Neuro-Oncology Advances

Gliomas are brain tumors characterized by highly variable growth patterns. Magnetic resonance imaging (MRI) is the cornerstone of glioma diagnosis and management planning. However, glioma features on MRI do not directly correlate with tumor cell distribution. Additionally, there is evidence that glioma tumor characteristics and prognosis are sex-dependent. Magnetic resonance elastography (MRE) is an imaging technique that allows interrogation of tissue stiffness in-vivo and has found utility in the imaging of several cancers. We investigate the relationship between MRI features, MRE features, and growth parameters derived from an established mathematical model of glioma proliferation and invasion. Results suggest that both the relationship between tumor volume and tumor stiffness as well as the relationship between the parameters derived from the mathematical model and tumor stiffness are sex-dependent. These findings lend evidence to a growing body of knowledge about the clinical importance of sex in the context of cancer diagnosis, prognosis and treatment.

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.

Background and Objectives: Are some brain regions intrinsically more vulnerable to metastatic colonization? We sought to characterize the spatial distribution of brain metastases and determine whether regional patterns vary according to primary tumor origin. Methods: We analyzed baseline MRI scans and expert tumor segmentations from 906 patients with 3,492 brain metastases treated with stereotactic radiosurgery. Lesions were normalized to MNI152 standard space and superimposed to generate probabilistic atlases of metastatic occurrence. Regional metastatic burden was quantified using anatomical and vascular atlases. Spatial distributions were additionally compared between lung cancer and melanoma metastases. Results: Metastatic burden was distributed nonuniformly throughout the brain. The cerebellum demonstrated the strongest enrichment relative to its anatomical volume (fold change 1.61, p < 0.001), accompanied by overrepresentation of the vertebrobasilar circulation (fold change 1.49, p < 0.001). Spatial distribution also varied by primary tumor type. Lung cancer metastases demonstrated greater infratentorial involvement than melanoma metastases (16.6% vs. 8.7%, p < 0.05), with a corresponding increase in cerebellar burden (14.8% vs. 6.8%, p < 0.05), whereas melanoma metastases were relatively concentrated within the frontal lobe (37.7% vs. 24.6%, p < 0.01). Infratentorial enrichment was observed across all carcinoma subgroups, with the greatest enrichment seen in gastrointestinal metastases (32.9% infratentorial). Conclusion: Brain metastases exhibit nonrandom spatial distributions, with preferential involvement of posterior and infratentorial structures. Regional patterns vary according to primary tumor origin, supporting the existence of region-specific vulnerability to metastatic disease.

BackgroundDiffuse low-grade gliomas (LGGs) are primary brain tumors with infiltrative, anisotropic growth related to surrounding white and grey matter structures. In this study, we illustrate the use of deformation-based morphometry (DBM) as a simple and objective method to study the local change in growth patterns of LGGs. MethodsAn imaging pipeline was developed involving the creation of patient-specific average templates and nonlinear registration of pre-treatment follow-up MRIs to the average template. Jacobian maps were derived and analyzed to identify areas of tissue expansion and contraction over time. ResultsOur analysis demonstrates that tissue expansion occurs primarily around the edges of the tumor, while the lesion core and areas adjacent to obstacles, such as the skull, show no significant growth. Tumors also appeared to grow faster and predominantly in areas of white matter. Regions of the brain surrounding the lesion showed slight contraction over time, likely representing compression due to mass effect of the tumor. ConclusionsWe demonstrate that DBM is a useful clinical tool to understand the long-term clinical course of an individuals tumor and identify areas of rapid growth, which can explain the clinical signs and symptoms, predict future symptoms, and guide targeted diagnostics and therapy. HighlightsO_LILow-grade glioma expansion occurs primarily around the edges of the tumor. C_LIO_LITumor cores and tissue next to obstacles show no significant growth over time. C_LIO_LIDBM provides a clinically valuable assessment of local tumor growth and activity. C_LI

