Background/Objectives Glioblastoma (GBM) is the most aggressive type of primary brain cancer, affecting children and adults alike and is associated with a poor prognosis (less than 15 months). It is therefore critical to develop more effective therapies that improve survival and reduce side effects for these lethal brain cancers. Glioblastoma cells constantly reprogram their metabolism to adapt to changing conditions. As current treatments are not effective, the need for a deeper understanding of metabolic processes and signaling pathways grows critical. Methods To support this, we have established a collection of human GBM cell cultures derived from freshly resected tumor tissues from 78 patients undergoing surgery at CHU de Québec – Université Laval. We aimed to characterize this cell lines by combining histologic, metabolomic and gene expression analysis. Results Among the 21 cases that successfully generated cell lines, all were IDH-wildtype (IDH-wt) GBMs, with molecular profiles, including Ki-67 proliferation index, ATRX status, p53 expression, and MGMT promoter methylation. Characterization of five cell lines revealed significant metabolic and phenotypic heterogeneity, with differences in amino acid levels, TCA cycle metabolites, and RNA expression of GBM subtype markers. Our data suggest that mesenchymal-like GBM cells exhibit higher metabolic activity compared to proneural-like cells. Conclusions Together, the analyses highlight the heterogeneity of GBM cells across patients. These findings emphasize the complexity of GBM and the need for personalized approaches in treatment, taking into account the diverse metabolic and molecular characteristics of GBM cells. ### Competing Interest Statement The authors have declared no competing interest. Canadian Cancer Society, https://ror.org/017343w90, 1057382

Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinical/research workflows. Here, we present an online collaborative platform that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/ ). Specifically, PHARAOH uses a weakly supervised, human-in-the-loop learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched annotation by human experts. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed efficiently and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Moreover, by using our PHARAOH pipeline, we showcase how correlation of cohort-level cytoarchitectural features with accompanying biological and outcome data can help systematically devise interpretable morphometric models of disease. Both the custom model design and feature extraction pipelines are amenable to crowdsourcing, positioning PHARAOH to become a fully scalable, systems-level solution for the expansion, generalization and cataloging of computational pathology applications. Faust, Chen, and colleagues present PHARAOH, a collaborative computational pathology platform that allows histologists to quickly develop custom labelled image datasets to train and catalogue a variety of machine learning models for histopathological analysis.

ObjectiveParkinson's and Huntington's diseases are characterized by progressive neuronal loss. Previous studies using human postmortem tissues have shown the impact of neurodegenerative disorders on adult neurogenesis. The extent to which adult neural stem cells are activated in the subventricular zone and whether therapeutic treatments such as deep brain stimulation promote adult neurogenesis remains unclear. The goal of the present study is to assess adult neural stem cells activation and neurogenesis in the subventricular zone of patients with Huntington's and Parkinson's diseases who were treated or not by deep brain stimulation.MethodsPostmortem brain samples from Huntington's and Parkinson's disease patients who had received or not long‐term deep brain stimulation of the subthalamic nucleus were used.ResultsOur results indicate a significant increase in the thickness of the subventricular zone and in the density of proliferating cells and activated stem cells in the brain of Huntington's disease subjects and Parkinson's disease patients treated with deep brain stimulation. We also observed an increase in the density of immature neurons in the brain of these patients.InterpretationOverall, our data indicate that long‐term deep brain stimulation of the subthalamic nucleus promotes cell proliferation and neurogenesis in the subventricular zone that are reduced in Parkinson's disease. Taken together, our results also provide a detailed characterization of the cellular composition of the adult human subventricular zone and caudate nucleus in normal condition and in Parkinson's and Huntington's diseases and demonstrate the plasticity of these regions in response to neurodegeneration. ANN NEUROL 2025

