Glioblastoma is a highly malignant brain tumor in which maximal safe resection is associated with improved survival, yet the oncological benefit of resection varies by molecular subtype. Recent work has shown that DNA methylation-defined subtypes, particularly receptor tyrosine kinase (RTK) I and II, benefit from complete CE (contrast-enriched) resection compared to mesenchymal tumors, highlighting the need for pre- or intraoperative tools that guide resection based on tumor biology. Here, we present iSTAMP ( i ntraoperative S patially-informed T umor A rchitecture M apping and P rofiling) a real-time, label-free molecular classification framework using stimulated Raman scattering microscopy and graph-based deep learning to predict glioblastoma epigenetic subtypes intraoperatively (within 5-7 minutes). Across 1,295 intraoperative tissue samples from 236 patients profiled with EPIC methylation arrays, our graph attention network achieved high predictive performance for all major subtypes (AUC range 0.88-0.99), with spatially stable predictions across tumor regions. RTK subtypes, but not mesenchymal tumors, showed significant survival benefit from GTR (HR = 0.42, p = 6.1 ×10 -6 ). Explainable AI methods revealed subtype-specific histopathological features, including necrosis and macrophage infiltration in mesenchymal tumors versus glio-fibrillary matrix or axon-rich regions in RTK tumors. Spatial transcriptomic validation confirmed cellular correlates with defined subtype specific SRH features. These findings support the integration of Raman-based molecular diagnostics into intraoperative workflows to guide biologically informed surgical strategies in glioblastoma.

Fluorescence-guided surgery improves intraoperative brain tumor visualization, but currently available agents remain unreliable for several common entities, particularly lower-grade gliomas and lesions without visible 5-aminolevulinic acid (5-ALA)-induced fluorescence. We prospectively analyzed 69 surgically obtained tumor specimens from 61 patients with WHO grade 4 gliomas, WHO grade 2/3 gliomas, meningiomas, and brain metastases before and after ex vivo incubation in 4 µM water-soluble high-load hypericin-polyvinylpyrrolidone complex (HHL-PVP). Fluorescence lifetime and intensity were quantified using a dual-tap CMOS camera system with a hypericin-specific 575-615 nm bandpass filter. HHL-PVP incubation significantly increased both fluorescence intensity and lifetime in all tumor entities, including specimens without visible 5-ALA fluorescence. An independently derived combined lifetime/intensity regression model discriminated pre- from post-incubation measurements with an area under the receiver operating characteristic curve of 0.975, sensitivity of 98.6%, and specificity of 82.6%; entity-specific areas under the curve ranged from 0.933 to 1.000. These findings support robust hypericin-associated signal detection across major brain tumor entities after ex vivo incubation and provide a rationale for future in vivo evaluation of HHL-PVP in fluorescence-guided neuro-oncological surgery.

High-grade gliomas (HGGs) are the most aggressive adult brain tumors, with a dismal median survival of approximately 15 months, highlighting the need for novel therapeutic strategies. In a prior immunotherapy trial using dendritic cells against glioblastoma, miR-216b emerged as a potential predictive biomarker. Thus, we hypothesize that miR-216b impacts glioma aggressiveness and thereby therapeutic success. Here, we demonstrate that miR-216b is significantly downregulated in the majority of Isocitrate dehydrogenase 1/2 (IDH) wild-type HGG tissue samples (n = 42) and cell models (n = 18). Functional assays revealed that miR-216b overexpression impairs glioma cell proliferation, migration, and stemness characteristics. Transcriptomic and target prediction analyses identified CDK4, a key cell cycle regulator, as a direct target of miR-216b, confirmed via luciferase reporter assays. Correspondingly, upregulating miR-216b (mimic) via transfection decreased CDK4 mRNA and protein levels accompanied by a p21-dependent increase of cells in G0/G1 phase. In addition, miR-216b expression correlated with increased sensitivity to the CDK4/6 inhibitor Abemaciclib. Notably, miR-216b levels were significantly higher in less aggressive IDH-mutant gliomas (n = 21), linking its downregulation to malignancy grade. Collectively, our findings discovered miR-216b as a tumor suppressor in HGGs, modulating CDK4 expression and affecting the responsiveness to CDK4/6 inhibitors. The observed results support the potential of miR-216b as both a prognostic and predictive indicator in HGGs.

