352, 95% CI 1.228-9.148, p = 0.018), WHO grade 2 SFT (HR 2.579, 95% CI 1.343-4.953, p = 0.004), and WHO grade 3 SFT (HR 5.814, 95% CI 2.887-11.712, p < 0.001) were significantly associated with shortened PFS, whereas STR (HR 3.217, 95% CI 1.435-7.210, p = 0.005) and WHO grade 3 SFT (HR 3.433, 95% CI 1.324-8.901, p = 0.011) were significantly associated with shortened OS. In univariate analyses, patients who received adjuvant radiotherapy (RT) after STR had longer PFS than patients who did not receive RT. The 2021 WHO classification of CNS tumors better predicted malignancy with different pathological grades, and in particular WHO grade 3 SFT had worse prognosis. Gross-total resection (GTR) can significantly prolong PFS and OS and should serve as the most important treatment method. Adjuvant RT was helpful for patients who underwent STR but not for patients who underwent GTR.

Embryonal and pineal tumours

AbstractEmbryonal and pineal tumours represent a diverse group of central nervous system (CNS) neoplasms. While many of the small round blue cell tumours that make up the embryonal neoplasms share similar histologic qualities, there are several morphologic and cytologic characteristics that are useful in distinguishing different tumour types. Similarly, pineal parenchymal tumours represent clinically diverse tumours, ranging from benign to overtly malignant. The most recent iteration of the World Health Organization Classification of CNS Tumours expanded greatly on the significance of molecular alterations in brain tumour diagnostics. In this article, we summarize the salient cytologic and histologic features of CNS embryonal and pineal tumours, and highlight diagnostically relevant molecular alterations within each tumour type.

CSF cytology of common primary CNS neoplasms categorized by CNS WHO 2021.

The detection of neoplastic cells in cerebral spinal fluid (CSF) is pivotal for the management of patients with central nervous system (CNS) tumours. This article delves into the CSF cytological characteristics of common CNS neoplasms, aligning with the 2021 World Health Organization (WHO) classification of CNS tumours. A retrospective review of CSF specimens positive for primary CNS neoplasms was performed at three tertiary medical centres. Only cases that had histopathologic confirmation and/or molecular workup were included. Common primary CNS neoplasms seen in CSF cytology specimens include medulloblastoma, (non-WNT/non-SHH as well as SHH-activated and TP53 mutant), pineoblastoma, atypical teratoid/rhabdoid tumour (AT/RT), IDH-wildtype glioblastoma, and primary diffuse large B-cell lymphoma of the CNS. Ependymomas and germinomas can also have CSF involvement but are less common. Although the typical histologic architecture of these tumours may not be preserved in the CSF, unique cytomorphologic features such as nuclear moulding, nuclear pleomorphism, rhabdoid cells, prominent nucleoli and rosette formation can still be appreciated. Adopting the updated terminology and correlating cytologic observations with molecular findings will streamline the diagnostic process, reducing the complexities and ambiguities pathologists often encounter when analysing CSF specimens for potential primary CNS neoplasms.

A retrospective analysis of practical benefits and caveats of the new WHO 2021 central nervous system tumor classification scheme in low-resource settings: "A perspective from low- and middle-income countries".

The revised classification of tumors of the central nervous system (CNS) by the World Health Organization (WHO) in 2021 was hailed as a major advance and improvement in the management of brain tumor patients. However, the increased reliance on sophisticated technology and molecular analysis posed a major challenge to healthcare systems in low- and middle-income countries. A few recent publications have drawn attention to the issue of the applicability of the new CNS WHO 2021 worldwide, but the exuberant enthusiasm observed in high-income countries seems to have stifled such a concern. In this study, we present data on the practical utility of the changes that occurred in CNS WHO 2021 in four institutions with limited resources. Our findings demonstrate no major alterations in patient management in low resource settings and significant added financial impact. While there is no doubt that the revised classification provides greater insight into tumor biology and molecular/genetic features of CNS tumors, its practical benefit and applicability in the majority of cases worldwide are limited, and attempts to improve its utility in low resource settings are warranted.

NIMG-82. FET PET SENSITIVITY IN PRIMARY BRAIN TUMORS ACCORDING TO CNS WHO 2021: VALUE AND PITFALLS IN PRESURGICAL IMPLEMENTATION