Glioblastoma (GBM) is a highly aggressive primary brain cancer with poor prognosis (<15 months), highlighting the urgent need for more effective therapies. As current treatments are not effective, the need for a deeper understanding of the biology of GBM cells, including how they reprogram their metabolism to support their aberrant and uncontrolled growth, is critical. To this end, we established a collection of 41 human glioma cell lines derived from freshly resected tumour tissues from 99 patients. We characterized 12 of these cell lines by combining histologic, genetic, stem cell derivation and self-renewal, and metabolomic analyses. Histological and genetic profiles included IDH mutation status, Ki-67 proliferation index, ATRX status, mutant TP53 expression, chromosome 10q loss, EGFR amplification, and MGMT promoter methylation. Of these, only p53 mutation expression status showed weak segregation of the cell lines into 2 separate metabolic groups based on amino acid levels, but none showed an effect on stem cell derivation or self-renewal. Further characterization of these 12 cell lines revealed significant metabolic and phenotypic differences when comparing mesenchymal versus proneural gene expression subtyping. We show significant increases in TCA cycle metabolites in mesenchymal-like GBM cells and higher overall metabolic activity compared to proneural-like cells. These findings highlight the complexity of GBM and the need for personalized treatments that consider the metabolome of each subtype as a potential therapeutic avenue.

BackgroundGliomas are among the most malignant tumors, with a very poor prognosis. Early diagnosis is highly desirable since it can help implement more effective treatments for smaller tumors, which have not yet extensively metastasized. Improving early diagnosis may facilitate access of patients to clinical trials and prepare them for the future availability of new disease-modifying treatments.MethodsWe analyzed retrospective samples collected at diagnosis (before therapy initiation), with PEA (Olink Proteomics), quantifying about 3000 proteins. We utilized 30 plasmas from gliomas (20 glioblastomas, 5 anaplastic astrocytomas, 5 anaplastic oligodendrogliomas) and 20 meningiomas (as controls). We then analyzed the data to identify proteins which either alone, or in combination, could discriminate gliomas from meningiomas, or correlate with clinical and molecular alterations.ResultsWe identified 8 plasma proteins which were increased in gliomas vs. meningiomas (GFAP, NEFL, EDDM3B, PROK1, MMP3, CTRL, GP2, SPINT3) and 4 proteins which were decreased in gliomas vs. meningiomas (FABP4, ALDH3A1, IL-12B and OXT). Partition algorithms and logistic regression algorithms with two biomarkers (GFAP and FABP4) achieved sensitivity of 83% and 93% at 100% and 90% specificity, respectively. The strongest single marker was GFAP with an area under the ROC curve (AUC) of 0.86. The AUC for the GFAP-FABP4 combination was 0.98.ConclusionPEA is a powerful new proteomic technology for biomarker discovery. GFAP and a handful of other plasma biomarkers may be useful for early glioma detection and probably, prognosis.StatementDetecting gliomas as early as possible is highly desirable since it can significantly improve the chances of effective treatments. Reliable glioma biomarkers can timely inform glioma patients about the efficacy of their prescribed treatment. Our results reveal some novel putative glioma markers that may prove valuable, when used alone or in combination, towards improved clinical care of gliomas. In order to better appreciate the potential usefulness of these markers, their performance needs to be further validated in a larger cohort of samples.

Glioblastoma is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance and progression. While regional and single cell transcriptomic variations of glioblastoma have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned across glioblastoma's hallmark histomorphologic niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to distinct histologic patterns across 20 patients and propose diverse molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Moreover, we highlight differential drug sensitivities and relative chemoresistance in glioblastoma cell lines with enhanced KRAS programs. Importantly, these pharmacological differences are less pronounced in transcriptional glioblastoma subgroups suggesting that this model may provide insights for targeting heterogeneity and overcoming therapy resistance.

