BACKGROUND:Molecular profiling is increasingly important for risk stratification and individualization of treatment recommendations for patients with meningioma. Prior investigations have established the genomic architecture of meningioma, but the distribution of meningioma copy number alterations (CNAs) and short somatic variants (SSVs) across demographic groups is incompletely understood. Here we define a prospective, consecutive benchmark for meningioma CNAs and SSVs across demographic groups. METHODS:Prospective next-generation DNA sequencing was performed on 1,104 tumor samples from 1,044 consecutive patients who were treated for meningioma or whose meningiomas underwent molecular testing at a large academic medical center in a major US city from 2019 to 2025. Univariate and multivariate regression analyses were performed to evaluate associations between demographic characteristics, CNAs, and SSVs. RESULTS:Meningiomas from Asian males were enriched in high-risk molecular features, including (1) copy number deletion of chromosomes 1p, 6q, and 14q, (2) copy number gain of chromosome 1q, (3) chromosome 1p/22q codeletion or co-occurrent 1p loss/1q gain, and (4) CDKN2A/B homozygous deletion. In contrast, meningiomas from females were enriched in low-risk molecular features, including SSVs in TRAF7, AKT1, and PIK3CA. The molecular architecture of meningiomas from Hispanic and non-Hispanic White individuals appeared similar, though Hispanic ethnicity was a unique independent predictor of low-grade disease. CONCLUSIONS:In this US-based cohort, the distribution of meningioma CNAs and SSVs varies across demographic groups. Asian males appear to have an increased risk of clinically aggressive meningioma due to enrichment in high-risk molecular features that were previously unappreciated in this patient population. Molecularly favorable meningiomas are more common in females, and low-grade meningiomas are more common in Hispanic individuals.

Background. Despite recent advances in the biology of IDH-wildtype glioblastoma, it remains a devastating disease with median survival of less than 2 years. However, the molecular underpinnings of the heterogeneous response to the current standard-of-care treatment regimen consisting of maximal safe resection, adjuvant radiation, and chemotherapy with temozolomide remain unknown. Methods. Comprehensive histopathologic, genomic, and epigenomic evaluation of paired initial and recurrent glioblastoma specimens from 106 patients was performed to investigate the molecular evolution and cellular phenotypes underlying differential treatment responses. Results. While TERT promoter mutation and CDKN2A homozygous deletion were early events during gliomagenesis shared by initial and recurrent tumors, most other recurrent genetic alterations (eg, EGFR, PTEN, and NF1) were commonly private to initial or recurrent tumors indicating acquisition later during clonal evolution. Furthermore, glioblastomas exhibited heterogeneous epigenomic evolution with subsets becoming more globally hypermethylated, hypomethylated, or remaining stable. Glioblastoma that underwent sarcomatous transformation had shorter interval to recurrence and were significantly enriched in NF1, TP53, and RB1 alterations and the mesenchymal epigenetic class. Patients who developed somatic hypermutation following temozolomide treatment had significantly longer interval to disease recurrence and prolonged overall survival, and increased methylation at 4 specific CpG sites in the promoter region of MGMT was significantly associated with this development of hypermutation. Finally, an epigenomic evolution signature incorporating change in DNA methylation levels across 347 critical CpG sites was developed that significantly correlated with clinical outcomes. Conclusions. Glioblastoma undergoes heterogeneous genetic, epigenetic, and cellular evolution that underlies prognostically different treatment responses.