Abstract BACKGROUND FET-PET imaging has emerged as a valuable tool in the characterization of unclear brain lesions that cannot be clearly diagnosed with conventional diagnostic methods. In 2021, the Central Nervous System (CNS) World Health Organization (WHO) introduced significant changes in the classification of CNS tumors. Notably, under the new classification, certain cases of WHO grade II glioma may be reclassified as glioblastoma. Unfortunately, a subset of grade II gliomas, as defined by the 2016 WHO classification, may exhibit no detectable FET uptake and lack contrast enhancement on MRI, which can lead to underestimation of such tumors and potentially result in inappropriate treatment decisions, including avoiding surgery. Question: In how many cases did the diagnostics consisting of MRI and FET PET underestimate the malignancy in unclear brain lesions? METHODS In this study, we conducted a retrospective analysis of all patients who underwent FET PET imaging of the brain between 2017 and June 2021, prior to their first surgical brain intervention. We obtained the results from presurgical MRI-scans and categorized the lesions based on whether they exhibited contrast media enhancement or not, in addition to the postsurgical molecular-histopathologic diagnosis. Our analysis focused on identifying the number of cases in which the diagnosis of malignancy was underestimated in the context of the WHO 2016 classification. To further investigate these cases, we conducted NGS and 850k-analysis to reclassify the results in accordance with the new CNS WHO 2021 classification. RESULTS After enrolling 85 patients, our study found that according to the 2016 classification, preoperative imaging underestimated malignancy in 14 cases (16%) where histopathological analysis revealed high-grade malignant brain tumors. In 32 cases (27%), the preoperative imaging correctly suggested a malignant tumor, which was later confirmed by histopathological analysis after surgery. The final results of this reclassification after the CNS-WHO-2021 will be presented at SNO-meeting.

Pediatric brain tumors

Pediatric central nervous system (CNS) tumors differ in their relative frequency, location, histology, biological behavior and prognosis from tumors in adults. Accurate neuropathological classification of CNS tumors is essential for therapeutic decisions and inclusion in therapy optimization studies. Tissue samples are analyzed by standardized conventional histological, immunohistological and molecular pathological methods and diagnosed according to the current World Health Organization (WHO) classification for CNS tumors (2021). By identifying characteristic genetic alterations and specific epigenetic signatures, the precision in the classification of pediatric brain tumors has significantly improved in recent years. The WHO classification allows aworldwide uniform, standardized classification of brain tumors and forms the basis for comparability of international epidemiological and clinical data. In some tumor types, such as childhood gliomas and embryonal tumors, key molecules and signaling pathways have been identified in recent years that represent starting points for new mechanism-based therapeutic modalities in the treatment of these patients.

Bridging the age gap: a review of molecularly informed treatments for glioma in adolescents and young adults.

Gliomas are the most common primary central nervous system (CNS) tumors and a major cause of cancer-related mortality in children (age <15 years), adolescents and young adults (AYA, ages 15-39 years), and adults (age >39 years). Molecular pathology has helped enhance the characterization of these tumors, revealing a heterogeneous and ever more complex group of malignancies. Recent molecular analyses have led to an increased appreciation of common genomic alterations prevalent across all ages. The 2021 World Health Organization (WHO) CNS tumor classification, 5th edition (WHO CNS5) brings forward a nomenclature distinguishing "pediatric-type" and "adult-type" gliomas. The spectrum of gliomas in AYA comprises both "pediatric-like" and "adult-like" tumor entities but remains ill-defined. With fragmentation of clinical management between pediatric and adult centers, AYAs face challenges related to gaps in medical care, lower rates of enrollment in clinical trials and additional psychosocial and economic challenges. This calls for a rethinking of diagnostic and therapeutic approaches, to improve access to appropriate testing and potentially beneficial treatments to patients of all ages.

Current status of DNA methylation profiling in neuro-oncology as a diagnostic support tool: A review.

Over the last 2 decades, high throughput genome-wide molecular profiling has revealed characteristic genetic and epigenetic alterations associated with different types of central nervous system (CNS) tumors. DNA methylation profiling has emerged as an important molecular platform for CNS tumor classification with improved diagnostic accuracy and patient risk stratification in comparison to the standard of care histopathological analysis and any single molecular tests. The emergence of DNA methylation arrays have also played a crucial role in refining existing types and the discovery of new tumor types or subtypes. The adoption of methylation data into neuro-oncology has been greatly aided by the development of a freely accessible machine learning-based classifier. In this review, we discuss methylation workflow, address the utility of DNA methylation profiling in CNS tumors in a routine diagnostic setting, and provide an overview of the methylation-based tumor types and new types or subtypes identified with this platform.

Vascular differences between IDH-wildtype glioblastoma and astrocytoma IDH-mutant grade 4 at imaging and transcriptomic levels.

