Neuro-Oncology Advances

BackgroundPaediatric and adolescent gliomas and glioneuronal tumours remain challenging to assess non-invasively. Amide proton transfer (APT) chemical exchange saturation transfer (CEST) MRI has shown promise in adult gliomas but has not been well studied in younger patients. PurposeTo assess whether APT CEST signal can act as a non-invasive surrogate of tumour proliferation in adolescent CNS tumours by correlating it with 18F-choline PET uptake (SUV) as a proxy for membrane synthesis / proliferative activity. MethodsTen adolescent patients (14-19 yrs) with confirmed or suspected gliomas / glioneuronal tumours underwent simultaneous APT CEST and 18F-choline PET-MRI. Regions of interest (ROIs) corresponding to non-enhancing, enhancing, necrotic tumour, and contralateral white matter were delineated. Mean APT signal intensity (SI) and PET SUV were extracted per ROI. Nonparametric statistics and Spearmans correlation analyses were performed. ResultsAPT SI was significantly elevated in enhancing, non-enhancing, and necrotic tumour ROIs compared to normal white matter (p<0.001). 18F-choline SUV was elevated in enhancing and necrotic ROIs vs white matter, but not significantly so for non-enhancing tumour (p=0.02). A strong correlation between whole-tumour APT SI and 18F-choline SUV was seen (Spearman {rho}=0.86, p<0.001). ConclusionOur results indicate that APT CEST is feasible in adolescents and may reflect proliferative tumour burden. The detection of elevated APT SI even in non-enhancing tumour regions suggests potential utility in monitoring non-contrast-enhancing disease. Larger cohorts and multimodal correlation (e.g. Ki-67, amino acid PET) are warranted to confirm and extend these findings.

BackgroundIsocitrate dehydrogenase (IDH) mutation and 1p19q codeletion are important beneficial prognosticators in glioma. IDH and 1p19q diagnosis requires tissue sampling and there are likely benefits of presurgical diagnosis. Research supports the potential of MRI-based IDH and 1p19q diagnosis, however there is a paucity of external validation outside the widely used The Cancer Imaging Archive (TCIA) dataset. We present a combined IDH and 1p19q classification algorithm and assess performance on a local retrospective cohort (NZ) and the Erasmus Glioma Database (EGD). Methods2D convolutional neural networks are trained to provide IDH and 1p19q classification. Inputs are T1 post-contrast, T2, and FLAIR sequences. Training data consists of preoperative imaging from the TCIA dataset (n=184) and a locally obtained NZ dataset (n=349). Evaluation data consists of the most recent cases from the NZ dataset (n=205) and the EGD (n=420). ResultsIDH classification accuracy was 93.3% and 91.5% on the NZ and EDG, with AUC values of 95.4% and 95.8%, respectively. 1p19q accuracy was 94.5% and 87.5% with AUC values of 92.5% and 85.4% on the NZ and EGD datasets. Combined IDH and 1p19q accuracy was 90.4% and 84.3% on the NZ and EGD, with AUC values of 92.4% and 91.2%. ConclusionsHigh IDH and 1p19q classification performance was achieved on the NZ retrospective cohort. Performance generalised to the EGD demonstrating the potential for clinical translation. This method makes use of readily available imaging and has high potential impact in glioma diagnostics. Key Points- IDH and 1p19q are the main molecular markers in glioma. - Accurate predictions can be obtained from preoperative MRI without changes to imaging protocols. - Non-invasive diagnosis will likely enhance treatment planning and facilitate targeted preoperative therapies. Importance of the StudyThe 2021 WHO CNS tumour classification system formalises the increasing recognition of molecular factors like IDH and 1p19q in the prognostication and treatment of glioma. Emerging research shows the potential of artificial intelligence methods applied to preoperative MRI sequences to noninvasively predict molecular status. A limitation of the literature published to date is a lack of generalisation and external validation outside the widely used TCIA dataset. Here we present the performance of an MRI-based IDH and 1p19q classification tool evaluated on a large consecutive cohort from New Zealand and an independent publicly available dataset of MR images from the Netherlands. We demonstrate high predictive performance with robust generalisation, indicating the potential usefulness of this method in the workup of glioma. Reliable preoperative tumour characterisation may facilitate tailored treatment approaches and early decision making without the need for additional imaging.