Despite prior success in classifying recurrent glioma noninvasively with multi-parametric MRI and AI, clinical applicability has yet to be demonstrated due to a lack of robust model evaluation and spatial preservation of tumor characteristics. This study develops, robustly evaluates, and clinically validates an interpretable model for predicting recurrent tumors from spatially varying, histopathologically-confirmed tissue samples. Machine learning models were developed using 254 pre-surgical multi-parametric MRI patches surrounding coordinates of tissue samples taken during recurrent surgery. A test AUROC of 0.74 ± 0.08 for distinguishing recurrent tumors, and 0.99 ± 0.01 for normal-appearing brain, demonstrated the feasibility of spatially mapping heterogeneity. Important features were consistent with current literature, and uncertainty was correlated with model failures (p ≤ 0.05). Volumetrics derived from prediction maps of recurrent tumors generated using a separate cohort of 56 patients with recurrent high-grade gliomas were significantly associated with survival. These results demonstrate a step towards clinical applicability of spatially mapping glioma recurrence.

This genomic profiling study describes the molecular architecture of 1104 prospective consecutive meningioma samples from one academic medical center.

Abstract Background: Molecular profiling of meningioma has revealed biologic drivers and therapeutic vulnerabilities that have refined risk stratification and guided clinical trials. Most molecular studies of meningioma are limited to retrospective cohorts, and the population prevalence of meningioma genomic alterations is incompletely understood. Methods: DNA mutations and copy number alterations (CNAs) were defined across a consecutive cohort of 1,104 meningiomas that were prospectively analyzed using the next-generation UCSF500 DNA sequencing assay at a single institution from 2019 to 2025. Results: Meningiomas were CNS WHO grade 1 (49.4%), grade 2 (30.9%), grade 3 (7.0%), or undefined (12.7%). Median patient age was 60 years (range 2-91) and 64.1% were female. Patient race included white (39.2%), Asian (10.4%), Black or African American (3.6%), Native American or Alaska Native (0.6%), Native Hawaiian (0.3%), other Pacific Islander (0.3%), other (9.5%), or unknown. Ethnicity was reported as Hispanic or Latino in 125 cases (11.3%). Loss of chromosomes 22q (64.1%), 1p (40.7%), and 14q (22.3%) were the most common CNAs. Multivariate regression identified male sex, increasing age, and Asian vs. white race as significant predictors of multiple CNAs that are associated with poor clinical outcomes, including co-occurrent 1p loss/1q gain (odds ratio [OR] for male sex: 2.79, p<0.001; increasing age OR 1.02, p=0.028; Asian vs. white race OR 2.77, p=0.001). CNA burden was enriched in high grade meningiomas (p<0.001), and male sex (OR 10.1, p<0.001), increasing age (OR 1.04, p=0.026), and Asian vs. white race (OR 4.31, p=0.047) were significant predictors of higher CNA burden. The most common short somatic variants (SSVs) were in NF2 (53.6%) and TRAF7 (16.3%), which were mutually exclusive in all but 2 meningiomas. CDKN2A/B homozygous deletions and TERT promoter mutations were identified in 3.1% and 3.6% of meningiomas, respectively. Increasing age was a significant predictor of NF2 (OR 1.02, p<0.001) and TERT promoter mutation (OR 1.06, p=0.001), and Asian vs. white race was a significant predictor of CDKN2A/B mutation (OR 4.26, p=0.003). Median tumor mutation burden (TMB) was 3.4/megabase. Mutations in ARID1A (p<0.001), ARID2 (p=0.028), CDKN2A/B (p=0.017), KMT2D (p=0.011), SMARCB1 (p=0.0061), TERT (p<0.001), TET2 (p=0.019), and TP53 (p=0.0033) were associated with elevated TMB. Increasing age (OR 1.01, p=0.02) and Asian (OR 5.02, p<0.001) and Black (OR 2.54, p=0.004) vs. white race were significant predictors of higher TMB. Conclusion: Prospective next-generation DNA sequencing of consecutive meningiomas defines the population prevalence of molecular alterations. These data fill a critical gap in the understanding of biological drivers of meningioma behavior and therapeutic response across demographic groups. Citation Format: Minh P. Nguyen, Kanish Mirchia, Brooke C. Braman, Ramin A. Morshed, Nancy Ann Oberheim Bush, Javier E. Villanueva-Meyer, William C. Chen, Walter Patrick Devine, Arie Perry, David R. Raleigh. Genomic profiling of 1,104 consecutive, prospective meningiomas defines the population prevalence of molecular alterations and elucidates potential biomarkers of treatment response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1979.