Gliosarcoma is a rare subtype of IDH-wildtype glioblastoma defined by mixed malignant glial and high-grade sarcomatous histological elements. Gliosarcoma is clinically managed similarly to glioblastoma and has a poor clinical outcome. The sarcoma-like regions of gliosarcoma are thought to represent extreme mesenchymal metaplasia of neoplastic glial cells. Factors contributing to this phenomenon are not completely understood. Here we report a single-institution series of 37 gliosarcomas including next-generation sequencing data on 25 cases and digital spatial whole-transcriptome analysis on 4 cases to characterize differential gene expression between glial and mesenchymal components. Gliosarcoma demographic and genetic features were compared to a cohort of 75 primary adult hemispheric IDH-wildtype non-sarcomatous glioblastomas. Patient age, tumor location, sex, and overall survival in gliosarcoma were similar to glioblastoma. Gliosarcomas showed a significantly lower rate of EGFR amplification and a higher rate of NF1 mutation compared to glioblastomas in next-generation sequencing analysis. Digital spatial whole-transcriptome analysis showed a distinct transcriptomic profile in sarcomatous regions with over-expression of genes involved in extracellular matrix development and remodeling. Selected differentially expressed transcripts were examined further by immunohistochemistry. The glial elements of gliosarcomas showed higher immunoreactivity for Chitinase-3-like protein 1 (CHI3L1) than glioblastomas, but low to absent expression within the sarcomatous elements. Lymphoid Enhancer-Binding Factor 1 (LEF1) immunoreactivity was identified within sarcomatous regions of gliosarcoma without detectable nuclear {beta}-catenin, suggesting a role for {beta}-catenin independent wingless (WNT) effector signaling in sarcomatous transformation. This study adds to the growing literature demonstrating differences in the genetic underpinning of gliosarcoma and glioblastoma, establishes feasibility of spatial transcriptomic approaches in gliosarcoma, and validates digital spatial profiling-based results as a discovery platform to identify pathways and immunohistochemical markers for further study.

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.

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

Glioblastoma (GBM) remains one of the most aggressive adult cancers, with limited treatment options due to an incomplete understanding of its biology. GBM exhibits different transcriptional subtypes, among which the mesenchymal (mGBM) variant is associated with a particularly aggressive nature and poor outcomes. Developing models that capture mGBM phenotypes could offer new insights into its biology and therapeutic vulnerabilities. However, the lack of in vitro models for mGBM has posed challenges in investigating the aggressive GBM subtype. Here, we present a gel-top culture model in which GBM cells exhibit MES features, including distinct transcriptomic profiles, invasive phenotypes, cell morphology, and cytoskeletal organization. Furthermore, co-culturing GBM cells with microglia in this system revealed enhanced microglial recruitment and interaction, effectively recapitulating the tumor microenvironment of mGBM that traditional 2D culture fails to model. Using the bioinformatic tool, Small Molecule Suite, we identified casein kinase 2 (CK2) as a targetable molecule in GBM cells within our system, potentially driving MES features. Notably, CK2 inhibitors reduced the mesenchymal signature of mGBM cells, suggesting their potential as a targeted therapy for mGBM. Together, our findings suggest that the gel-top culture model replicates key features of mGBM, serving as a valuable platform for studying its biology and identifying novel therapeutic strategies.