BackgroundPrecise glioma segmentation in MRI is essential for accurate diagnosis, optimal treatment planning, and advancing clinical research. However, most deep learning approaches require complete, standardized MRI protocols that are frequently unavailable in routine clinical practice. This study presents and evaluates GlioMODA, a robust deep learning framework designed for automated glioma segmentation that delivers consistent high performance across varied and incomplete MRI protocols. MethodsGlioMODA was trained and validated on the BraTS 2021 dataset (1,251 training, 219 testing cases), systematically assessing performance across eleven clinically relevant MRI protocol combinations. Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and panoptic quality metrics. Volumetric accuracy was benchmarked against manual ground truth, and statistical significance was established via Wilcoxon signed-rank tests with Benjamini-Yekutieli correction. ResultsGlioMODA demonstrated state-of-the-art segmentation accuracy across tumor subregions, maintaining robust performance with incomplete or heterogeneous MRI protocols. Protocols including both T1-weighted contrast-enhanced and T2-FLAIR sequences yielded volumetric differences versus manual ground truth that were not statistically significant for enhancing tumor (ET: median difference 55 mm3, p = 0.157) and whole tumor (WT: median difference -7 mm3, p = 1.0), and exhibited median DSC differences close to zero relative to the four-sequence reference protocol. Omitting either sequence led to substantial and significant volumetric errors. ConclusionsGlioMODA facilitates reliable, automated glioma segmentation using a streamlined two-sequence protocol (T1-contrast + T2-FLAIR), supporting clinical workflow optimization and broader implementation of quantitative volumetry compatible with RANO 2.0 criteria. GlioMODA is published as an open-source, easy-to-use Python package at https://github.com/BrainLesion/GlioMODA/. Key PointsO_LIT1-CE + T2-FLAIR maintains enhancing and whole tumor segmentation comparable to four-sequence MRI. C_LIO_LIConsistent volumes with T1-CE + T2-FLAIR support reliable RANO 2.0 assessment. C_LIO_LIOpen-source GlioMODA (models + code) supports rapid integration. C_LI Importance of the StudyAutomated glioma segmentation is limited in practice by incomplete or heterogeneous MRI protocols. GlioMODA directly addresses this barrier by delivering consistent accuracy across 11 clinically relevant sequence combinations and identifying a streamlined protocol (T1-contrast and T2-FLAIR) whose enhancing- and whole-tumor volumes are not statistically different from expert reference. This enables shorter scans and reproducible volumetry compatible with RANO 2.0, facilitating reliable response assessment in trials and routine care. By releasing trained models and code as an easy-to-use open-source package, this work enables external validation and integration into neuro-oncology workflows.

BackgroundPediatric brain tumors are the leading cause of cancer death in children, with surgical resection critical for survival and neurodevelopment. Intraoperative molecular imaging has advanced in adults but remains limited in pediatrics. This review examines the availability of intraoperative metabolomic and molecular imaging including fluorescence-guided surgery, magnetic resonance imaging, and mass spectrometry, AI integration, and multi-modal imaging in pediatric brain tumor surgery. MethodsLiterature search was done in PubMed, Scopus, Web of Science, and Embase from 2010-2025. Included studies addressed intraoperative molecular imaging in pediatrics, metabolomic neurosurgery approaches, fluorescence-guided surgical techniques, or AI application in pediatric brain tumor care. ResultsOf 2,856 articles, 84 met criteria. Pediatric intraoperative imaging predominantly relies on magnetic resonance imaging (21 studies), with more limited metabolomic approaches (16 studies) and emerging fluorescence-guided surgery applications (9 studies). Intraoperative MRI increased gross total resection rates from approximately 67% with conventional surgery to 84-89% with iMRI guidance, while maintaining similar rates of new neurological deficits around 8%. Mass spectrometry shows promise for real-time tissue characterization but remains largely confined to adult neurosurgical populations. Fluorescence-guided surgery using 5-aminolevulinic acid (5-ALA) and sodium fluorescein has demonstrated safety in over 249 pediatric cases, with fluorescence utility correlating with tumor grade and proving most effective in glioblastoma (85% fluorescence rate) and anaplastic ependymoma (77%), but limited in pilocytic astrocytoma (26%) and medulloblastoma (39%). Artificial intelligence in pediatric neuroimaging improved tumor segmentation and outcome prediction across 15 studies, while two multimodal imaging studies integrating MRI with diffusion and PET demonstrated modified surgical plans in most cases involving eloquent brain regions and improved progression-free survival. Key gaps include: (1) limited pediatric metabolomic databases, (2) absence of real-time metabolomic platforms optimized for developing brains, (3) age-dependent variability in fluorescence-guided surgery efficacy, (4) insufficient integration of neurodevelopmental considerations into surgical planning, and (5) lack of standardized protocols for multi-modal imaging integration. ConclusionThe review highlights opportunities to advance intraoperative molecular imaging in pediatric neurosurgery via metabolomic-guided, fluorescence-guided, and AI-integrated approaches. Future research should develop pediatric-specific metabolomic platforms, optimize fluorescence imaging protocols for younger children, establish age-specific biomarker libraries, and create integrated decision-support systems considering oncological and neurodevelopment outcomes.

BackgroundTherapeutic intervention at glioblastoma (GBM) progression, as defined by current assessment criteria, is arguably too late as second-line therapies fail to extend survival. Still, most GBM trials target recurrent disease. We propose integration of a novel imaging biomarker to more confidently and promptly define progression and propose a critical timepoint for earlier intervention to extend therapeutic exposure. Patients/MethodsA retrospective review of 622 GBM patients between 2006-2019 yielded 135 meeting resection, clinical, and imaging inclusion criteria. We qualitatively and quantitatively analyzed 2000+ sequential brain MRIs (initial diagnosis to first progression) for development of T2 FLAIR signal intensity (SI) within the resection cavity (RC) compared to the ventricles (V) for quantitative inter-image normalization. PFS and OS were evaluated using Kaplan-Meier curves stratified by SI. Specificity and sensitivity were determined using a 2x2 table and pathology confirmation at progression. Multivariate analysis evaluated SI effect on the hazard rate for death after adjusting for established prognostic covariates. Recursive partitioning determined successive quantifiers and cutoffs associated with outcomes. Neurological deficits correlated with SI. ResultsSeventy-five percent of patients developed SI on average 3.4 months before RANO-assessed progression with 84% sensitivity. SI-positivity portended neurological decline and significantly poorer outcomes for PFS (median, 10 vs. 15 months) and OS (median, 20 vs. 29 months) compared to SI-negative. RC/V ratio [&ge;]4 was the most significant prognostic indicator of death. ConclusionsImplications of these data are far-reaching, potentially shifting paradigms for glioma treatment response assessment, altering timepoints for salvage therapeutic intervention, and reshaping glioma clinical trial design. KEYPOINTSO_LIIncreased confidence in defining true tumor progression is of critical importance. C_LIO_LIImaging markers preceding progression offer novel timepoints for salvage therapies. C_LIO_LIEarlier intervention might increase tumor therapy exposure and reshape clinical trial design. C_LI IMPORTANCE OF STUDYTherapeutic intervention at progression has failed to show benefit. Accurately defining progression impacts clinical decision-making, yet current response assessment criteria in glioblastoma remain unvalidated. The data presented identifies a highly sensitive brain tumor imaging biomarker, SI, which coincides with declining neurologic function and might supplement existing criteria to improve clinician confidence to declare GBM progression. Furthermore, as SI precedes current assessment guidelines by an average of 3.4 months, this finding might also offer an earlier window of opportunity for salvage therapeutic intervention and reshape glioma clinical trial design. This signal has been previously associated with glioma progression; however, prior studies were hampered by overly inclusive criteria and failed to make the innovative clinical and prognostic associations evidenced in our study. Prospective validation of the proposed imaging biomarker is currently underway as part of a centrally reviewed prospective interventional clinical trial for newly diagnosed GBM.