We investigated whether metabolic ratios derived from ultra-high-field 7-T 3D-FID-CRT-MRSI can predict intraoperatively visible 5-aminolevulinic acid (5-ALA) fluorescence in gliomas and compared their predictive performance to established imaging markers, including contrast enhancement (CE) on MRI and PET tumor-to-normal ratio (TNR). We retrospectively analyzed 43 patients with histopathologically confirmed adult-type diffuse gliomas (CNS WHO grades 2–4) who underwent preoperative 7-T MRSI and 5-ALA-guided resection. Group differences between 5-ALA-positive and 5-ALA-negative tumors were tested for 16 metabolic ratios to either total creatine (tCr) or combined N-acetylaspartate and N-acetyl-aspartyl-glutamate (NAA + NAAG; total NAA; tNAA) using non-parametric statistics with Šidák correction. CE-MRI status and PET TNR (subcohort, n = 31) were included as reference predictors. We additionally evaluated a subgroup of non-enhancing gliomas (n = 27). Receiver operating characteristic (ROC) analysis was performed to determine diagnostic performance. 5-ALA-positive gliomas demonstrated significantly altered metabolic profiles, showing lower mI/tNAA (p < 0.001) and higher Gln/tCr, Glx/tCr, Gly/tCr, and GSH/tCr ratios (all p < 0.001). These ratios achieved high predictive accuracy for fluorescence (AUCrange = 0.79–0.94), comparable or superior to PET TNR (AUC = 0.90) and CE-MRI (AUC = 0.84). In a subcohort of nonenhancing gliomas, Gly/tCr and Gln/tCr showed a high prediction accuracy (AUC = 0.90). 7-T MRSI metabolic ratios can predict intraoperative 5-ALA fluorescence and may serve as an alternative or adjunct to CE-MRI and PET for preoperative patient selection for 5-ALA administration. Finally, these findings could be especially beneficial in non-enhancing gliomas, where CE-MRI offers limited predictive information. Question Does 7-T MRSI enable preoperative prediction of 5-ALA fluorescence to support patient selection for fluorescence-guided glioma surgery? Findings Several 7-T MRSI metabolic ratios (mI/tNAA, Gln/tCr, Glx/tCr, Gly/tCr and GSH/tCr) robustly predicted 5-ALA fluorescence across glioma subtypes, with diagnostic performance comparable to contrast-enhanced MRI and PET. Clinical relevance Ultra-high-field 7-T MRSI enables noninvasive preoperative prediction of intraoperative 5-ALA fluorescence in gliomas with performance comparable to PET and contrast-enhanced MRI, supporting surgical planning without the need for contrast agents or radiation exposure.

Abstract Background Secondary malignancies following multimodal oncological treatment are rare but represent a severe long-term complication, particularly in pediatric patients. Systematic analyses in pediatric neuro-oncology are currently lacking. Methods Based on the complete institutional pediatric neuro-oncology database, patients who developed a secondary malignancy during follow-up were identified. Extracranial secondary malignancies and meningiomas were detected using assigned ICD codes (C diagnoses excluding C71 or D32), extracted via an algorithm-based approach. Intracerebral secondary malignancies were identified through an algorithm-driven analysis of all tumor board decisions, using both keyword-based text search and a large language model. Clinical data were retrieved from the hospital patient information system. Results Among a total of 1823 patients in the pediatric neuro-oncology database, 28 patients with 34 secondary malignancies were identified. The most frequent primary tumors were medulloblastoma (n = 13), ependymoma (n = 3), and atypical teratoid/rhabdoid tumor (ATRT; n = 3). Most patients (n = 21) had been treated with craniospinal irradiation, there were only 5 patients with no documented radiotherapy. Likewise, only 7 patients had not undergone chemotherapy for the primary tumor. The most common secondary malignancies were high-grade gliomas (n = 9), meningiomas (n = 7), leukemias (n = 6), and thyroid carcinomas (n = 4). Latency periods varied substantially, ranging from 6.2 years for leukemias to 17.2 years for meningiomas. While thyroid carcinomas and meningiomas showed excellent prognosis, survival in patients with secondary high-grade gliomas was severely limited. Conclusions The applied information technology–based methods successfully identified patients with secondary malignancies within a comprehensive pediatric neuro-oncology database. The observed tumor entities correspond to expected patterns, with primary tumors amenable to curative multimodal therapy and typical secondary malignancies. Further analyses focusing on clinical risk factors and molecular characteristics of neuro-oncological secondary malignancies are planned.

