Citations

Abstract High-grade gliomas (HGG) have significant spatial heterogeneity, complicating therapeutic approaches. While multiparametric MRI (mpMRI) enables noninvasive assessment of this heterogeneity, most predictive models lack biological interpretability when used to classify or predict outcomes. Here, we developed an unsupervised framework that identifies novel mpMRI feature patterns through dimensional reduction, linking them to clinically relevant molecular signatures. We analyzed 18 advanced mpMRI features from 73 stereotactic biopsies across 20 HGG patients. Imaging data were normalized, followed by dimensional reduction using an iterative UMAP across varying parameters. Density-based clustering was used to generate stable image-derived clusters through consensus co-clustering. RNA-seq data from matched biopsies were analyzed using DESeq2 and GSEA to identify differentially expressed genes and enriched pathways. We then constructed a network linking specific imaging features to these transcriptional signatures. Validation was performed in an independent cohort, allowing us to extend clusters across full brain mpMRI. Unsupervised clustering of mpMRI features produced nine stable image-based clusters. These clusters were not fully explained by contrast enhancement, metabolic class, or tumor purity, shown by intraclass correlation (ICC) analyses, suggesting they capture novel biological variation. Transcriptomic analysis showed each cluster was associated with distinct gene expression patterns and enriched biological pathways, including neuronal differentiation, immune chemotaxis, and chromatin remodeling. Key imaging features (sRCBV, MTT, K2)—combined in cluster-specific patterns—enabled construction of an interpretable imaging–transcriptomic network. This was validated in an independent cohort. We then applied to full brain mpMRI, mapping spatially distinct regions to biological trends and patient outcomes. Addressing a research gap in imaging HGG, we present an interpretable network that connects specific patterns of mpMRI to clinically meaningful molecular signatures. This approach advances our understanding of tumor heterogeneity and supports future translational work in precision patient care.

Abstract INTRODUCTION Glioblastoma is the most common malignant adult brain tumor with poor prognosis, largely due to its heterogeneous landscape. The AI-RANO and RANO-RGP groups organized the BraTS-Pathology 2024 challenge, providing a publicly-available dataset and a benchmarking environment for AI models to automatically identify six clinically-relevant histopathologic sub-regions in H&E-stained whole slide images (WSIs). METHODS We reclassified the TCGA-GBM and TCGA-LGG data according to the WHO 2021 criteria and identified diagnostic WSIs of 188 glioblastoma (IDH-wt, CNS WHO Gr.4) patients. A well-defined annotation protocol was communicated to eight neuropathologists, across eight institutions, who created “ground truth” annotations for Cellular Tumor (CT), Geographic Necrosis, Cortical Infiltration, Pseudopalisading Necrosis (PN), Microvascular Proliferation, and Penetration into White Matter (PWM). Comprehensive WSI curation for elimination of artifactual content and follow up partitioning resulted in approximately 300,000 data samples/patches. FINDINGS Inter-observer variability assessment highlighted difficulties in establishing a “gold standard”. The least annotated sub-regions (PWM and PN) were the most challenging to the participating methods, whereas all methods achieved their highest accuracy in the most annotated sub-region (CT). The highest average performance across sub-regions was obtained by a foundation model-based method. However, this top-ranked solution achieved an F1 score of 0.51 (Accuracy=0.65, Sensitivity=0.56, Specificity=0.93). CONCLUSIONS Our findings suggest that achieving state-of-the-art performance, in automatically identifying clinically-relevant glioblastoma sub-regions, remains an unsolved research problem. Analysis of the ground-truth annotations underscores the critical need for more precisely defined and neuropathologically standardized annotation protocol. Current AI algorithms are facing difficulties in addressing glioblastoma’s heterogeneous histologic landscape in the absence of large, comprehensively, and consistently annotated data. Building upon these findings, and towards developing more accurate AI algorithms, the 2025 iteration of the BraTS-Path challenge is currently on-going by leveraging two million data samples, aiming to enhance our disease understanding and improve diagnostic accuracy, ultimately contributing to improved patient outcomes.