Cerebrovascular disease (CVD) has been associated with cognitive impairment. Yet, our understanding of vascular contribution to cognitive decline has been limited by heterogeneity of definitions and assessment, as well as its occurrence in cognitively healthy aging. Therefore, we aimed to establish the natural progression of CVD associated with aging. We conducted a retrospective observational study of 63 cognitively healthy participants aged 19-84 years selected through the histological archives of the CHU de Quebec. Assessment of CVD lesions was performed independently by 3 observers blinded to clinical data using the Vascular Cognitive Impairment Neuropathology Guidelines (VCING). We found moderate to almost perfect interobserver agreement for most regional CVD scores. Atherosclerosis (rho = 0.758) and arteriolosclerosis (rho = 0.708) showed the greatest significant association with age, followed by perivascular hemosiderin deposits (rho = 0.432) and cerebral amyloid angiopathy (CAA; rho = 0.392). Amyloid and tau pathologies were both associated with higher CVD load, but only CAA remained significantly associated with amyloid plaques after controlling for age. Altogether, these findings support the presence of multiple CVD lesions in the brains of cognitively healthy adults, the burden of which increases with age and can be quantified in a reproducible manner using standardized histological scales such as the VCING.

Adult infiltrating gliomas are highly aggressive tumors of the central nervous system with a dismal prognosis despite intensive multimodal therapy (chemotherapy and/or radiotherapy). In this study, we studied the expression, methylation and interacting miRNA profiles of GABA-, glutamate- and calcium-related genes in 661 adult infiltrating gliomas available through the TCGA database. Neurotransmitter-based unsupervised clustering identified three established glioma molecular subgroups that parallel major World Health Organization glioma subclasses (IDH-wildtype astrocytomas, IDH-mutant astrocytomas, IDH-mutant oligodendroglioma). In addition, this analysis also defined a novel, neurotransmitter-related glioma subgroup (NT-1), mostly comprised of IDH-mutated gliomas and characterized by the overexpression of neurotransmitter-related genes. Lower expression of neurotransmission-related genes was correlated with increased aggressivity in hypomethylated IDH-wildtype tumors. There were also significant differences in the composition of the tumor inflammatory microenvironment between neurotransmission-based tumor categories, with lower estimated pools of M2-phenotype macrophages in NT-1 gliomas. This multi-omics analysis of the neurotransmission expression landscape of TCGA gliomas-which highlights the existence of neurotransmission-based glioma categories with different expression, epigenetic and inflammatory profiles-supports the existence of operational neurotransmitter signaling pathways in adult gliomas. These findings could shed new light on potential vulnerabilities to exploit in future glioma-targeting drug therapies.

Abstract Glioblastoma (GBM) is an aggressive brain tumor with an expected survival of under 15 months despite spirited multimodal therapy. This grim outlook has remained virtually unchanged over the last 40 years and thus necessitates alternative research and therapeutic development strategies. Historically, traditional models of cancer biology have largely considered GBM tissue to be a homogenous mass. Emerging studies however now suggests that the interaction of tumor cells with various normal components of the brain and immune system play an important role in helping cancer grow and develop resistance to therapies. Better understanding of these hallmark features of GBM, collectively known as the “tumor microenvironment” (TME), could lead to the development of new and more effective therapies. In light of this, there is a renewed interest in revisiting our theories of cancer and preserving the microscopic anatomy of GBM in our molecular profiling efforts. Here we leverage laser capture microdissection (LCM) and mass spectrometry-based proteomics, in order to generate a detailed map of the distribution of proteins within tissues across a large number of patients. Specifically, we will isolate, and profile well-understood microscopic components of GBM: cellular tumor (CT), microvascular proliferation (MVP), infiltrating tumor (IT), palisading cells around necrosis (PAN) and normal brain tissue (LE). All of this data will be provided in an online-based GBM atlas as a publicly available resource. This atlas and the associated database for clinical and genomic data will serve as a useful platform for developing therapeutics and testing novel hypotheses related to GBM biology. Citation Format: K.H. Brian Lam, Ugljesa Djuric, Ihor Batruch, Maxime Richer, Phedias Diamandis. An anatomic proteomic atlas of human glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2694.