Background Standard treatments for meningioma include surgery and radiotherapy, but the timing and technical details of these treatments are debated, particularly as advances in meningioma biology expand options for treatment. Patients with meningioma may experience adverse quality-of-life and neurocognitive sequelae following diagnosis and treatment, and survivorship resources for these patients are limited.Methods We initiated a Multidisciplinary Meningioma Program (MMP) to advance the care of patients at our institution in September 2024. The MMP consists of synergistic clinical, scientific, and survivorship resources, including a weekly tumor board and multidisciplinary clinic, clinical trials and prospective registries for translational research, a meningioma-specific support group, and basic and translational research. In this article, we describe the implementation and initial patient-facing aspects of the MMP.Results In the first 15 months of the MMP, 602 cases were discussed at the MMP tumor board, and 142 patients were seen in consultation in the MMP multidisciplinary clinic. Twelve percent of patients discussed in the MMP tumor board were offered clinical trial enrollment, and 84% were offered prospective registry enrollment. Participants in the MMP support group cited informational and emotional benefits. Following implementation of the MMP, the clinical volume of consultations for meningioma increased by 87.0%-533.3%, and the volume of treatments increased by 42.1%-115.2% across clinical departments.Conclusions Through creation of a multidisciplinary program for meningioma treatment, research, and support services, we enabled consensus clinical recommendations, streamlined enrollment on clinical trials and prospective registries, improved access to support services, and increased patient volume. Meningiomas are common tumors that arise from the lining of the brain and spinal cord, and evidence-based strategies for the care of meningioma are evolving. Despite the prevalence of meningioma, clinical trials, prospective registries, and survivorship resources for patients with meningioma are limited. We hypothesized that patients with meningioma would benefit from multidisciplinary management and coordination of care across healthcare providers and specialties. Here we report on the development and implementation of a multidisciplinary meningioma program, which, to our knowledge, is the first meningioma-specific program to integrate a multidisciplinary clinic, a specialized weekly tumor board conference, clinical trials, research studies, and survivorship resources. This Multidisciplinary Meningioma Program fostered consensus clinical recommendations for patient care, streamlined and increased patient enrollment on clinical trials and prospective registries, and generated patient-facing support programs, including a peer support group, which participants found beneficial. The program also increased the volume of new patients seen for meningioma care in neurological surgery, radiation oncology, and neuro-oncology, and may serve as a model for other institutions.

