Explore the vast range of titles available.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging—with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology. An online approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups has been developed to help to improve current diagnostic standards. Classifying tumour types for better diagnoses Precise cancer diagnoses are essential to ensure the best treatment plans for patients, but standardization of the diagnostic process has been challenging. The authors present a comprehensive approach for DNA-methylation-based classification of brain tumours. The tool improves diagnostic precision of standard methods, and is made available online for broad accessibility. The results illustrate the potential applications of molecular diagnosis tools.

Central nervous system tumours represent one of the most lethal cancer types, particularly among children 1 . Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of resection and minimizing risk of neurological damage and comorbidity 2 , 3 . However, surgeons have limited knowledge of the precise tumour type prior to surgery. Current standard practice relies on preoperative imaging and intraoperative histological analysis, but these are not always conclusive and occasionally wrong. Using rapid nanopore sequencing, a sparse methylation profile can be obtained during surgery 4 . Here we developed Sturgeon, a patient-agnostic transfer-learned neural network, to enable molecular subclassification of central nervous system tumours based on such sparse profiles. Sturgeon delivered an accurate diagnosis within 40 minutes after starting sequencing in 45 out of 50 retrospectively sequenced samples (abstaining from diagnosis of the other 5 samples). Furthermore, we demonstrated its applicability in real time during 25 surgeries, achieving a diagnostic turnaround time of less than 90 min. Of these, 18 (72%) diagnoses were correct and 7 did not reach the required confidence threshold. We conclude that machine-learned diagnosis based on low-cost intraoperative sequencing can assist neurosurgical decision-making, potentially preventing neurological comorbidity and avoiding additional surgeries. Sturgeon is a pretrained neural network that uses incremental results from nanopore sequencing to rapidly classify central nervous system tumours and can be used to aid critical decision-making during surgery.

The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M–mutant; RELA fusion–positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma—a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.

Key Points The 2016 WHO Classification of Tumours of the Central Nervous System reflects a paradigm shift, replacing traditional histology-based glioma diagnostics with an integrated histological and molecular classification system that enables more-precise tumour categorization The requisite diagnostic biomarkers in the 2016 WHO classification of gliomas are IDH1/2 (IDH) mutations, 1p/19q codeletion, H3F3A or HIST1H3B/C K27M (H3-K27M) mutations and C11orf95–RELA fusions Additional diagnostically relevant biomarkers include loss of nuclear ATRX expression, TERT -promoter mutations, KIAA1549–BRAF fusions, BRAF -V600E mutation, H3F3A -G34 mutation, and several other alterations associated with rare glioma entities MGMT -promoter methylation is predictive of benefit from alkylating chemotherapy in patients with IDH-wild-type glioblastoma; predictive biomarkers for targeted therapies, such as IDH1 and BRAF mutations, are also emerging Novel methods for large-scale DNA-methylation, copy-number and mutational profiling will further advance the assessment of glioma-associated molecular biomarkers Clinical trials require assessment of molecular biomarkers as criteria for study entry and/or patient stratification; predictive DNA sequencing followed by targeted therapy will support the implementation of precision medicine in neuro-oncology In 2016, a revised WHO classification of glioma was published, in which molecular data and traditional histological information are incorporated into integrated diagnoses. Herein, the authors highlight the developments in our understanding of the molecular genetics of gliomas that underlie this classification, and review the current landscape of molecular biomarkers used in the classification of disease subtypes. In addition, they discuss how these advances can promote the development of novel pathogenesis-based therapeutic approaches, paving the way to precision medicine. Genome-wide molecular-profiling studies have revealed the characteristic genetic alterations and epigenetic profiles associated with different types of gliomas. These molecular characteristics can be used to refine glioma classification, to improve prediction of patient outcomes, and to guide individualized treatment. Thus, the WHO Classification of Tumours of the Central Nervous System was revised in 2016 to incorporate molecular biomarkers — together with classic histological features — in an integrated diagnosis, in order to define distinct glioma entities as precisely as possible. This paradigm shift is markedly changing how glioma is diagnosed, and has important implications for future clinical trials and patient management in daily practice. Herein, we highlight the developments in our understanding of the molecular genetics of gliomas, and review the current landscape of clinically relevant molecular biomarkers for use in classification of the disease subtypes. Novel approaches to the genetic characterization of gliomas based on large-scale DNA-methylation profiling and next-generation sequencing are also discussed. In addition, we illustrate how advances in the molecular genetics of gliomas can promote the development and clinical translation of novel pathogenesis-based therapeutic approaches, thereby paving the way towards precision medicine in neuro-oncology.