Global agreement in central nervous system (CNS) tumor classification is essential for predicting patient prognosis and determining the correct course of treatment, as well as for stratifying patients for clinical trials at international level. The last update by the World Health Organization of CNS tumor classification and grading in 2021 considered, for the first time, IDH-wildtype glioblastoma and astrocytoma IDH-mutant grade 4 as different tumors. Mutations in the genes isocitrate dehydrogenase (IDH) 1 and 2 occur early and, importantly, contribute to gliomagenesis. IDH mutation produces a metabolic reprogramming of tumor cells, thus affecting the processes of hypoxia and vascularity, resulting in a clear advantage for those patients who present with IDH-mutated astrocytomas. Despite the clinical relevance of IDH mutation, current protocols do not include full sequencing for every patient. Alternative biomarkers could be useful and complementary to obtain a more reliable classification. In this sense, magnetic resonance imaging (MRI)-perfusion biomarkers, such as relative cerebral blood volume and flow, could be useful from the moment of presurgery, without incurring additional financial costs or requiring extra effort. The main purpose of this work is to analyze the vascular and hemodynamic differences between IDH-wildtype glioblastoma and IDH-mutant astrocytoma. To achieve this, we evaluate and validate the association between dynamic susceptibility contrast-MRI perfusion biomarkers and IDH mutation status. In addition, to gain a deeper understanding of the vascular differences in astrocytomas depending on the IDH mutation, we analyze the transcriptomic bases of the vascular differences.

Magnetic resonance spectroscopic correlates of progression free and overall survival in "glioblastoma, IDH-wildtype, WHO grade-4".

The 2021 World Health Organization (WHO) Central Nervous System (CNS) Tumor Classification has suggested that isocitrate dehydrogenase wildtype (IDH-wt) WHO grade-2/3 astrocytomas with molecular features of glioblastoma should be designated as "Glioblastoma, IDH-wildtype, WHO grade-4." This study analyzed the metabolic correlates of progression free and overall survival in "Glioblastoma, IDH-wildtype, WHO grade-4" patients using short echo time single voxel 1H-MRS. Fifty-seven adult patients with hemispheric glioma fulfilling the 2021 WHO CNS Tumor Classification criteria for "Glioblastoma, IDH-wildtype, WHO grade-4" at presurgery time point were included. All patients were IDH1/2-wt and TERTp-mut. 1H-MRS was performed on a 3 T MR scanner and post-processed using LCModel. A Mann-Whitney U test was used to assess the metabolic differences between gliomas with or without contrast enhancement and necrosis. Cox regression analysis was used to assess the effects of age, extent of resection, presence of contrast enhancement and necrosis, and metabolic intensities on progression-free survival (PFS) and overall survival (OS). Machine learning algorithms were employed to discern possible metabolic patterns attributable to higher PFS or OS. Contrast enhancement (p = 0.015), necrosis (p = 0.012); and higher levels of Glu/tCr (p = 0.007), GSH/tCr (p = 0.019), tCho/tCr (p = 0.032), and Glx/tCr (p = 0.010) were significantly associated with shorter PFS. Additionally, necrosis (p = 0.049), higher Glu/tCr (p = 0.039), and Glx/tCr (p = 0.047) were significantly associated with worse OS. Machine learning models differentiated the patients having longer than 12 months OS with 81.71% accuracy and the patients having longer than 6 months PFS with 77.41% accuracy. Glx and GSH have been identified as important metabolic correlates of patient survival among "IDH-wt, TERT-mut diffuse gliomas" using single-voxel 1H-MRS on a clinical 3 T MRI scanner.

Oligodendrocyte Transcription Factor 2 as a Potential Prognostic Biomarker of Glioblastoma: Kaplan-Meier Analysis and the Development of a Binary Predictive Model Based on Visually Accessible Rembrandt Image and Magnetic Resonance Imaging Radiomic Features.

Oligodendrocyte transcription factor 2 (OLIG2) is universally expressed in human glioblastoma (GB). Our study explores whether OLIG2 expression impacts GB patients' overall survival and establishes a machine learning model for OLIG2 level prediction in patients with GB based on clinical, semantic, and magnetic resonance imaging radiomic features. Kaplan-Meier analysis was used to determine the optimal cutoff value of the OLIG2 in 168 GB patients. Three hundred thirteen patients enrolled in the OLIG2 prediction model were randomly divided into training and testing sets in a ratio of 7:3. The radiomic, semantic, and clinical features were collected for each patient. Recursive feature elimination (RFE) was used for feature selection. The random forest (RF) model was built and fine-tuned, and the area under the curve was calculated to evaluate the performance. Finally, a new testing set excluding IDH-mutant patients was built and tested in a predictive model using the fifth edition of the central nervous system tumor classification criteria. One hundred nineteen patients were included in the survival analysis. Oligodendrocyte transcription factor 2 was positively associated with GB survival, with an optimal cutoff of 10% (P = 0.00093). One hundred thirty-four patients were eligible for the OLIG2 prediction model. An RFE-RF model based on 2 semantic and 21 radiomic signatures achieved areas under the curve of 0.854 in the training set, 0.819 in the testing set, and 0.825 in the new testing set. Glioblastoma patients with ≤10% OLIG2 expression tended to have worse overall survival. An RFE-RF model integrating 23 features can predict the OLIG2 level of GB patients preoperatively, irrespective of the central nervous system classification criteria, further guiding individualized treatment.