BACKGROUND:DNA methylation profiling is a predictor of meningioma behavior and outcomes. We aimed to identify qualitative and quantitative MRI features to distinguish between three meningioma methylation groups: Merlin-intact, Immune-enriched, and Hypermitotic, each with distinct clinical outcomes, biologic features, and therapeutic vulnerabilities. MATERIALS AND METHODS:Preoperative MRIs were retrospectively analyzed in meningiomas with previous DNA methylation profiling. Pearson's Chi-square, Fisher exact, and ANOVA tests were used to compare features between the three groups. ROC AUCs were used to assess the accuracy in discriminating between groups. RESULTS:165 patients (54 years ± 14 SD; 58 men) were analyzed. 60 meningiomas were Merlin-intact, 55 Immune-enriched, and 50 Hypermitotic. Qualitative reduced diffusion (p< .001), nADC (p< .001), T2WI signal intensity (p= .02), T1 CE volume (p<.005), and tumor site (p<.001) varied between the groups. Merlin-intact meningiomas had higher T2WI signal intensity than Immune-enriched tumors (1.97 ± 0.98 vs 1.63 ± 0.45, p= .04). Hypermitotic meningiomas had the highest proportion of tumors with qualitative reduced diffusion (67%) and lowest nADC values (1.07 ± 0.14) compared to Merlin-intact (18%, p< .001; 1.41 ± 0.30, p< .001) and Immune-enriched (31%, p= .02; 1.29 ± 0.29, p= .002) meningiomas. The presence of qualitative reduced diffusion (AUC 0.71, p= .001) and lower nADC (AUC 0.82, p< .001) were able to predict Hypermitotic meningiomas. Merlin-intact tumors were predicted by the absence of qualitative reduced diffusion (AUC 0.66, p= 0.01), higher nADC (AUC 0.74, p< .001), and higher T2WI signal intensity (AUC 0.64, p= .047). Hypermitotic tumors (64.3 cm3 ± 49.1) had larger T1CE volumes than Merlin-intact (42.5 cm3 ± 37.9, p= .02) and Immune-enriched (38.2 cm3 ± 37.7, p=<.002) tumors, with tumor size a predictor of Hypermitotic (AUC 0.65, p=.003) and Immune-enriched (AUC 0.62, p=.02) meningiomas. Merlin-intact tumors were predicted by presence at the skull base (AUC 0.67, p<.001) while Immune-enriched tumors were predicted by location outside of the skull base (AUC 0.61; 95% CI 0.70, 0.52, p= .02). CONCLUSIONS:MR imaging has the potential to discriminate between different molecular groups of meningioma and to serve as a surrogate non-invasive marker of tumor behavior.

Meningiomas are among the few intracranial tumors that are more common in females than males. The majority of meningiomas express progesterone receptor (PR), and differences in exposure to progestogens are thought to contribute to sex-based disparity in meningioma. Previous epidemiological and clinical data have demonstrated that multiple progestins, such as cyproterone acetate (CPA), are linked to meningioma. More recently, international epidemiological studies have found a significantly increased risk of meningioma with depot medroxyprogesterone acetate (DMPA), a widely used injectable contraceptive sold under the trade name Depo-Provera. Although the absolute risk of meningioma remains small, exposure to DMPA increases the risk of meningioma by 1.5- to 5.5-fold. DMPA-associated meningiomas are clinically significant, as the risk of craniotomy for tumor resection, not just the risk of tumor diagnosis, is elevated after DMPA exposure. These data suggest that DMPA should be avoided in individuals with a history of meningioma when alternatives are available. The risk of meningioma should be discussed with patients during DMPA counseling, and the utility of screening patients with prolonged DMPA exposure for meningiomas should be investigated. Further work is needed to elucidate the mechanisms of PR signaling in meningioma and to understand the complex interplay of tumor genomics, hormone exposure and signaling, and social and structural drivers of health that may contribute to disparities in meningioma.

Alpha-ketoglutarate (aKG) is a central intermediate of cerebral energy metabolism and a precursor for glutamate synthesis in the brain. Alterations in aKG metabolism occur in pathological contexts, including isocitrate dehydrogenase (IDH) mutant astrocytomas and oligodendrogliomas, in which mutant IDH converts aKG to the oncometabolite 2-hydroxyglutarate. Given its central role in brain metabolism, non-invasive interrogation of aKG-dependent metabolic flux is needed. Hyperpolarized (HP) 13C MR enables real-time visualization of metabolic conversion by transiently enhancing signal intensity by several orders of magnitude. Leveraging this approach, we report the first-in-human feasibility and safety study of HP [1-13C]aKG MR spectroscopy in the healthy brain (n = 3). A standard operating procedure (SOP) was developed for sterile [1-13C]aKG dose production, achieving reproducible polarization levels averaging 30.5 ± 2.2%. Following intravenous administration, time-resolved 13C spectra in healthy volunteers demonstrated the detection of HP aKG resonance and a measurable downstream glutamate signal, consistent across repeat acquisitions, with a delayed temporal profile relative to aKG observed in a representative dataset. Although performed in healthy volunteers, these results establish feasibility for HP [1-13C]aKG metabolic imaging to open a new window into normal and pathological brain cellular metabolism.