BackgroundDiagnosing diffuse midline glioma (DMG) through invasive tissue biopsies is challenging due to the tumors eloquent location at the pons. Liquid biopsies offer a promising noninvasive alternative; however, their limited detection sensitivity and lack of information on the source of biomarkers pose great challenges. This study aimed to evaluate the feasibility and safety of using focused ultrasound (FUS) with microbubbles to enrich circulating DMG tumor biomarkers in blood and cerebrospinal fluid (CSF) using a mouse model. MethodsMurine DMG cells, transfected with enhanced green fluorescent protein (EGFP) and firefly luciferase (Fluc) genes, were orthotopically injected into the mouse brain. Magnetic resonance imaging was used to guide FUS targeting of the DMG tumor. Droplet digital PCR assays were developed to detect circulating tumor DNA (ctDNA) and RNA (ctRNA) of EGFP and Fluc in blood and CSF samples collected after FUS. ResultsFUS enhanced the plasma levels of EGFP ctRNA by 5.4-fold (p=0.0112), compared with liquid biopsy without FUS. CSF EGFP ctDNA was increased by 2.5-fold (p=0.0253), and Fluc ctDNA was increased by 2.6-fold (p=0.0253) with FUS. No brain tissue damage was associated with FUS sonication. ConclusionsThis study demonstrated the feasibility and safety of FUS in enriching tumor biomarkers in blood and CSF in a mouse model of DMG. The enrichment ratio for circulating biomarkers depends on the source (plasma vs. CSF), analyte type (ctDNA vs. ctRNA), and individual marker (EGFP vs. Fluc). These findings support the potential future application of FUS to advance the diagnosis of DMG through liquid biopsy. Key pointsO_LIddPCR assays were developed to detect plasma and CSF ctDNA and ctRNA in a mouse model of DMG C_LIO_LIFUS with microbubbles enriched ctDNA and ctRNA in the blood and CSF of a mouse model of DMG C_LIO_LIThe enrichment ratio on circulating biomarkers is dependent on the source (plasma vs. CSF), analyte type (ctDNA vs. ctRNA), and individual marker (EGFP vs. Fluc) C_LI Importance of the studyLiquid biopsy via detecting circulating tumor biomarkers holds enormous clinical value in the diagnosis of diffuse midline glioma (DMG), where the eloquent location of the tumor poses significant risks to invasive surgical tissue biopsies. However, the effectiveness of liquid biopsy is hindered by its low sensitivity and the lack of information about the source of biomarkers. This study demonstrated that noninvasive and spatially targeted FUS treatment can release tumor-derived DNA and RNA into the blood and CSF in a mouse model of DMG. This study also found that the enrichment effect of FUS on circulating biomarkers depends on the source (plasma vs. CSF), analyte type (ctDNA vs. ctRNA), and individual marker (EGFP vs. Fluc). This study opens new avenues for advancing noninvasive DMG diagnosis through FUS-enhanced liquid biopsy (sonobiopsy).

Glioblastoma studies employ syngeneic orthotopic models to preserve tumor-immune interactions, but intracranial tumor burden is challenging to monitor longitudinally. Bioluminescence imaging enables non-invasive assessment, although reporter immunogenicity may compromise model fidelity. We engineered murine GL261 glioma cells to stably express nanoluciferase (NLuc) and compared them with parental GL261 (WT) and GL261 cells expressing red-shifted firefly luciferase (Red-FLuc). In vitro, GL261-NLuc retained growth kinetics and morphology comparable to GL261-WT and produced >100-fold stronger bioluminescence than GL261-Red-FLuc. In immunocompetent mice, GL261-NLuc formed lethal brain tumors with survival and tumor histopathology, immune profile, and response patterns to experimental oncolytic virus therapy broadly resembling GL261-WT. In contrast, GL261-Red-FLuc tumors regressed and exhibited heightened inflammation and increased infiltration of activated CD8+ T-cells. Longitudinal imaging of GL261-NLuc tumors detected treatment-associated changes in growth kinetics not captured by survival alone. These establish GL261-NLuc as a practical reporter for longitudinal immunocompetent glioblastoma studies amenable to immunotherapy evaluations.

Previous characterization of the genome and transcriptome of glioblastoma (GBM) has revealed molecular alterations that potentially drive GBM pathogenesis and heterogeneity 1-6. These open-resources are evolving, such as The Cancer Genome Atlas (TCGA) and The Cancer Imaging Atlas (TCIA) at the National Institute of Health comprising a large cohort of molecular and MRI data. Yet, no report deciphers the link between molecular signatures and MRI-classified GBM. The necessity to re-form molecular and imaging data motivated our computational approach to integrate TCIA and TCGA datasets derived from GBM. We uncovered common and distinct molecular signatures across GBM patients and specific to tumor locations, respectively. Despite heterogeneity in GBM, the top 12 genes from our analysis highlights that the dysregulation of a subset of neurotransmitter receptor or transporter and synaptic activity is common across GBM patients. The coherent layer of imaging and molecular information would help us stratify precision neuro-oncology and treatment options in ways that are not possible through MRI or genomic data alone. Our findings provide molecular targets in the disrupted neurocircuit of GBM, suggesting imbalanced excitation and inhibition. Given the fact that GBM patients exhibit similar symptoms resembling patients with neurodegenerative diseases and seizures, our results supported the hypothesis-GBM in the context of neurological disorders beyond a solely cancerous disease.