Glioblastoma (GBM) is the most aggressive primary brain tumor and can have cystic components, identifiable through magnetic resonance imaging (MRI). Previous studies suggest that cysts occur in 7-23% of GBMs and report mixed results regarding their prognostic impact. Using our retrospective cohort of 493 patients with first-diagnosis GBM, we carried out an exploratory analysis on this potential link between cystic GBM and survival. Using pretreatment MRIs, we manually identified 88 patients with GBM that had a significant cystic component at presentation and 405 patients that did not. Patients with cystic GBM had significantly longer overall survival and were significantly younger at presentation. Within patients who received the current standard of care (SOC) (N=184, 40 cystic), we did not observe a survival benefit of cystic GBM. Unexpectedly, we did not observe a significant survival benefit between this SOC cystic cohort and patients with cystic GBM diagnosed before the standard was established (N=40 with SOC, N=19 without SOC); this significant SOC benefit was clearly observed in patients with noncystic GBM (N=144 with SOC, N=111 without SOC). When stratified by sex, this significant survival benefit was only preserved in male patients (N=303, 47 cystic). We report differences in the absolute and relative sizes of imaging abnormalities on MRI and the prognostic implication of cysts based on sex. We discuss hypotheses for these differences, including the possibility that the presence of a cyst could indicate a less aggressive tumor.

BackgroundHigh-grade gliomas are fatal with universally poor prognosis. Initiation of effective cancer immune responses requires functional immune cells, particularly afferent antigen-presenting cells, which are typically absent from the brain parenchyma. To overcome this limitation, two adenoviral vectors expressing HSV1-TK and Flt3L were combined to target human gliomas. This first-in-human trial assessed safety, cytotoxicity, and recruitment of immune cells to the brain, in support of a future phase 1b/2 clinical trial. MethodsTreatment-naive high-grade glioma adult patients received injections of adenoviral vectors expressing HSV1-TK and Flt3L into the tumor bed, following maximal safe resection, at six escalating doses ranging from a total of 1.1x1010 to a maximum of 2x1011 viral particles. This was followed by two 14-day courses of Valacyclovir and standard upfront chemoradiation. Key inclusion criteria were age between 18 to 75, KPS[&ge;]70, and treatment-naive possible high-grade glioma amenable to gross total resection. Patients were consented pre-operatively, and definitive enrollment occurred intraoperatively upon pathology confirmation of malignant glioma. FindingsThe treatment was well-tolerated without dose-limiting toxicity in patients with high grade glioma (n=17) (including 3 of the Gliosarcoma variant), or Anaplastic Ependymoma (n=1). The maximal-tolerated dose was not reached. The median overall survival was 21.3 months (95%CI: 11.1, 26.1) compared to 14.6 months with standard-of-care, with seven patients surviving for >2 years, three patients surviving for >3 years, and one patient still alive 57 months after enrollment. Tissue from subsequent re-resections from eight subjects showed elevated markers for CD3+, CD8+ T cells, and plasmacytoid dendritic cells (pDCs), suggesting the potential stimulation of anti-glioma immunity. Additionally, we detected biological activity from both viral vectors: (i) an increase in serum levels of Flt3L two weeks after vector administration, and (ii) expression of HSV1-TK in neurons, astrocytes, and SOX2+ cells in brain tumor samples up to 17 months post-vector injection into the brain. InterpretationUse of two adenoviral vectors expressing HSV1-TK and Flt3L appears to be both safe and feasible. Promising evidence from multiplex immunocytochemical analyses shows the presence of the expected immune infiltration, i.e., pDCs, along with persistent vector expression lasting up to 17 months post-injection. Moreover, the two-year survival rate of 38.8% compared to 19.6% with standard-of-care is promising, suggesting that this approach warrants further investigation in a phase 1b/2 clinical trial. FundingFunded in part by Phase One Foundation, Los Angeles, CA, The Board of Governors at Cedars-Sinai Medical Center, and The Rogel Cancer Center at The University of Michigan; clinicaltrials.gov: NCT01811992.