Since its first activities in 2008 and 2009, the Response Assessment in NeuroOncology (RANO) group has given guidance on response assessment, trial design, and trial procedures to improve and standardize the way clinical trials in neurooncological studies are performed. To achieve its objectives, a variety of working groups have been initiated that cover many aspects of clinical trial design and outcome assessment in patients with tumors affecting the Central Nervous System. The RANO working groups are built on expertise without a formal structure, which makes rapid responses to new developments possible. RANO is aiming at evidence-based guidelines and recommendations, but in the absence of evidence will provide consensus-based guidance achieved by inviting recognized international experts. In its 15 years of existence, more than 60 RANO papers have been published mostly in high-ranking journals, and its recommendations have been accepted by regulators and industry as guiding principles. RANO organizes two meetings per year, one in conjunction with the annual American Society for Clinical Oncology (ASCO) meeting, and one during the annual Society for Neuro-Oncology meeting. These meetings are open, as are the working groups of RANO. New initiatives are welcomed.

Artificial intelligence (AI) has the potential to enable more precise, efficient, and reproducible interpretation of medical imaging data to improve patient care in paediatric neuro-oncology. Paediatric brain tumours present distinct histopathological, molecular, and clinical challenges that require tailored AI solutions. Recent advances have led to paediatric-specific AI tools for tumour segmentation, treatment response evaluation, recurrence prediction, toxicity assessment, and integrative multimodal analysis. These innovations have the potential to improve diagnostic accuracy, streamline workflows, and inform personalised treatment strategies. However, clinical implementation remains hindered by challenges related to data heterogeneity, model generalisability, and integration into clinical practice. In this Policy Review, we highlight key developments, challenges, and priority areas for imaging-based AI for paediatric neuro-oncology. Our goal is to provide oncology practitioners with a focused overview of current capabilities, unmet needs, and future directions at the intersection of AI and paediatric neuro-oncology.

The Response Assessment in Pediatric Neuro-Oncology (RAPNO) criteria provide an important framework for evaluating treatment efficacy and tumour progression in clinical studies of paediatric brain tumours. As artificial intelligence (AI) rapidly transforms clinical practice, integrating AI into the RAPNO framework presents a unique opportunity to enhance quantitative, data-driven approaches for response assessment. However, successful clinical implementation faces challenges, including variability in imaging protocols, scarce annotated datasets, and regulatory and ethical considerations. To address these barriers, this Policy Review, led by the AI for Assessment in Pediatric Neuro-Oncology (AI-RAPNO) subcommittee, outlines key challenges and proposes recommendations to improve AI trustworthiness, generalisability, and implementation in paediatric neuro-oncology. We highlight the potential of AI for response assessment, multimodal integration, and synthetic control groups in clinical trials. Our recommendations emphasise the need for standardised imaging protocols, robust validation frameworks, and infrastructure to support AI readiness in clinical studies. By addressing these needs, AI-RAPNO aims to bridge the gap between AI research and clinical application, ensuring reliable and actionable AI-driven tools for paediatric neuro-oncology.

The current standard-of-care (SOC) practice for defining the clinical target volume (CTV) for radiation therapy (RT) in patients with glioblastoma still employs an isotropic 1–2 cm expansion of the T2-hyperintensity lesion, without considering the heterogeneous infiltrative nature of these tumors. This study aims to improve RT CTV definition in patients with glioblastoma by incorporating biologically relevant metabolic and physiologic imaging acquired before RT along with a deep learning model that can predict regions of subsequent tumor progression by either the presence of contrast-enhancement or T2-hyperintensity. The results were compared against two standard CTV definitions. Our multi-parametric deep learning model significantly outperformed the uniform 2 cm expansion of the T2-lesion CTV in terms of specificity (0.89 ± 0.05 vs 0.79 ± 0.11; p = 0.004), while also achieving comparable sensitivity (0.92 ± 0.11 vs 0.95 ± 0.08; p = 0.10), sparing more normal brain. Model performance was significantly enhanced by incorporating lesion size-weighted loss functions during training and including metabolic images as inputs.