Abstract OBJECTIVE To demonstrate that Prostate-specific membrane antigen (PSMA) PET imaging, though primarily used in prostate cancer surveillance, can reveal PSMA uptake in non-prostatic tumors due to neovascular PSMA expression. BACKGROUND PSMA PET imaging is increasingly used for staging and monitoring prostate cancer. However, PSMA uptake is not exclusive to prostate carcinoma and may be observed in non-prostatic malignancies, including glioblastoma, due to neovascular expression. CASE REPORT We present a 67-year-old gentleman with metastatic prostate carcinoma, diagnosed in 2014 and treated with neoadjuvant Lupron, prostatectomy, and salvage radiation. In 2019, he developed nodal recurrence in retroperitoneal and bilateral iliac chain nodes, treated with lymphadenopathy, six cycles of Taxotere-based chemotherapy, and consolidative proton beam radiation (56.25 Gy/25 fractions). He later developed progression to the right proximal humerus and left coracoid process, treated with stereotactic body radiation therapy (25Gy/5 fractions). Follow-up PSMA PET/CT revealed no systemic abnormalities but showed a new PSMA-avid lesion in the right medial temporal lobe. Brain MRI confirmed a 3.5 x 1.7 x 2.5 cm ring-enhancing, intra-axial lesion with edema. The patient was asymptomatic. PSA was undetectable on abiraterone/prednisone and leuprolide. He underwent right frontotemporal craniotomy and resection. Pathology confirmed Glioblastoma, IDH-wildtype, CNS WHO Grade 4 with focal epithelioid features and positive MGMT promoter methylation. He received standard-of-care treatment with radiation, temozolomide, and TTF device. Surveillance PSMA PET and MRI showed no residual brain avidity or progression. Discussion: PSMA is a type II transmembrane glycoprotein expressed by prostatic and various non-prostatic tissues. In healthy brain parenchyma, PSMA expression is typically absent but is overexpressed in neovasculature of high-grade gliomas, including glioblastoma, IDH-wildtype, allowing for radiotracer uptake, as seen in this case. CONCLUSION This case illustrates the potential role of PSMA as a diagnostic tool, as it allows for easy identification of glioblastoma neovasculature, as demonstrated in our case report.

Abstract Diffuse midline glioma (DMG) is a universally fatal pediatric brainstem cancer. Outside of clinical trials, palliative radiotherapy (RT) remains the only approved treatment, with a median overall survival (OS) of <12 months. The brain-penetrant oral therapy dordaviprone (ONC201) has demonstrated early clinical efficacy by activating the overexpressed mitochondrial protease ClpP, inducing mitochondrial dysfunction. Our previous studies identified that dordaviprone triggers compensatory energy production via the PI3K/mTOR pathway to avoid cell death. To overcome this adaptation, we combined dordaviprone with the brain-penetrant pan-PI3K/mTOR inhibitor paxalisib, showing synergistic anti-tumor effects across multiple immunocompromised patient-derived xenograft models. These results led to the international Phase II clinical trial (PNOC022/NCT05009992), where >20% of patients enrolled pre-radiation achieved survival beyond 2 years. However, approximately 20% of patients experienced immune-related toxicities-mucositis, colitis, and skin reactions, likely driven by paxalisib’s pan-PI3K inhibition, as PIK3CG (p110γ) and PIK3CD (p110δ) are enriched in hematopoietic cells. In contrast, PIK3CA (p110α) is a known genetic dependency in DMG. We hypothesized that the brain-penetrant, p110α-selective inhibitor GCT-007 could maintain therapeutic efficacy while reducing immune toxicity. DMG patient-derived cell lines showed comparable sensitivity to paxalisib and GCT-007 (mean IC50: 0.659 μM vs. 0.775 μM, respectively). Unbiased proteomic profiling in two DMG lines treated with GCT-007 identified robust downregulation of PI3K/AKT/mTOR signaling, confirmed via immunoblotting. Pathway enrichment further showed upregulation of oxidative phosphorylation, reinforcing the rationale for synergistic combination with dordaviprone. In aggressive immunocompetent syngeneic models, GCT-007 significantly extended overall survival (median 53 vs. 40 days, p=0.0001). When combined with dordaviprone, enzastaurin, or immune checkpoint inhibitors (ICI), GCT-007 increased survival in immune-competent DMG models by an average of 53% to 77% compared to vehicle (p=0.001), with 25% of mice in the GCT-007 + ICI cohort achieving long-term survival. Ongoing spatial profiling of the tumor immune microenvironment and organ-toxicity screening is underway to support translation.

Abstract Glioblastoma (GBM) is a highly aggressive brain tumor with poor survival outcomes and limited tools for accurate prediction of individual patient prognosis. While many prognostic models rely on advanced molecular testing, such methods are not routinely accessible across most clinical settings. In this study, we identify a practical and clinically applicable approach to estimate prognosis for GBM patients using three objective and readily accessible features: the patient’s age, tumor surface irregularity determined from MRI scans, and the expression of ICAM1 measured by standard immunohistochemistry. We show that higher ICAM1 levels are linked to worse survival and a more inflamed but immunosuppressive tumor microenvironment. These findings are functionally validated in ICAM1-wildtype and knockout mouse models using single-cell proteomics via cytometry by time-of-flight, and an immune-comprehensive panel of antibodies. By combining age, ICAM1, and MRI-derived shape features into a single Clinical-Imaging-Molecular (CIM) score, we developed a model that can reliably separate patients with a GBM into higher- and lower-risk groups. This score was validated across multiple international institutions and patient populations and does not require genomic sequencing or specialized technologies. The CIM model could serve as a supplement for care providers to better tailor treatment plans and follow-up strategies for individual patients, making personalized care more achievable even in resource-limited settings.