PurposeArtificial intelligence (AI)-automated tumor delineation for pediatric gliomas would enable real-time volumetric evaluation to support diagnosis, treatment response assessment, and clinical decision-making. Auto-segmentation algorithms for pediatric tumors are rare, due to limited data availability, and algorithms have yet to demonstrate clinical translation. MethodsWe leveraged two datasets from a national brain tumor consortium (n=184) and a pediatric cancer center (n=100) to develop, externally validate, and clinically benchmark deep learning neural networks for pediatric low-grade glioma (pLGG) segmentation using a novel in-domain, stepwise transfer learning approach. The best model [via Dice similarity coefficient (DSC)] was externally validated and subject to randomized, blinded evaluation by three expert clinicians wherein clinicians assessed clinical acceptability of expert- and AI-generated segmentations via 10-point Likert scales and Turing tests. ResultsThe best AI model utilized in-domain, stepwise transfer learning (median DSC: 0.877 [IQR 0.715-0.914]) versus baseline model (median DSC 0.812 [IQR 0.559-0.888]; p<0.05). On external testing (n=60), the AI model yielded accuracy comparable to inter-expert agreement (median DSC: 0.834 [IQR 0.726-0.901] vs. 0.861 [IQR 0.795-0.905], p=0.13). On clinical benchmarking (n=100 scans, 300 segmentations from 3 experts), the experts rated the AI model higher on average compared to other experts (median Likert rating: 9 [IQR 7-9]) vs. 7 [IQR 7-9], p<0.05 for each). Additionally, the AI segmentations had significantly higher (p<0.05) overall acceptability compared to experts on average (80.2% vs. 65.4%). Experts correctly predicted the origins of AI segmentations in an average of 26.0% of cases. ConclusionsStepwise transfer learning enabled expert-level, automated pediatric brain tumor auto-segmentation and volumetric measurement with a high level of clinical acceptability. This approach may enable development and translation of AI imaging segmentation algorithms in limited data scenarios. SummaryAuthors proposed and utilized a novel stepwise transfer learning approach to develop and externally validate a deep learning auto-segmentation model for pediatric low-grade glioma whose performance and clinical acceptability were on par with pediatric neuroradiologists and radiation oncologists. Key PointsO_LIThere are limited imaging data available to train deep learning tumor segmentation for pediatric brain tumors, and adult-centric models generalize poorly in the pediatric setting. C_LIO_LIStepwise transfer learning demonstrated gains in deep learning segmentation performance (Dice score: 0.877 [IQR 0.715-0.914]) compared to other methodologies and yielded segmentation accuracy comparable to human experts on external validation. C_LIO_LIOn blinded clinical acceptability testing, the model received higher average Likert score rating and clinical acceptability compared to other experts (Transfer-Encoder model vs. average expert: 80.2% vs. 65.4%) C_LIO_LITuring tests showed uniformly low ability of experts ability to correctly identify the origins of Transfer-Encoder model segmentations as AI-generated versus human-generated (mean accuracy: 26%). C_LI

BACKGROUNDImaging-based monitoring of gliomas is limited by treatment-related changes. D-2-hydroxyglutarate (D-2-HG), produced by the isocitrate dehydrogenase (IDH) mutation, is detectable in cerebrospinal fluid (CSF) that can be accessed from various anatomic compartments. We evaluated CSF D-2-HG as a serially accessible biomarker for IDH-mutant gliomas. METHODSA CLIA-approved gas chromatography mass spectrometry assay was developed for CSF D- and L-2-HG. Lumbar and cranial CSF samples were collected from patients with IDH-mutant gliomas or IDH-wild-type brain tumors and non-tumor pathologies via surgical field collection, lumbar punctures, Ommaya reservoirs, and ventriculoperitoneal shunts. RESULTSCSF D-2-HG was significantly higher in cranial than lumbar samples from IDH-mutant glioma patients (median lumbar=0.20 M, cranial = 1.72 M; p<0.0001). Cranial, but not lumbar, CSF D-2-HG distinguished primary IDH-mutant gliomas from IDH-wild type lesions (cranial AUC= 0.89, 95% confidence interval (CI)= 0.80-0.97); lumbar AUC= 0.52, 95% CI=0.28-0.76). When evaluated in recurrent lesions as a separate validation cohort, this finding was also reproduced in this group (cranial AUC=0.97, 95% CI= 0.94-1.00; lumbar AUC=0.60, 95% CI=0.38-0.83). Cranial CSF D-2-HG levels decreased to 0.54x of baseline with resection in seventeen patients (p=0.0129) but did not decrease significantly with chemoradiation in five patients (p=0.6250). Longitudinal anatomical changes, such as cavity collapse, influenced serial sample interpretation. In grade 4 IDH-mutant astrocytomas, serial cranial CSF D-2-HG increased with disease progression and differentiated stability from pseudoprogression when tumor-CSF contact was sufficient. CONCLUSIONSSerial cranial CSF D-2-HG shows promise as a monitoring biomarker in patients with IDH-mutant gliomas when anatomic variables remain constant. KEY POINTSO_LICranial CSF D-2-HG levels exceed that of lumbar CSF in patients with IDH-mutant gliomas. C_LIO_LICranial CSF D-2-HG may discriminate disease stability vs. treatment effects, although post-resection anatomical changes can impact monitoring. C_LI IMPORTANCE OF THE STUDYImproved glioma monitoring is needed due to challenges distinguishing disease progression from treatment-related changes on imaging. Toward this goal, we evaluated CSF D-2-HG as a biomarker of IDH-mutant gliomas using a CLIA-approved assay. This study answers whether D-2-HG can identify IDH-mutant gliomas via either cranial or lumbar CSF. Importantly, in seventeen patients, we demonstrate that CSF D-2-HG is responsive to cytoreduction via resection, but not chemoradiation in five patients. This is also the first study to demonstrate that longitudinal anatomical changes can impact evaluation of CSF D-2-HG as a monitoring biomarker. Finally, the study demonstrates that serial CSF D-2-HG can increase with disease progression, but not pseudoprogression or stable disease, in five patients with grade 4 IDH-mutant astrocytomas. These findings support the potential of CSF D-2-HG as a monitoring biomarker in patients with IDH-mutant gliomas, particularly when there are minimal changes to the anatomy of the resection cavity.