The fifth edition of the WHO classification of CNS Tumors (WHO CNS5) has revised the diagnostic and grading criteria for Adult‐type Diffuse Gliomas (ADGs) by integrating molecular parameters with histologic features. Conducting molecular testing for most ADGs is now crucial in fulfilling the WHO CNS5 diagnostic criteria. However, due to additional costs and technical barriers, implementing molecular diagnostics is often not feasible in Low‐Income Countries (LICs) and Lower Middle‐Income Countries (LMICs). Therefore, practical approaches are needed for diagnosis in resource‐restrained settings. Hence, the Asian Oceanian Society of Neuropathology (AOSNP), through the ‘ADAPTR’ (Adapting Diagnostic Approaches for Practical Taxonomy in Resource‐Restrained Regions) initiative, aimed to provide resource‐stratified recommendations for diagnosing ADGs based on available resources while adhering to the WHO guidelines as much as possible. ADAPTR identified different resource levels (RLs) of diagnostic pathology services, ranging from RL I to RL V, with RL I to RL IV being applicable to the LMICs, and provides recommendations for a ‘Histology‐oriented integrated diagnosis format’ for each tumor type at different RLs. In addition, diagnostic flow charts for ADGs have been generated to suit these RLs. The emphasis is mainly on using histopathological approaches with immunohistochemistry, while molecular testing recommendation is categorized as ‘can be considered’, ‘highly recommended’ or ‘obligatory’, to reach the next level diagnosis. In each RL, either a WHO CNS5 diagnosis with an accompanying CNS WHO grade or an ADAPTR descriptive diagnosis with an associated ADAPTR histologic grade is provided, depending on the context. ADAPTR recommendations are therefore a practical adaptation of the WHO CNS5 guidelines that will suit routine diagnostic practices in resource‐restrained regions. ADAPTR recommendations for Adult‐type Diffuse Gliomas in Resource‐restrained settings.

The recent publication of the 2016 World Health Organization Classification of Tumors of the Central Nervous System (2016 CNS WHO) represents a significant advance in the classification of human brain tumors [ 1]. For the first time, a CNS WHO classification defines diagnostic entities by combining molecular and histological information. In doing so, the classification facilitates more precise diagnosis of well-understood entities and clearer designation of less-understood entities, which will in turn allow further study and likely future advances in their classifications.

Tumour diversity Ependymoma is a type of neural tumour that arises throughout the central nervous system. Using comparative transcriptomics in mouse and human tumours, Johnson et al . home in on mutations that are specific to individual tumour subgroups. In the course of their study, the authors generate the first mouse model of ependymoma and demonstrate the power of interspecific genomic comparisons to interrogate cancer subgroups. Ependymoma is a type of neural tumour that arises throughout the central nervous system. Using comparative transcriptomics in mouse and human tumours, these authors home in on mutations that are specific to individual tumour subgroups. In doing so, they generate the first mouse model of ependymoma and demonstrate the power of interspecific genomic comparisons to interrogate cancer subgroups. Understanding the biology that underlies histologically similar but molecularly distinct subgroups of cancer has proven difficult because their defining genetic alterations are often numerous, and the cellular origins of most cancers remain unknown 1 , 2 , 3 . We sought to decipher this heterogeneity by integrating matched genetic alterations and candidate cells of origin to generate accurate disease models. First, we identified subgroups of human ependymoma, a form of neural tumour that arises throughout the central nervous system (CNS). Subgroup-specific alterations included amplifications and homozygous deletions of genes not yet implicated in ependymoma. To select cellular compartments most likely to give rise to subgroups of ependymoma, we matched the transcriptomes of human tumours to those of mouse neural stem cells (NSCs), isolated from different regions of the CNS at different developmental stages, with an intact or deleted Ink4a/Arf locus (that encodes Cdkn2a and b). The transcriptome of human supratentorial ependymomas with amplified EPHB2 and deleted INK4A/ARF matched only that of embryonic cerebral Ink4a/Arf −/− NSCs. Notably, activation of Ephb2 signalling in these, but not other, NSCs generated the first mouse model of ependymoma, which is highly penetrant and accurately models the histology and transcriptome of one subgroup of human supratentorial tumour. Further, comparative analysis of matched mouse and human tumours revealed selective deregulation in the expression and copy number of genes that control synaptogenesis, pinpointing disruption of this pathway as a critical event in the production of this ependymoma subgroup. Our data demonstrate the power of cross-species genomics to meticulously match subgroup-specific driver mutations with cellular compartments to model and interrogate cancer subgroups.