Classification of primary CNS tumours is currently achieved by complementing histologic analysis with molecular information, in accordance with the WHO guidelines, and aims at providing accurate prognosis and optimal patient management. cIMPACT-NOW update 3 now recommends grading diffuse IDH-wild type astrocytomas as grade IV glioblastomas if they bear one or more of the following molecular alterations: EGFR amplification, TERT promoter mutation, and whole-chromosome 7 gain combined with chromosome 10 loss. In this reanalysis of the Cancer Genome Atlas (TCGA) glioma expression datasets, we identified 14 IDH-wt infiltrating astrocytic gliomas displaying a “normal-like (NL)” transcriptomic profile associated with a longer survival rate. Some of these tumours would be considered as GBM-equivalents with the current diagnostic algorithm. A k-nearest neighbors model was used to identify 3-gene signatures able to identify NL IDH-WT gliomas. Genes such as C5AR1 (complement receptor) SLC32A1 (vesicular gamma-aminobutyric acid transporter), and SMIM10L2A (long non-coding RNA) were overrepresented in these signatures which were validated further using the Chinese Glioma Genome and Ivy Glioblastoma Atlases. They showed high discriminative power and correlation with survival. This finding could lead to the validation of an immunohistochemical or PCR test which would facilitate classification of IDH-WT astrocytomas with unclear histological grading. Furthermore, associated signaling pathways might represent novel treatment targets for aggressive tumours. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Reconsider recent updates in the WHO classification of infiltrating gliomas. 2. Discuss advanced bioinformatics profiling of the brain cancer transcriptome.

Background Classification of primary central nervous system tumors according to the World Health Organization guidelines follows the integration of histologic interpretation with molecular information and aims at providing the most precise prognosis and optimal patient management. According to the cIMPACT-NOW update 3, diffuse isocitrate dehydrogenase-wild type (IDH-WT) gliomas should be graded as grade IV glioblastomas (GBM) if they possess one or more of the following molecular markers that predict aggressive clinical course: EGFR amplification, TERT promoter mutation, and whole-chromosome 7 gain combined with chromosome 10 loss. Methods The Cancer Genome Atlas (TCGA) glioma expression datasets were reanalyzed in order to identify novel tumor subcategories which would be considered as GBM-equivalents with the current diagnostic algorithm. Unsupervised clustering allowed the identification of previously unrecognized transcriptomic subcategories. A supervised machine learning algorithm (k-nearest neighbor model) was also used to identify gene signatures specific to some of these subcategories. Results We identified 14 IDH-WT infiltrating gliomas displaying a "normal-like" (NL) transcriptomic profile associated with a longer survival. Genes such as C5AR1 (complement receptor), SLC32A1 (vesicular gamma-aminobutyric acid transporter), MSR1 (or CD204, scavenger receptor A), and SYT5 (synaptotagmin 5) were differentially expressed and comprised in gene signatures specific to NL IDH-WT gliomas which were validated further using the Chinese Glioma Genome Atlas datasets. These gene signatures showed high discriminative power and correlation with survival. Conclusion NL IDH-WT gliomas represent an infiltrating glioma subcategory with a superior prognosis which can only be detected using genome-wide analysis. Differential expression of genes potentially involved in immune checkpoint and amino acid signaling pathways is providing insight into mechanisms of gliomagenesis and could pave the way to novel treatment targets for infiltrating gliomas.

Childhood brain tumors have suspected prenatal origins. To identify vulnerable developmental states, we generated a single-cell transcriptome atlas of >65,000 cells from embryonal pons and forebrain, two major tumor locations. We derived signatures for 191 distinct cell populations and defined the regional cellular diversity and differentiation dynamics. Projection of bulk tumor transcriptomes onto this dataset shows that WNT medulloblastomas match the rhombic lip-derived mossy fiber neuronal lineage and embryonal tumors with multilayered rosettes fully recapitulate a neuronal lineage, while group 2a/b atypical teratoid/rhabdoid tumors may originate outside the neuroectoderm. Importantly, single-cell tumor profiles reveal highly defined cell hierarchies that mirror transcriptional programs of the corresponding normal lineages. Our findings identify impaired differentiation of specific neural progenitors as a common mechanism underlying these pediatric cancers and provide a rational framework for future modeling and therapeutic interventions.