BackgroundThe presence of structural and geometric variations within gliomas, even among those with similar histologic grades, reflects the phenotypic heterogeneity unique to a genetic and epigenetic landscape. Whole glioma mass comprises of various subcomponents identified on MR imaging: enhancing, nonenhancing, necrosis, and edema fractions in varied fractions across patients. The geometry of whole tumor mass and the glioma subcomponents is highly irregular. Thereby, traditional Euclidean geometry is not suitable for quantifying the geometric dimensions. Here, we employ non-Euclidean geometric measurements: Fractal Dimension and lacunarity of the glioma subcomponents as a discriminator of IDH and MGMT status of gliomas. MethodsFractality and Lacunarity measurements were obtained using the tumor masks generated for enhancing, nonenhancing, and edema subcomponents from the preoperative T1, T1c, and T2-Flair MRI. Fractality and lacunarity measures of each subcomponent were evaluated between IDH mutant and wildtype gliomas. The fractality and lacunarity measures in IDH mutant and wildtype gliomas were further stratified for MGMT methylated and unmethylated gliomas. The fractality and lacunarities were trained and tested using supervised ML modeling as discriminators of IDH and MGMT status. Further, Cox Hazard estimations and the Kaplan-Meir investigations were performed to evaluate the impact of fractality and lacunarity measures of glioma subcomponents on the overall survival of the patients. ResultsIDH wildtype gliomas had [~]2-fold higher fractality for the enhancing subcomponent compared to IDH mutant enhancing subcomponent, while IDH mutant gliomas showed higher fractality for the nonenhancing subcomponent. Furthermore, the edema subcomponent did not differ for fractality or lacunarity measures between IDH mutant and wildtype gliomas. Fractal or lacunarity measures for either of the three subcomponents do not vary across MGMT methylated and unmethylated status with a given IDH mutant or wildtype gliomas. A combination of fractal measures of the enhancing and nonenhancing subcomponents together provided highly accurate and sensitive discrimination of IDH status using the supervised ML models. Moreover, fractality measure [&ge;] 0.69 for the enhancing subcomponent was associated with shortened patient survival: a fractal dimension value corresponding to that of IDH wild type gliomas. However, fractality and lacunarity estimates were not sensitive for discrimination of MGMT status. ConclusionGlioma structural heterogeneity measured as fractality and lacunarity using routine structural MRI measurements provide a noninvasive quantitative platform definitive of the molecular subtype of gliomas: IDH mutant vs. wildtype. Establishing fractality and/or lacunarity quantities as signatures of prognostic molecular events provides an avenue to bypass the need of biopsy/surgical interventions for decision-making, determining the molecular subtypes and overall clinical management of gliomas. Importance of the StudyThe non-Euclidean geometric measurements such as fractal dimension and lacunarity of enhancing, nonenhancing, and edema subcomponents are potentially unique quantitative metrics, discriminative of IDH status and patient survival. Fractality and Lacunarity estimates using the conventional structural MRI (T1w, T1C, T2, and T2F) provide an easy-to-use quantitative radiogenomics platform for improved clinical decisions, bypassing the need for immediate surgical interventions to ascertain prognostic molecular markers in gliomas, which is likely to improve overall clinical management and outcomes. Key PointsO_LIIncreased fractal dimensions of the enhancing subcomponents in IDH wildtype tumors, suggestive of highly irregular geometry, may potentially serve as a quantitative noninvasive determinant of IDH wildtype tumors. C_LIO_LIA combined fractal estimation of enhancing and nonenhancing subcomponents is the optimal and accurate discriminator of IDH mutant vs. wildtype. C_LIO_LIHigh fractal dimension of enhancing subcomponent and reduced fractality of nonenhancing subcomponent is predictive of shortened patient survival. C_LI