PurposeTo externally evaluate three binary classification models designed to differentiate the molecular subtype of pediatric low-grade glioma (pLGG) between BRAF Fusion, BRAF Mutation, and Wild Type on T2-weighted magnetic resonance imaging using self-supervised transfer learning, which enables effective performance in a low data setting. Materials and methodsThis retrospective study evaluates pLGG molecular subtyping models, pre-trained using data collected at Dana Farber Cancer Institute/Bostons Childrens Hospital, on two datasets from the Hospital for Sick Children, one consisting of patients identified from the electronic health record between January 2000 to December 2018 (n=336) and another consisting of patients identified from the electronic health record between January 2019 to April 2023 (n=87). These datasets consist of T2-weighted MRI with pLGG and corresponding genetic marker identifications, labelled as BRAF Fusion, BRAF Mutation, or Wild Type. The datasets included manually annotated ground-truth segmentations that were used in the classification pipeline during evaluation. The models were evaluated using the area under the receiver operating characteristic curve (AUC). To acquire a per-class probabilities across all three considered molecular subtypes, we used the output probabilities from each binary model as logits input to a Softmax function. These probabilities were used to determine the AUC of the models on each evaluated dataset. ResultsThe models performed achieved a macro-average AUC of 0.7671 on the newer dataset from the Hospital for Sick Children but achieved a lower macro-average AUC of 0.6463 on the older dataset from the Hospital for Sick Children. ConclusionsThe evaluated pLGG molecular subtyping models have the potential for effective generalization but may require further fine-tuning for consistent performance across varying datasets.

Brain tumors are among the most lethal cancers with gliomas representing the most morphologically complex type. Precise and time efficient glioma segmentation and classification are essential for accurate diagnosis, treatment planning, and patient monitoring. Magnetic resonance imaging (MRI) remains the primary imaging modality for noninvasive glioma assessment. This review systematically analyzes deep learning (DL) and artificial intelligence (AI) approaches for brain tumor segmentation and classification. Thirty one studies, out of 310 published between 2022 and 2025, met the inclusion criteria, among which 8 performed both segmentation and classification tasks. For segmentation, most of the studies used publicly available multiparametric MRI datasets. Segmentation performance varied by model and tumor region, with those focused on the whole tumor region achieving the highest Dice Score Coefficient (DSC). Classical U Nets achieved DSC scores around 80%, while advanced models integrating residual or attention modules exceeded 90%. Two main classification tasks were performed: tumor type and glioma staging. Classification models primarily relied on learned features extracted from multiparametric MRI using DL models, reporting an accuracy from 91.3% to 99.4%, with sensitivity and specificity typically above 95%, indicating robust predictive performance. Surprisingly, explainable AI approaches were infrequently applied, highlighting the persistent need for greater model transparency to foster clinical trust. Overall, these results demonstrate the strong potential of current AI based segmentation and classification pipelines. These methods can help clinicians accelerate the decision making process, increasing both the accuracy and efficiency of brain tumor diagnosis. These approaches may also support the development of personalized treatment plans tailored to each patient.

BackgroundBrain tumors can present as focal neurologic deficits (reflecting the tumor location) or generalized symptoms due to increased intracranial pressure. Occasionally, brain tumors can be found incidentally in asymptomatic patients or in patients with unrelated symptoms who undergo brain imaging. The term incidentaloma is used to refer to these imaging abnormalities. ObjectiveThe object of this study was to examine the prevalence and correlates of asymptomatic glioma in a large epidemiological study of brain tumors. MethodsThe analysis was based on a large series of patients with glioma (N = 1989) enrolled in a multicenter clinic-based epidemiologic study between 2005 and 2017. Patients were considered asymptomatic from the tumor, and thus as having an incidentally detected glioma (IDG), if the tumor was diagnosed during workup of injury or unrelated medical condition. ResultsA total of 32 of 1989 (1.6%) patients were asymptomatic at diagnosis. The leading indication for brain imaging in IDG was non-workplace injuries followed by medical workup for unrelated conditions. IDG was more prevalent in patients younger than 50 years of age (2.6% vs 1.0%). IDG was also more common in patients with low grade gliomas (4.7% for WHO grade II and 1.5% for WHO grade III) vs glioblastomas (0.6% in WHO grade IV). ConclusionThe present data suggest that gliomas may be found incidentally, especially among low grade gliomas. Studies of IDG may be useful as a proxy for early detection of tumor as a means to improve patient survival.