Advancements in computer vision and artificial intelligence (AI) carry the potential to make significant contributions to health care, particularly in diagnostic specialties such as radiology and pathology. The impact of these technologies on physician stakeholders is the subject of significant speculation. There is however a dearth of information regarding the opinions, enthusiasm, and concerns of the pathology community at large. Here, we report results from a survey of 487 pathologist-respondents practicing in 54 countries, conducted to examine perspectives on AI implementation in clinical practice. Despite limitations, including difficulty with quantifying response bias and verifying identity of respondents to this anonymous and voluntary survey, several interesting findings were uncovered. Overall, respondents carried generally positive attitudes towards AI, with nearly 75% reporting interest or excitement in AI as a diagnostic tool to facilitate improvements in workflow efficiency and quality assurance in pathology. Importantly, even within the more optimistic cohort, a significant number of respondents endorsed concerns about AI, including the potential for job displacement and replacement. Overall, around 80% of respondents predicted the introduction of AI technology in the pathology laboratory within the coming decade. Attempts to identify statistically significant demographic characteristics (e.g., age, sex, type/place of practice) predictive of attitudes towards AI using Kolmogorov–Smirnov (KS) testing revealed several associations. Important themes which were commented on by respondents included the need for increasing efforts towards physician training and resolving medical-legal implications prior to the generalized implementation of AI in pathology.

PurposeThe present report of two fatal awake malignant hyperthermia (MH) episodes in an MH susceptible (MHS) family is intended to raise awareness among medical personnel and MHS individuals to the possibility of life-threatening non-anesthesia-triggered MH episodes and to provide a strong incentive for development of effective preventive measures.Clinical featuresTwo young athletic males (28 and 16 yr old), members of the same extended family with a history of anesthesia-related MH episodes and deaths, succumbed ten years apart on two different continents, with symptoms unrelated to anesthesia but strikingly similar to typical anesthetic-induced MH. Both suffered an abrupt surge in body temperature, tachycardia, tachypnea, muscle rigidity, hyperkalemia, and respiratory and metabolic acidosis. Despite aggressive resuscitation attempts, both developed cardiac arrest and died shortly upon arrival to hospital emergency departments. Autopsy analyses were negative for drugs, alcohol, or bacterial infection. Individual and familial genetic analyses revealed a novel, potentially pathogenic RYR1 variant (p.Gly159Arg) that co-segregates with the MHS phenotype in the family. Both fatal awake MH episodes are hypothesized to have been triggered by physical exertion compounded with a febrile illness that in one case was due to influenza type A.ConclusionsLife-threatening awake MH episodes may develop in some MHS individuals in the absence of anesthetic triggers. Potential triggers can be physical exertion in combination with a febrile illness. Malignant hyperthermia susceptible patients are recommended to be vaccinated against flu and restrict physical activities when febrile, wear an MH alert bracelet, and inform medical personnel of their MH history. Oral dantrolene is suggested to be available to MHS patients for administration with the early signs of awake MH.

Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. We leverage 100 Hematoxylin & Eosin (H&E)-stained whole slide images (WSIs) from 25 Balb/c mice with various level of brain metastatic tumor burden. A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs. Upon comparison with manually drawn regions, it is apparent that the algorithm learned to identify and segment cancer cell-specific nuclei and normal brain tissue. Our approach resulted in a robust and highly concordant correlation between automated metastases quantification of brain metastases and manual human assessment (R2 = 0.8783; P < 0.0001). This simple approach is amenable to other similar analyses, including that of human tissues. Widespread adoption of these tools aims to democratize ML and improve precision in traditionally qualitative tasks in histopathology-based research.

Effectiveness of chemotherapy for treating glioblastoma (GBM) brain tumors is hampered by the blood–brain barrier which limits the entry into the brain of most drugs from the blood. To bypass this barrier, convection-enhanced delivery (CED) was proposed to directly inject drugs in tumor. However, the benefit of CED may be hampered when drugs diffuse outside the tumor to then induce neurotoxicity. Encapsulation of drugs into liposome aims at increasing tumor cells specificity and reduces neurotoxicity. However, the most appropriate liposomal formulation to inject drugs into brain tumor by CED still remains to be determined. In this study, four liposomal carboplatin formulations were prepared and tested in vitro on F98 glioma cells and in Fischer rats carrying F98 tumor implanted in the brain. Impact of pegylation on liposomal surface and relevance of positive or negative charge were assessed.