Purpose Solitary fibrous tumors (SFTs) are a rare type of mesenchymal tumors. The World Health Organization reclassified SFTs in 2021. Currently, guidelines concerning treatment and follow-up are lacking. We performed a retrospective case series with reclassification of SFTs, according to the most recent WHO classification, to explore tumor-behavior. The purpose is to build a framework for long-term patient surveillance. Methodology A retrospective case study was performed according to the PROCESS guidelines. Inclusion criteria were: patients operated on between 2013 and 2023 in two neurosurgical centers with the diagnosis of ‘hemangiopericytoma’ or SFT on histopathological stains. Patients were excluded if the original stains of the primary tumor were unavailable. The following demographic, radiologic and therapeutic parameters were included in the review: age, sex, original and reclassified anatomopathological diagnosis, location, extent of resection, use of postoperative radiotherapy, location of and time to recurrence, location of—and time to metastasis, and survival. Histological material was re-examined by experienced neuropathologists. Results Ten patients were identified with a solitary fibrous tumor of the central nervous system (CNS) (three females) between 2013 and 2023. Age at diagnosis ranged from 38 up to 81. Eight patients were treated by gross total resection (GTR) and postoperative radiotherapy (RT) was applied in five cases. Initial WHO grading consisted of three grade I, two grade II, and six grade III lesions. Reclassification according to the WHO 2021 classification of CNS tumors resulted in seven reclassifications, all towards a lower grade. Four patients showed local recurrence, six to eight years after diagnosis, and five patients developed systemic metastases, nine to 13 years after diagnosis. Discussion Although rare, SFT should be included in the differential diagnosis of intracranial tumors with extra-axial growth patterns. The current histological grade according to the WHO 2021 does not seem to account for local recurrence rate or systemic metastasis. When a solitary fibrous tumor is presumed, gross total resection is the recommended treatment. Lifelong patient follow-up is necessary due to the risk of delayed recurrence and distant metastasis, even after gross-total resection. We would advocate for the use of CT thorax-abdomen or full body PET in the detection of systemic metastases at diagnosis and during follow-up, however optimal intervals remain unclear.

Recent advances in artificial intelligence (AI) and computer vision empower deep-learning models to infer molecular features from histopathological images to classify CNS tumours. The aim of this study was to test the classification accuracy of a molecular inference-based AI assistant for CNS tumour diagnosis. In this multi-institutional, retrospective study, we used data from whole slide images of samples from patients aged 0–95 years, diagnosed with primary or recurrent CNS tumours. Reference diagnostic labels were determined by DNA methylation-based tumour classification to match one of 52 tumour types selected to encompass most types of gliomas, embryonal tumours, and meningeal and mesenchymal tumours encountered in clinical practice. The Neuropath-AI model was trained on 5835 samples from the National Cancer Institute (NCI; USA), the Children's Brain Tumor Network (USA), and the Digital Brain Tumour Atlas (Austria) to infer molecular features from whole slide images and to use these to predict tumour types with associated confidence scores. The test cohort comprised 5516 samples identified in laboratory archives between May 17, 2024, and May 13, 2025, from the NCI, Northwestern Medicine (USA), University of Pittsburgh Medical Center (USA), and University College London (UK). There were 2753 (50%) female and 2763 (50%) male patients, median age 43 years (IQR 25–59). The primary objective was to measure the classification accuracy of the model family-level and terminal classification predictions in test samples, with coprimary endpoints of sample coverage and prediction and balanced accuracy. Sample coverage was defined as samples receiving a model prediction with a confidence score above a specified threshold. Prediction accuracy and balanced accuracy were analysed in the covered samples (ie, those meeting the confidence criterion) and evaluated by comparing the top-1 or top-2 predictions with reference labels. Family-level classifications were reached in 5299 (96%) of 5516 samples. Predictions to one of the terminal classifications with at least moderate confidence were reached for 4772 (87%) samples. The single highest-scoring classification matched the reference label in 3817 (95% CI 3770–3865; 80% [95% CI 79–81]) of 4772 samples (balanced accuracy 66% [95% CI 63–70]). The two highest-scoring classifications contained the reference label in 4103 (95% CI 4056–4152; 86% [95% CI 85–87]) of 4772 samples (balanced accuracy 75% [95% CI 71–78]). Our model provides the basis for a clinically applicable deep-learning assistant to improve human efficiency and accuracy of CNS tumour diagnosis. The model will be made publicly available and could be implemented to assist human pathologists in future prospective studies. The Intramural Research Program of the National Institutes of Health.