Abstract BACKGROUND CNS ependymal tumors rarely demonstrate mixed subependymal and ependymal pathology. The clinical course of these tumors is difficult to predict, but prior studies have identified TERT mutations and loss of chromosome 6 as poor prognostic factors in these tumors when located in the posterior fossa. METHODS In this retrospective single-institution study, patients with a diagnosis of mixed subependymoma/ependymoma were identified and chart reviewed to describe demographics, pathological findings, treatment courses and clinical outcomes. RESULTS Eleven patients (median age 57, 2 female, 9 male) were identified. Ten tumors were in the posterior fossa and 1 in the cervical spine. At diagnosis, 2 underwent gross total resection, 8 underwent subtotal resection, and 1 was only biopsied. Radiotherapy was administered to 5 patients at initial diagnosis (54Gy in 30 fractions). Systemic therapy (temozolomide and bevacizumab) was administered at recurrence to 2 patients. Loss of chromosome 6 was present in 5 /11 patients and TERT mutations were present in 4/11 patients, with both present in 3/11 patients. Median progression-free survival (mPFS) was 90.1 months (95% CI: 90.1–NA), with 3 progression events observed at 22, 90, and 107 months. Median follow-up duration was 27.9 months. Receiving radiotherapy had a longer mPFS (98 vs 22 months, p=0.15), although non-significant. At the time of data cut-off none of the patients were deceased. CONCLUSIONS Mixed subependymoma/ependymoma is a rare diagnosis with varying clinical outcomes. Our case series demonstrated that the majority of patients did not exhibit the previously identified poor prognostic factors. Although non-significant due to limited case numbers, radiotherapy was suggestive of longer mPFS.

Abstract BACKGROUND MYCN-amplified spinal ependymoma is a recently classified, rare, and aggressive subtype of spinal cord tumor, first defined by the 2021 WHO CNS5 classification. It predominantly affects adults in their 30s, with a slight female predominance; fewer than 50 cases have been reported worldwide. PURPOSE To characterize the molecular, imaging, and clinical features of MYCN-amplified spinal ependymomas and highlight their distinctiveness to aid in early diagnosis and therapeutic planning. METHODS We retrospectively reviewed institutional cases of MYCN-amplified spinal ependymomas and performed a literature-based analysis. Data on imaging, histopathology, molecular profiles, and clinical outcomes were examined to identify distinguishing diagnostic and prognostic features. RESULTS These tumors are typically present as intradural extramedullary masses, often in the thoracic spine, with intense contrast enhancement and prominent peritumoral edema. A lack of a clear cleavage plane with the spinal cord, early leptomeningeal, and intracranial dissemination are hallmark features. Rare reports of systemic metastases (e.g., to the humerus and paraspinal muscle) exist. Histologically, they are high-grade (WHO grade III) anaplastic ependymomas with a distinct DNA methylation profile. MYCN amplification—commonly seen in medulloblastomas and glioblastomas—drives tumor aggressiveness. Clinically, these tumors are refractory to standard treatments, with high rates of recurrence or progression within 1–2 years post-treatment. The median overall survival is approximately seven years, significantly poorer than conventional spinal ependymomas. CONCLUSION MYCN-amplified spinal ependymomas represent a rare but highly aggressive tumor subtype with unique imaging, histological, and molecular features. Early recognition is critical given rapid progression and poor prognosis. Investigational therapies targeting MYCN—such as HDAC, PARP, Aurora A-kinase, and BET inhibitors—hold promise for improved outcomes.