Brain metastases are a frequent and debilitating manifestation of advanced cancer. Here, we collect and analyze neuroimaging of 3,065 cancer patients with 13,067 brain metastases, representing an extensive collection for research. We find that metastases predominantly localize to high perfusion areas near the grey-white matter junction, but also identify notable differences depending on the primary cancer histology as well as brain regions which do not conform to this relationship. Lung and breast cancers, in contrast to melanoma, frequently metastasize to the cerebellum, hinting at biological pathways of spread. Additionally, the deep brain structures are relatively spared from metastasis, regardless of primary cancer type. Leveraging this data, we propose a probabilistic brain metastasis risk model to enhance the therapeutic ratio of whole-brain radiotherapy by targeting high risk areas while preserving cortical and subcortical brain regions of functional significance and low metastasis risk, potentially reducing the cognitive side effects of therapy.

An ongoing challenge faced in neuro-oncology is non-invasively distinguishing treatment-induced effects (TxE) following chemotherapy and/or radiation therapy from true tumor recurrence (rTumor). Previous research has explored the utility of AI-based models for this task but has overlooked within-lesion heterogeneity and the non-enhancing, T2-lesion. We investigated the value of integrating models trained using: 1) multi-parametric MRI (mpMRI) including anatomical, diffusion-weighted, and perfusion-weighted images, and 2) individual spectra from 1cc regions surrounding the tissue-sample locations, to improve the discrimination of treatment-effect from tumor recurrence. This retrospective study included 144 high-grade glioma patients who underwent MRI scans before surgical resection for suspected recurrence. Imaging included standard anatomical, diffusion-weighted, and dynamic-susceptibility-contrast perfusion-weighted MRI, and lactate-edited ¹H-MRSI. 324 spatially-localized tissue-samples were histopathologically classified as TxE or rTumor. The mpMRI model utilized 10 mm volumetric patches of each standardized image contrast (T2-FLAIR, T1-post-contrast, peak height and %-recovery from perfusion, ADC and FA from diffusion) centered on the tissue sample coordinates and generated 20 ensemble predictions. The AI-based spectra model predicted Ki-67, cellularity, and a composite tumor aggressiveness index from the entire 1D-spectrum reconstructed at the location of the tissue-sample. These predictions were concatenated into 4 machine-learning classifiers, which were trained on the combined feature set and on individual imaging and spectral features to assess model contributions. Balancing the dataset enhanced the performance of all models, most notably that of gradient boosting (AU-ROC: 0.655 to 0.724). The radiopathomic spectra model increased performance of the weighted ensemble by 4% to 0.73+/-0.04 AU-ROC when integrated with our previously-developed mpMRI model. Three imaging features consistently ranked among the top five predictors across classifiers. Integrating radiopathomic-derived features from an AI-based model using the entire spectrum with mpMRI features preserved diagnostic performance across all models, with a maximum improvement of 5.26% in AU-ROC. Current work is evaluating different strategies for combining models for contrast-enhancing and non-enhancing samples separately.