BackgroundTumor vasculature is a key driver of glioma progression, yet routine quantification depends on subjective histopathologic assessment or resource-intensive ancillary immunohistochemistry. A scalable, objective method for vascular phenotyping from routine histology remains an unmet need. MethodsWe leveraged 10x Genomics Xenium spatial transcriptomics data from a glioblastoma specimen to generate molecularly resolved annotations of GBM-associated endothelial cells and pericytes across 809,041 cells. These annotations were transferred to matched H&E-stained sections to train a DINO-DETR-based object detection model using a binary classification scheme (vascular vs. other). The model was validated on four independent Xenium patient slides and applied to a retrospective cohort of 119 diffuse gliomas spanning WHO grades 2-4 (oligodendroglioma, astrocytoma, and glioblastoma) with linked survival data. ResultsBinary vascular cell detection achieved a precision of 0.78, a recall of 0.63, and an F1 score of 0.70, with an overall accuracy of 98.6%. Orthogonal spatial validation confirmed that predicted vascular cells were preferentially localized within annotated blood vessel regions. In subtype-stratified survival analysis, high AI-derived vascular cell proportion was significantly associated with worse overall survival in astrocytoma patients (log-rank p < 0.019). ConclusionCross-modal AI training using spatial transcriptomics enables scalable, molecularly informed vascular quantification directly from routine H&E slides. Within the astrocytoma subtype, where tumor grade is most heterogeneous and vascular phenotype most variable, objective vascular quantification provides independent prognostic information demonstrating the potential of spatially supervised deep learning to extract clinically meaningful microenvironmental signals from universally available histologic material.

Glioblastoma progression is spatially heterogeneous, but conventional imaging provides limited information about where subsequent tumor progression is likely to occur. We developed a directional magnetic resonance elastography framework to test whether local post-treatment tumor-brain interface mechanics are associated with later spatial tumor progression. In a secondary analysis of a prospectively acquired glioblastoma cohort, wedge-level viscoelastic instability features were extracted from the first post-treatment MRE scan and related to novel tumor burden on the second post-treatment scan after excluding tumor already present on pretreatment or first post-treatment imaging. Nine patients had longitudinal imaging suitable for spatial comparison; six lesions showed net interval growth and were included in the primary wedge-level directional analysis, while three non-growing lesions were retained for descriptive comparison. In growing lesions, several directional mechanical features were descriptively associated with later novel tumor burden. In cluster-aware models accounting for within-patient dependence among wedges, mean {Delta}tan{delta} ; showed the most consistent association with later wedge-level novel tumor fraction across mixed-effects and generalized estimating equation analyses. Associations were directionally stable across wedge-width sensitivity analyses. These findings provide proof of principle that post-treatment glioblastoma interface mechanics contain spatially resolved information related to where later tumor emergence occurs, supporting further validation of directional MRE as a framework for longitudinal mapping of progression geometry.

Purpose: The prognostic value of diffusion- and perfusion-derived tumor-mask heterogeneity for overall survival in post-treatment glioblastoma was evaluated using a public MRI dataset. Materials and Methods: The University of California San Diego Post-Treatment Glioblastoma (UCSD-PTGBM) dataset was used to construct a first-timepoint cohort of 133 subjects. Twenty tumor-mask features were extracted from high b-value apparent diffusion coefficient (ADC) and dynamic susceptibility contrast (DSC) perfusion maps. Prognostic associations were assessed using univariate and adjusted Cox regression. A benchmark compared clinical, diffusion, perfusion, and combined models using cross-validated concordance indices and permutation testing. Results: ADC standard deviation (ADCstd) showed the strongest univariate prognostic association (hazard ratio 1.56, false discovery rate q = 0.0003, concordance index 0.621) and remained independently significant after clinical adjustment (HR 1.48, p < 0.001). Mean transit time standard deviation (MTTstd) was the strongest perfusion-derived feature (HR 1.38, q = 0.025, concordance index 0.578). ADCstd and MTTstd showed low correlation (Spearman r = 0.24). In cross-validation, neither imaging feature alone significantly improved discrimination over the clinical baseline (clinical plus ADC, {Delta}C = +0.058, p = 0.071; clinical plus MTT, {Delta}C = +0.035, p = 0.194). Only the model combining clinical variables, ADCstd and MTTstd achieved a significant improvement (concordance index 0.619; {Delta}C = +0.072, p = 0.029). Conclusion: ADC heterogeneity was the numerically strongest imaging signal, while DSC perfusion heterogeneity was weaker and less consistent. Only the combined model significantly outperformed the clinical baseline, but not ADC alone, leaving perfusion's contribution unproven.