IntroductionGlioblastoma is a devastating brain tumor with poor prognosis despite current treatment modalities. Chimeric antigen receptor T-cell (CAR T-cell) therapy has shown promise in other cancers but has yielded mixed results in glioblastoma. This augmented meta-analysis aims to address the limitations of previous studies and evaluate the safety and efficacy of CAR T-cell therapy for recurrent glioblastoma. MethodsWe followed PRISMA guidelines, including specific inclusion and exclusion criteria, for our literature review. Eight studies with 108 patients were included. We used standard and augmented meta-analyses to assess outcomes, complications, and publication bias. ResultsIt was found that the mean overall survival for glioblastoma patients who underwent CAR T-cell therapy was 6.49 months, demonstrating no significant deviation from the median survival observed in those following the standard protocol. CAR T-cell therapy did not lead to a statistically significant improvement in achieving complete responses, with only 80% of patients exhibiting this outcome. Conversely, 44% of patients experienced stable disease, while 58% faced disease progression after CAR T-cell therapy. Adverse events were notable, with CAR T cell therapy-related encephalopathy affecting 37% of treated patients, while cytokine release syndrome was a rare event, observed in only 3% of cases. ConclusionsTo our knowledge, this is the first study that utilizes this novel statistical technique to predict the outcomes of CAR T-cell therapy for recurrent glioblastoma. The results of this study are predictive rather than confirmatory. CAR T-cell therapy for glioblastoma was not predicted to significantly improve survival or achieve substantial complete responses. Stable disease rates are modest, while disease progression is notable. Adverse events, especially CAR T-cell therapy-related encephalopathy, raise safety concerns. Further trials and refinements are needed to enhance CAR T-cell therapys effectiveness and safety in glioblastoma treatment, Manuscript Click here to view linked References potentially through optimizing administration routes and target antigens or combining it with other therapies. This challenging disease necessitates continued research to improve patient outcomes.

BackgroundGlioblastoma (GBM) remains one of the most aggressive primary brain tumors, with limited survival despite maximal safe resection and chemoradiotherapy. Neoadjuvant bevacizumab (BEV) has been proposed to reduce peritumoral edema, improve functional status, and potentially enhance progression-free survival (PFS). However, its survival benefit in newly diagnosed, surgically resectable GBM remains unclear. ObjectiveTo systematically review and quantitatively synthesize the evidence on neoadjuvant BEV in adult patients with newly diagnosed, resectable GBM, focusing on survival and functional outcomes. MethodsFollowing PROSPERO registration (CRD420251078761), we searched PubMed, Embase, Scopus, Web of Science, and Cochrane Library up to July 20, 2025, without language restrictions. Eligible randomized trials, non-randomized trials, and cohort studies compared neoadjuvant BEV (alone or with other therapies) to standard care without BEV. Primary outcomes were overall survival (OS) and PFS; secondary outcomes included Karnofsky Performance Status (KPS), steroid use, radiological response, and biomarkers. Data were pooled using a random-effects model. ResultsThirteen studies (2 RCTs, 7 non-randomized trials, 4 cohorts) met the inclusion criteria; four (n=751) were eligible for meta-analysis. Pooled HR for OS was 0.72 (95% CI: 0.42-1.25, p=0.246) and for PFS was 0.72 (95% CI: 0.42-1.22, p=0.220), both with low heterogeneity (I2=0%). Functional outcomes suggested improved KPS and reduced steroid dependence, but certainty was low. Biomarker and radiological findings were inconsistent. ConclusionsNeoadjuvant BEV in resectable GBM does not significantly improve OS or PFS but may offer symptomatic and functional benefits. Current evidence is limited by small sample sizes, heterogeneous protocols, and low methodological quality. Well-designed multicenter RCTs are warranted.

BackgroundBRAF status is crucial for treating pediatric low-grade gliomas (pLGG) and can be assessed non-invasively from segmented tumor regions on MRI using machine learning (ML). However, there are inherent limitations to manual and automated tumor segmentations. PurposeTo assess the performance of automated segmentation algorithms and to develop and assess a segmentation-free ML classification pipeline that identifies BRAF status from whole-brain FLAIR MRI sequences. Materials and MethodsIn this REB-approved retrospective study, molecularly-characterized tumors and whole-brain FLAIR MR images were collected from 455 patients with pLGG treated between 1999 and 2023 at a single tertiary care childrens hospital. We trained and evaluated three medical segmentation models, TransBTS, MedNeXt, and MedicalNet. Next, we developed a model to identify BRAF status from whole-brain FLAIR MRI, without any reliance on manual or automated segmentations. We then implemented a novel pretraining regimen that embedded segmentation knowledge into the whole-brain FLAIR MRI classification model. Finally, we trained and evaluated a baseline model that used manual segmentations as inputs. All ML models were trained and evaluated under a nested-cross validation scheme, and mean performance across all test folds was compared using the corresponding t-test. ResultsThe MedNeXt segmentation model (mean Dice score: 0.555) outperformed both the convolutional neural network (CNN) based MedicalNet (0.516) and the CNN-transformer hybrid TransBTS (0.449) (p <0.05 for all comparisons). The MedNeXt style classification model achieved a one-vs-rest area under the ROC curve of 0.741 using the whole brain FLAIR sequence as an input, without any segmentation knowledge. This was improved to 0.772 through pretraining on the segmentation task, which was not significantly different from the baseline manual segmentation-based model (0.756, p-value: 0.141). ConclusionBRAF status can be assessed non-invasively using ML models based on whole-brain FLAIR sequences. Dependence on inconsistent manual or automated segmentations can be reduced by integrating tumor region information into the model through pretraining.