Isocitrate dehydrogenase (IDH)-mutant gliomas demonstrate metabolic reprogramming and remain challenging to assess using conventional MRI alone. 1H MR spectroscopic imaging (MRSI) of steady-state metabolism and real-time hyperpolarized carbon-13 (HP-13C) MRI of dynamic metabolism are of interest for evaluating response to treatment. This study aimed to improve characterization of IDH-mutant glioma through multimodal analysis of 1H/HP-13C MRI and histopathology in patients before surgery for suspected tumor progression. Six IDH-mutant glioma patients (1F/5M, 46.2±7.6 years; 5 astrocytoma [2 grade 2, 1 grade 3, 2 grade 4], 1 grade 3 oligodendroglioma) received multiparametric 1H/HP-13C MR examinations on a 3-Tesla MR scanner before surgery. All patients had confirmed tumor progression; one grade 2 underwent malignant transformation to grade 3. During surgery, 21 tissue samples were collected with imaging coordinates and analyzed for histopathology. Imaging parameters were reprocessed to center on recorded biopsy locations to provide normalized apparent diffusion coefficients (nADC)/relative cerebral blood volume (nCBV)/relative cerebral blood flow (nCBF)/peak height (nPH), %recovery, choline-to-N-acetylaspartate index (CNI), pyruvate-to-lactate conversion rate (kPL), ratios of lactate-to-pyruvate (Lac/Pyr) and bicarbonate-to-pyruvate (Bic/Pyr). Tissue samples were assessed for treatment effects, blood vessels (BV), carbonic anhydrase 9 (CA9), and Ki-67 expressions. Due to limited sample sizes, summary statistics and trends were assessed. 3 tissue samples with pure treatment effects showed higher Bic/Pyr ratio (median [min max]: 0.05 [0.04, 0.15] vs. 0.03 [0.005, 0.31]) and lower CNI (0.20 [-0.24, 0.64] vs. 8.01 [0.58, 14.88]) compared to 15 recurrent tumor tissue samples. In recurrent tumor tissue samples, higher tumor proliferation (Ki67) was associated with higher kPL, CNI, nPH, nCBV, nCBF, and nADC, while no clear association was observed between vascularity (BV) or hypoxia (CA9) indexes and imaging parameters. This study showcases our initial experience in using multiparametric 1H/HP-13C MR imaging to characterize recurrent IDH-mutant gliomas and identify noninvasive imaging markers corresponding with tissue histopathology.

PURPOSE:Sonodynamic therapy, which combines a tumor cell-selective sonosensitizer with ultrasound, is gaining attention as a promising new treatment approach for glioblastoma. The objective of this case study is to report on the first applications of 5-aminolevulinic acid (5-ALA) in combination with low-intensity, non-targeted ultrasound as neo-adjuvant treatment in therapy naïve glioblastoma. METHODS:Three patients with therapy naïve newly diagnosed glioblastoma were treated once before cytoreductive surgery with 5-ALA in combination with hemispheric, low-intensity, non-targeted ultrasound, assuming cell death to be triggered by non-ablative activation of 5-ALA-induced, tumor selective porphyrins. RESULTS:No adverse effects were noted. Post-procedural MRI indicated a decrease in apparent diffusion coefficient values in tumors, suggesting cytotoxic effects. Relative cerebral blood volumes and leakage were increased for two patients with available perfusion imaging. Tissue obtained during surgery suggested increased cleaved-caspase III expression, a marker of apoptosis. CONCLUSION:We saw an immediate marked imaging response indicating cytotoxic edema and indications of a histopathology response from just a single treatment. Correlation to clinical outcomes and extension of overall survival remains to be seen. A Phase 1 safety study has been submitted for regulatory approval.

BACKGROUND:Radiosurgery is a standard treatment modality for arteriovenous malformations (AVMs). Spinal cord AVMs represent a particular challenge for radiosurgery due to the significant involvement of normal spinal cord tissues that are inseparable from the AVM. OBSERVATIONS:MR and CT images of the spine acquired using different patient setups and immobilization can be challenging to co-register for accurate radiosurgery planning due to the mobility of the spine and resulting differences in spine position between imaging studies. Integrating MRI simulation into radiosurgery treatment design solves this issue and can be valuable for accurately targeting spinal cord AVMs while sparing normal adjacent spinal cord tissue. LESSONS:Here the authors present the case of a female patient with cervical spinal cord AVM who was treated with spinal cord radiosurgery that uses this approach. https://thejns.org/doi/10.3171/CASE25413.