BackgroundThe early assessment of successful treatment response in high-grade glioma is challenging using conventional MRI, due to phenomena such as pseudoprogression which can mimic tumor progression. New methods are needed to evaluate therapeutic efficacy rapidly and accurately. Deuterium Metabolic Imaging (DMI) is a novel, non-invasive technique that can map downstream glucose metabolism in vivo. This study aimed to evaluate the utility of DMI for assessing metabolic response to chemoradiotherapy (ChRT) in glioma patients. MethodsIn this prospective two-site study, 18 patients with high-grade glioma underwent 3 T DMI before and after ChRT. Following oral administration of [6,6-2H2]glucose, 3D metabolic maps of 2H-glucose (Gluc), 2H-lactate (Lac), and 2H-glutamate+glutamine (Glx) were acquired. Ratios of Lac/Gluc and Glx/Gluc were calculated in the tumor and normal-appearing brain parenchyma to assess glycolytic and oxidative metabolism, respectively. ResultsBefore treatment, the tumor Glx/Gluc ratio was significantly lower than in the contralateral brain tissue. Following ChRT, a significant decrease in the normalized Lac/Gluc ratio was observed within the tumor volume, indicating a reduction in glycolysis. An important finding was that a lower pre-treatment normalized Glx/Gluc ratio in the tumor was a significant predictor of shorter progression-free survival (median 98 vs. 351 days, p=0.034). ConclusionsDMI identified metabolic changes in high-grade gliomas responding to ChRT. Reduced oxidative metabolism pre-treatment was associated with a poorer prognosis, while post-treatment decreases in glycolysis were demonstrated following therapy. DMI is a promising tool for non-invasively stratifying patients and monitoring treatment efficacy.

BackgroundMedulloblastoma is the most common pediatric malignant brain tumor in the population, with molecular subtypes that differ in prognosis and therapeutic response. Identifying these subtypes before surgery is important for tailoring management. ObjectiveThis study aimed to distinguish medulloblastoma molecular subtypes using a combination of conventional MRI features and MRI-based texture analysis. Materials and methodsWe retrospectively analyzed 58 patients with preoperative MRI and histopathologic confirmation of medulloblastoma. Cases were classified into SHH pathway-activated or Group 3/4 subtypes. Morphologic MRI characteristics, apparent diffusion coefficient (ADC) ratios, and texture analysis parameters were compared between the groups. ResultsOf the 58 patients, 55.2% had SHH pathway-activated tumors. Morphological features, including location out of the midline or in the cerebellar hemisphere (p<0.001), peri-tumoral edema (p=0.041), macrocysts (p=0.001), nodular involvement/lobulation (p=0.002), and heterogeneous contrast enhancement (p=0.002) were more common in SHH tumors. ADC measurements showed that the solid tumor-to-thalamus ratio was significantly lower in SHH tumors (p<0.001), with a threshold of 0.855 providing 82.1% sensitivity and 92.3% specificity. As for texture analysis parameters, kurtosis (p=0.023), SumOfSqs (p=0.022) and 01-10-50-90% percentile (p=0.011; p=0.001; p=0.006; and p=0.013 respectively) values obtained from ADC images and kurtosis (p=0.041), SumOfSqs (p=0.005), SumVarnc (p=0.014), SumEntrp (p=0.032) values obtained from T1W images were statistically significant in differentiating SHH and group 3/ group 4 medulloblastoma. ConclusionIntegrating MRI morphological features, ADC-based measurements, and texture analysis provides complementary information for non-invasive differentiation of medulloblastoma molecular subtypes.