BackgroundThe importance of timely diagnosis of brain tumours is well recognised but how this translates to impacts on important patient outcomes is not well described. This study aimed to quantify the effect of time-to-treatment interval and tumour size at diagnosis on six patient outcomes: tumour-specific survival, overall survival, new or worsened neurological deficit, 30-day mortality, recurrence and inpatient length of stay in 12 months following diagnosis. MethodsA retrospective cohort study including 1196 patients from Southeast Scotland diagnosed with brain tumours between 2010 and 2020. Regression methods (Cox, Logistic & Linear) used to estimate per day impact of time-to treatment and tumour size impact on outcomes. ResultsThe mean time from first presentation to treatment was 161 days (median: 57 days) with wide variance observed (standard deviation: 330 days). Time-to-treatment results showed an association between longer intervals and reduced mortality. The mean tumour size was 4.06cm (SD: 1.75cm). Each 1cm increase in tumour size increased tumour-specific mortality risk by approximately 11% (HR: 1.11, 95%CI: 1.06-1.16), increased all-cause mortality by 6% (HR: 1.06, 1.02-1.11), increased the expected inpatient stay by 1.5 (0.2-2.8) days, and the odds of new or worsened neurological deficit by 11% (OR: 1.11, 1.01-1.21). ConclusionsLarger tumour size was consistently associated with increased hazard ratios for mortality. Applying these estimates together with estimates of mean tumour growth rates available in the literature, if a 6cm Glioblastoma tumour were diagnosed 1 month earlier then we would expect a 18-28% reduction in hazard of brain tumour mortality. FundingDxcover Ltd. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSBrain tumour is frequently diagnosed in an emergency setting, with multiple primary care visits often preceding a diagnosis, suggesting opportunities for earlier diagnosis. Previous studies have produced conflicting findings regarding the impact of diagnostic delays on survival and other patient outcomes. Some suggest that longer intervals are associated with worse prognosis, while others report a paradoxical association where delayed diagnosis appears to correlate with improved survival, the waiting time paradox. Prior research has largely focused on the prognostic value of tumour size rather than its role as a mediator of diagnostic delay, limiting causal inference regarding the benefits of earlier detection. Added value of this studyThis study addresses gaps in the literature by examining tumour size as a key mediating factor in the relationship between diagnostic timing and patient outcomes. Using a large, well-curated dataset of 1,196 patients from Southeast Scotland, we demonstrate that tumour size at diagnosis is strongly associated with mortality risk, neurological deficits, inpatient stay, and recurrence. A one-month earlier diagnosis of a 6cm glioblastoma, for example, could reduce tumour size sufficiently to lower the hazard of brain tumour mortality by 18- 28%. By disentangling tumour size effects from time-to-treatment intervals, our study provides a clearer framework for evaluating early detection strategies in neuro-oncology. Implications of all the available evidenceThese findings reinforce the potential impact of earlier diagnosis for brain tumour patients. Building upon the accumulated evidence that tumour size is a critical factor influencing prognosis, this study is the first to robustly estimate the causal association. Earlier detection, through influence on tumour size at diagnosis, may offer measurable survival benefits and potential reductions in healthcare resource use. Given the observational nature of this study, further prospective trials and mathematical modeling are needed to assess the impact of early diagnosis interventions at a population level.

BackgroundDespite innovative approaches, outcomes for pediatric high-grade gliomas (HGGs) remain poor. Doxorubicin (Dox) is commonly used to treat many childhood cancers, with a well-known safety profile, although the blood-brain barrier limits its use in central nervous system tumors. However, its antineoplastic activity is reported in vitro and in vivo glioma models. We aimed to assess safety and activity of the addition of Dox to the standard treatment in this population. MethodsA monocentric, non-randomized, phase II interventional study was opened at Meyer Childrens Hospital IRCCS in Florence (EudraCT 2015-002307-28), introducing Dox 100 mg/m2 over a 96-hour infusion following chemo-radiotherapy as a post-operative treatment, alongside valproic acid throughout the treatment. The endpoints were safety and efficacy of the add-on Dox approach in prolonged infusion. ResultsTwenty-one heterogeneous malignant pediatric HGGs patients were enrolled. However at the time of Dox administration, only twelve patients presented a performance status sufficient to receive the investigational drug. Dox single course-group (10 patients) exhibited a median overall survival (OS) of 13.7 months (6.9 months in non-Dox-treated patients). Analyzing a multivariate Cox regression, patients with diffuse midline glioma showed a significantly higher risk of events compared to those with other HGG (approximately 80%, p = 0.008). Dox-treated DMG shows a slight reduction in event rate (9.52 vs 12.55). Interestingly, all patients (6/12) with hemispheric malignant glioma, who had undergone Dox, relapsed at sites distant from the primary tumor. Currently, only one patient is alive (a Dox-treated grade 3 anaplastic pleomorphic xanthoastrocytoma), Considering the Dox-treated patients, despite 35 Serious Adverse Reactions related to Dox were reported, predominantly hematologic, the treatment after focal radiotherapy was well tolerated. No signs of cardiotoxicity, nephrotoxicity, or neurotoxicity following Dox infusion were reported. ConclusionThis preliminary study shows that a prolonged infusion Dox add-on to standard multimodal treatment for pediatric HGGs is well tolerated with no significant adverse events and with a positive impact in terms of survival, although not statistically significant.