Malignant glial tumors are associated with a poor prognosis, presenting a short median patient survival and a very limited response to therapies. Although the first line therapy is standardized, there exists no consensus as to which second line treatment modality is better. We thus sought to demonstrate the feasibility of transforming our newly established expertise into personalized treatments for glioma patients by developing an advanced in vivo Avatar model developed from patient derived tumors. The typical medical Avatar system entails implantation of patient tumor samples in immunodeficient mice for subsequent test in drug efficacy. As the generation of mouse Avatars is a slow and costly approach, many cancer patients are set to have a significant disease progression before the results from the mouse model become available. We recently developed a rapid and cost-effective, pre-clinical model that is well suited for precision medicine – the ex-ovo chicken embryo ChorioAllantoic Membrane (CAM) assay. We will present data indicating that tumor growth occurs very rapidly in ex ovo CAMs, with measurable tumors obtained within a few days, as opposed to several weeks in mice. Even though tumor sizes are smaller in the CAM than in mice, the engrafting rate is higher and tumor sizes are more uniform. Implantation of glioma tissue fragments from 25 patients led to the successful establishment of CAM xenograft tumors which faithfully recapitulate the histology of the primary tumor for the majority of patients. Furthermore, we observed a significant inhibition of tumor growth in CAMs treated with first and second line chemotherapeutic drugs. This next-generation Avatar model has the potential to become an asset for personalized medicine in gliomas treatment.

To present an unrecognized vascular complication of bacillus Calmette-Guérin (BCG) therapy administered for superficial bladder carcinoma. We also review the potential mimickers for primary angiitis of the central nervous system (PACNS) as well as complications of intravesical BCG therapy. An 89-year-old Caucasian man with a history of relapsing high-grade bladder carcinoma treated with intravesical BCG presented with recurring episodes of right upper limb paresthesia with clumsiness and dysarthria. Magnetic resonance imaging of the head revealed multiple predominantly left-sided frontotemporal micronodular peri-vascular lesions. Left frontal lobe biopsy showed non-necrotizing granulomatous vasculitis. Ziehl staining was negative. Initially, he was treated for PACNS but his symptoms relapsed during every attempt to taper the corticosteroids. Six months later, he developed bilateral mycobacterial endophthalmitis, caused by Mycobacterium bovis. Brain biopsy was reviewed and confirmed the presence of perivascular mycobacteria. A retrospective diagnosis of BCG-induced central nervous system vasculitis was made and he was treated with high-dose corticosteroids, moxifloxacin, isoniazid, ethambutol, and rifampicin. BCG is a live attenuated form of Mycobacterium bovis widely used as tuberculosis vaccination and intravesical therapy for superficial forms of bladder cancer. Systemic complications affect roughly 5% of patients and can manifest months or years after the last instillation. Cases of endophthalmitis, meningitis, aortitis, or mycotic aneurysms have been described, but no reports of CNS vasculitis have been found. In disseminated forms of BCG infections, referred to as BCGitis, histopathology usually reveals granulomatous inflammation. Mycobacterial cultures are often negative, making this a diagnostic challenge. This is the first documented case of BCG-induced small-vessel CNS vasculitis. Mycobacterium bovis infection is rare and findings are often nonspecific, making the diagnosis very difficult. Other infectious and non-infectious causes must be ruled out appropriately before considering this entity.

Chronic subdural hematoma (CSDH) is one of the most frequent reason for cranial neurosurgical consultation. There is no widely accepted medical treatment for this condition. Herein, we present the protocol for the Tranexamic Acid (TXA) in Chronic Subdural Hematomas (TRACS) trial aiming at determining whether TXA can increase the rate of CSDH resolution following conservative management, lower the number of required surgical procedures and decrease the rate of CSDH recurrence following surgical evacuation.