For pediatric patients with central nervous system (CNS) tumors, primary treatment often involves neurosurgical tumor resection. Accurate diagnosis is crucial to perform the best-suited extent of resection. Since two years, Sturgeon, an AI-based validated intraoperative nanopore sequencing tool, has been fully integrated with frozen section analyses as standard of care in our nationwide centralized pediatric oncology hospital. This care evaluation is the first to demonstrate the impact of Sturgeon on neurosurgical decision-making. Sturgeon delivered correct diagnoses in 82 out of 94 consecutive patients (87.2%, <90 minutes), no diagnosis in 11.7% and 1 incorrect diagnosis. The diagnosis obtained by Sturgeon supported the intended surgical strategy (85.7%) or changed the strategy (14.3%) toward a more aggressive or limited resection. This resulted in only 3.2% second-look surgeries. In conclusion, intraoperative use of Sturgeon provides essential guidance toward the most optimal neurosurgical strategy and thereby has great potential to contribute to better clinical outcome.

BackgroundConventional MRI cannot reliably distinguish radiation necrosis (RN) from recurrent metastasis after cranial radiotherapy, as both can show similar enhancement despite different biology. We tested whether these entities are mechanically non-equivalent in vivo and separable by MRE-derived viscoelastic metrics and perilesional interface-instability features. MethodsIn a prospective, histopathology-anchored cohort, 11 post-radiotherapy enhancing lesions were classified as RN (n=3) or recurrent/progressive tumor (n=8). MRE was acquired at 3.0 T with single-frequency 60-Hz excitation to derive storage modulus (G'), loss modulus (G''), and complex shear modulus magnitude (|G*|). Co-primary endpoints were median tumor G' and |G*|, each tested one-sided (RN > tumor) with Holm correction across the two co-primary tests. Median tumor G'' was tested two-sided. A prespecified secondary 6-endpoint family (absolute and tumor/NAWM-normalized G', G'', and |G*|) was analyzed with Benjamini-Hochberg FDR control. Exploratory instability mapping in a 0-6 mm peritumoral shell generated interface-topology metrics, including convexity. ResultsAbsolute tumor-core medians were higher in RN than tumor for |G*| (1.79 vs 1.32 kPa; Cliffs {delta}=0.67; q=0.10), G' (1.62 vs 1.09 kPa; {delta}=0.50; q=0.14), and G'' (0.81 vs 0.46 kPa; {delta}=0.75; q=0.10). NAWM normalization improved separation: tumor/NAWM |G*| (2.26 vs 1.41; {delta}=0.92; q=0.04) and tumor/NAWM G'' (2.67 vs 0.87; {delta}=1.00; q=0.04) were FDR-significant. Convexity also differentiated RN from tumor (0.49 vs 0.36; {delta}=1.00; MWU p=0.01). ConclusionsTumor/NAWM G'', tumor/NAWM |G*|, convexity, and tumor G'' emerged as the strongest candidate features, indicating that RN is mechanically harder and more dissipative than recurrent metastasis. Signal strength was high (Cliffs {delta} up to 1.00) but should be interpreted cautiously given sample size. Exploratory analyses further suggest that instability mapping captures biologically relevant interface behavior. These findings support a mechanics-based RN-versus-recurrence framework and justify prespecified, preregistered external validation.

The blood-brain barrier (BBB) is a highly specialized system that is critical for regulating transport between the blood and the central nervous system. In brain tumors, the vasculature system is compromised, and is referred to as the blood-tumor barrier (BTB). The ability to precisely model the unique physiological properties of the BTB is essential to decipher its role in tumor pathophysiology and for the rational design of efficacious therapeutics. Here, we introduce a robust and high-throughput in vitro 3D human BTB organoid model that recapitulates various key features of the BTB observed in vivo and in clinical GBM samples. The organoids are composed of patient-derived glioblastoma stem cells (GSCs), human brain endothelial cells (EC), astrocytes and pericytes, which are formed through self-assembly. Transcriptomic and functional analyses reveal that the GSCs in the BTB organoids exhibit enhanced level of stemness, mesenchymal signature, invasiveness and angiogenesis, and this is further confirmed in in vivo studies. We demonstrate the ability of the BTB organoids to model therapeutic delivery and drug efficacy on brain tumor cells. Collectively, our findings show that the BTB organoid model has broad utility as a clinically representative system for studying the BTB and evaluating brain tumor therapies.