BackgroundThis study identified a clinically significant subset of glioma patients with tumor outside of contrast-enhancement present at autopsy, and subsequently developed a method for detecting non-enhancing tumor using radio-pathomic mapping. We tested the hypothesis that autopsy-based radio-pathomic tumor probability maps would be able to non-invasively identify areas of infiltrative tumor beyond traditional imaging signatures. MethodsA total of 159 tissue samples from 65 subjects were aligned to MRI acquired nearest to death for this study. Demographic and survival characteristics for patients with and without tumor beyond the contrast-enhancing margin were computed. An ensemble algorithm was used to predict pixelwise tumor presence from pathological annotations using segmented cellularity (Cell), extracellular fluid (ECF), and cytoplasm (Cyt) density as input (6 train/3 test subjects). A second level of ensemble algorithms were used to predict voxel-wise Cell, ECF, and Cyt on the full dataset (43 train/22 test subjects) using 5-by-5 voxel tiles from T1, T1+C, FLAIR, and ADC as input. The models were then combined to generate non-invasive whole brain maps of tumor probability. ResultsTumor outside of contrast was identified in 41.5 percent of patients, who showed worse survival outcomes (HR=3.90, p<0.001). Tumor probability maps reliably tracked non-enhancing tumor in the test set, external data collected pre-surgery, and longitudinal data to identify treatment-related changes and anticipate recurrence. ConclusionsThis study developed a multi-1 stage model for mapping gliomas using autopsy tissue samples as ground truth, which was able to identify regions of tumor beyond traditional imaging signatures.

IMPORTANCECurrent methods for glioma treatment response assessment are limited. Intracranial cerebrospinal fluid (CSF) may provide a previously untapped source of longitudinal biomarkers, such as cell-free DNA (cfDNA), for disease monitoring. OBJECTIVETo assess the feasibility of obtaining longitudinal intracranial CSF cfDNA from patients with gliomas during their disease course. DESIGNThis case series was initiated in 2021, with patients followed until last clinical follow-up (death or present time). SETTINGThis single-center study was conducted at a large academic medical center. PARTICIPANTSAdults with gliomas were recruited for longitudinal intracranial CSF collection using 1) Ommaya reservoirs, from which CSF would be sampled on at least two separate occasions, or 2) CSF collection from other clinically indicated CSF access devices, such as ventriculoperitoneal (VP) shunts. INTERVENTIONSCSF was collected from Ommaya reservoirs in four patients and from an existing VP shunt in one patient. MAIN OUTCOMES AND MEASURESThe study aimed to collect CSF for biobanking and biomarker discovery, with the hypothesis that CSF could serve as a source of longitudinally acquirable biomarkers. RESULTSFive patients (2 females, 3 males; median: 40 years, range 32-64 years) underwent longitudinal intracranial CSF collection via Ommaya reservoirs (n=4/5 patients) or VP shunt (n=1/5). Three patients had glioblastoma and two had astrocytoma, IDH-mutant, grade 4. In total, thirty-five CSF samples were obtained (median: 3.80 mL; 0.5-5 mL), with 30 (85.7%) yielding sufficient cfDNA for Next-Generation Sequencing (n=28) or Low-Pass Whole Genome sequencing (all samples). Tumor fraction was found to increase with radiographic progression. Changes in variant allelic frequencies (VAFs) may be seen within individual patients after resection and chemoradiation. In two patients, changes in tumor-specific IDH1 VAF correlated with CSF D-2-hydroxyglutarate levels, the oncometabolite of IDH mutant tumors. Copy number burden (CNB) decreased below the limit of quantification during treatment. CONCLUSIONS AND RELEVANCELongitudinal CSF cfDNA can feasibly be obtained via CSF access devices in patients with gliomas during their disease course. Ongoing studies will evaluate hypotheses generated in this case series regarding how longitudinal CSF cfDNA could be utilized to sensitively detect changes in disease burden. Trial RegistrationNCT04692324 https://clinicaltrials.gov/study/NCT04692324; NCT04692337 https://clinicaltrials.gov/study/NCT04692337 QUESTIONWhat is the feasibility of obtaining longitudinal intracranial cerebrospinal fluid cell-free DNA from patients with high-grade gliomas to evaluate changes during treatment? FINDINGIn this case series, we find that CSF cfDNA can feasibly be obtained throughout treatment via CSF access devices. We find that changes in tumor fraction or tumor-associated variant allele frequencies (VAFs) may correlate with disease trajectory, with VAFs positively correlating to other tumor-associated candidate biomarkers. MEANINGLongitudinal cerebrospinal cell-free DNA may inform the impact of treatment throughout a specific patients disease course, from the time of resection through radiographic progression.

BackgroundIn glioblastoma (GBM), promoter methylation of the O6-methylguanine-DNA methyltransferase (MGMT) is associated with beneficial chemotherapy but has not been accurately evaluated based on radiological and pathological sections. To develop and validate an MRI and pathology image-based deep learning radiopathomics model for predicting MGMT promoter methylation in patients with GBM. MethodsA retrospective collection of pathologically confirmed isocitrate dehydrogenase (IDH) wild-type GBM patients (n=207) from three centers was performed, all of whom underwent MRI scanning within 2 weeks prior to surgery. The pre-trained ResNet50 was used as the feature extractor. Features of 1024 dimensions were extracted from MRI and pathological images, respectively, and the features were screened for modeling. Then feature fusion was performed by calculating the normalized multimode MRI fusion features and pathological features, and prediction models of MGMT based on deep learning radiomics, pathomics, and radiopathomics (DLRM, DLPM, DLRPM) were constructed and applied to internal and external validation cohorts. ResultsIn the training, internal and external validation cohorts, the DLRPM further improved the predictive performance, with a significantly better predictive performance than the DLRM and DLPM, with AUCs of 0.920 (95% CI 0.870-0.968), 0.854 (95% CI 0.702-1), and 0.840 (95% CI 0.625-1). ConclusionWe developed and validated cross-scale radiology and pathology models for predicting MGMT methylation status, with DLRPM predicting the best performance, and this cross-scale approach paves the way for further research and clinical applications in the future.