Abstract BACKGROUND Intraoperative electrocorticography (ECoG) may aid localization of language function and help guide eloquent brain tumor resection. However, relevance of ECoG findings to language outcomes remains unclear. METHODS Fifteen patients with brain tumors (Mdn age=60; 67% high-grade glioma) underwent awake craniotomy and intraoperative language mapping. Time-frequency ECoG gamma activity was visualized in real-time followed by direct electrical stimulation (DES) across 3 paradigms (NeuroMapper Tasks: Object, Action, Auditory). Early and late ECoG gamma modulations and positive DES sites were co-registered with postoperative MRI. A subset (N=11) completed expressive language testing (Phonemic and Semantic Fluency, Confrontation Naming) prior to and <2 months following surgery. Correlational analyses and Mann-Whitney U tests examined relationships between test results and surgical impact upon expressive task-elicited gamma activity (Object and Action Naming). RESULTS Across tasks, positive DES sites were identified in 93% of cases (Auditory, 73%; Action, 71%, Object, 57%). No positive DES sites were resected. Early and/or late gamma activations were elicited in all patients, resected in 40%, and within 1 cm of surgical impact in 90%. Four patients (36%) showed reliable decline on at least 1 outcome, which was associated with Action Naming activity <1 cm of surgical impact (phi=.67, p=.035). On individual tests, Confrontation Naming and Phonemic Fluency decline were rare (9% and 18%), though Semantic Fluency declined in 36% of patients. Change in Semantic Fluency was associated with resection of Object Naming gamma sites (Z=-1.83, p=.044) and presence of Action Naming gamma features near surgical impact (Z=-2.25, p=.014). CONCLUSIONS Despite preservation DES sites and sparing of basic confrontation naming ability, verbal fluency declined in a substantial group of patients. Decline was associated with gamma features resected or near surgical impact. While preliminary, findings suggest task-based ECoG can augment DES in informing risk of surgically-acquired language decline.

Abstract High-grade gliomas (HGGs) are aggressive, infiltrative brain tumors with poor survival outcomes and limited therapeutic options. Their spatial and molecular heterogeneity necessitates large, well-curated datasets integrating imaging, clinical, and genomic information to enable precision medicine and drive novel treatment development. Using the Medtronic StealthStation neuronavigation system, we collected over 1,000 spatially localized intraoperative biopsy samples from more than 250 patients undergoing resection for glioma at Mayo Clinic and partnering sites. Each biopsy site was recorded with 3D coordinates, labeled in real time, flash-frozen, and annotated with metadata including collection time, anatomical location, and surgical plan. Samples were processed and entered into a secure database along with the corresponding sampling coordinates, frozen pathology, whole-exome and RNA sequencing data, 5-ALA fluorescence status, patient demographics, and T1, T1Gd, T2, and T2-FLAIR MRI. This multimodal dataset supports radiogenomic research by enabling precise mapping of imaging features to molecular characteristics. A subset of this data, including 268 biopsies from 52 patients (25 male, 27 female) with reported intraoperative 5-ALA fluorescence, was used to develop a radiomics model predicting locoregional patterns of 5-ALA positivity based on preoperative MRI. Image features were extracted from the biopsy sample coordinates across multiple MRI sequences and paired with corresponding 5-ALA fluorescence status to train the model for regional fluorescence prediction. The final model achieved 83% accuracy on the training set and 86% on the validation set using a 70-15-15 data split; this included 208 samples for training and 42 for validation, stratified by patient to ensure no overlap of samples from the same individual across sets. These findings demonstrate the feasibility of predicting 5-ALA fluorescence using radiomic features from standard preoperative MRI, offering a promising noninvasive strategy for improving intraoperative decision-making and enabling more precise, personalized resections in patients with high-grade gliomas.

Abstract Computer vision now enables deep learning models to link histopathologic features to molecular features in a quantitative manner not previously achievable by human pathologists. However, human interpretability is often limited. We trained a deep neural network to predict gene expression from digital whole slide images (WSIs) using 848 central nervous system (CNS) tumors with paired WSI and RNA-sequencing data. We used inferred RNA expression levels as a surrogate for protein expression of the CNS tumor lineage markers GFAP, OLIG2, and SSTR2, as well as the proliferation marker MKI67 (Ki67). We established thresholds for categorizing the inferred expression levels as positive or negative based on levels observed in cross-validation testing in select tumor types to approximate routine clinical interpretation of immunohistochemical (IHC) staining. We tested the sensitivity and specificity of our ‘virtual’ IHC on an independent, multi-institutional cohort of over 2,000 CNS tumors with objective diagnostic labels derived from DNA methylation-based tumor classification. As a dichotomous variable, inferred GFAP expression showed a sensitivity of 74% and a specificity of 99% as a glial marker in a cohort of gliomas and meningiomas. In the same cohort, OLIG2 showed a sensitivity of 75% and a specificity of 97% as a glial marker, while SSTR2 showed a sensitivity of 83% and a specificity of 97% as a marker of meningioma. In a cohort of ependymomas and gliomas, virtual IHC for OLIG2 showed a sensitivity of 75% and a specificity of 82% in distinguishing ependymomas from gliomas. Finally, in a cohort of gliomas, inferred expression of MKI67 demonstrated a trend consistent with what would be expected by actual IHC, with increasing MKI67 expression with increasing tumor grade. Our model provides the basis for a human interpretable and clinically applicable deep neural network to aid human pathologists in the diagnosis and grading of CNS tumors.