IntroductionMagnetic resonance (MR) imaging is essential for diagnosing central nervous system (CNS) tumors, guiding surgical planning, treatment decisions, and assessing postoperative outcomes and complications. While recent work has advanced automated tumor segmentation and report generation, most efforts have focused on preoperative data, with limited attention to postoperative imaging analysis.MethodsThis study introduces a comprehensive pipeline for standardized postsurgical reporting in CNS tumors. Using the Attention U-Net architecture, segmentation models were trained, independently targeting the preoperative tumor core, non-enhancing tumor core, postoperative contrast-enhancing residual tumor, and resection cavity. In the process, the influence of varying MR sequence combinations was assessed. Additionally, MR sequence classification and tumor type identification for contrast-enhancing lesions were explored using the DenseNet architecture. The models were integrated seamlessly into an automated and standardized reporting pipeline, following the RANO 2.0 guidelines. Training was conducted on multicentric datasets comprising 2000 to 7000 patients, incorporating both private and public data, using a 5-fold cross-validation.ResultsEvaluation included patient-, voxel-, and object-wise metrics, with benchmarking against the latest BraTS challenge results. The segmentation models achieved average voxel-wise Dice scores of 87%, 66%, 70%, and 77% for the tumor core, non-enhancing tumor core, contrast-enhancing residual tumor, and resection cavity, respectively. Classification models reached 99.5% balanced accuracy in MR sequence classification and 80% in tumor type classification.DiscussionThe pipeline presented in this study enables robust, automated segmentation, MR sequence classification, and standardized report generation aligned with RANO 2.0 guidelines, enhancing postoperative evaluation and clinical decision-making. The proposed models and methods were integrated into Raidionics, open-source software platform for CNS tumor analysis, now including a dedicated module for postsurgical analysis.

Although patients with childhood cerebellar pilocytic asytrocytomas (CCPA) demonstrate favorable survival rates, long-term cognitive and behavioral impairments are frequently observed. While some studies have already identified short-term functional brain alterations in mixed patient cohorts, research focusing on the structural connectivity in CCPA is missing. Therefore, we aimed to investigate the white matter tracts in adults who underwent resection of a cerebellar pilocytic astrocytoma in childhood. Thirteen patients with CCPA who underwent 3 Tesla magnetic resonance imaging (MRI) in average 15 years after resection and 13 age- and sex-matched healthy controls were enrolled in this study. Cortical thickness (CT) and volume of brain regions were quantified using Freesurfer. Automated tract segmentation was conducted with TractSeg based on probabilistic tractography. Data preprocessing was carried out using MRtrix3 and FSL. The left (p = 0.023) and right (p = 0.049) lingual gyrus and the right anterior cingulate (p = 0.016) showed increased CT in patients compared to controls. Additionally, the volume of the left parahippocampal gyrus was significantly greater in the patient group (p = 0.033), whereas a reduction in the volume of the left precentral gyrus was observed (p = 0.027).Tractography analysis revealed significantly reduced fractional anisotropy (FA) values in left and right superior cerebellar peduncle and middle cerebellar peduncle (p < 0.001) in patients. Conversely, increased FA values were observed in the left cingulate tract (p < 0.001). Our study revealed the first comprehensive long-term structural brain alterations in patients following resection of CCPA, including changes in cortical morphology and white matter integrity. These findings give mechanistic insights into the long-term impact on cognition of surgery for CCPA, thereby opening the avenue for imaging surrogates on cognitive outcome.

Glioblastoma (GBM) is a high-grade glioma marked by high intratumoral molecular heterogeneity, resistance to therapy and poor prognosis. Leveraging a multisampling approach, we investigated the relation between genomic and physical distance, and whether this information was captured by imaging phenotypes and patient outcomes. We profiled 79 spatially distinct tumor regions from 24 GBM patients (F/M = 0.60; median age = 65.5 years) using deep whole-exome sequencing (>300x coverage). Genomic distance was quantified by pairwise Euclidean distance of somatic mutational profiles and integrated with 3D neuronavigation-based spatial coordinates and progression-free survival (PFS). Two distinct growth patterns emerged: expansive, defined by strong correlation between molecular and spatial distance (RE = 0.6), and stochastic, characterized by molecular divergence uncoupled from spatial proximity (RS = -0.2). High molecular distance correlated with reduced PFS (R = -0.5538, p = 0.026), and stochastic expansion predicted unfavorable outcome (p = 0.035), frequently localized to the frontal lobe. Radiomic analysis from contrast-enhanced T1-weighted MRI revealed that molecular distance was positively correlated with heterogeneous texture features (e.g., GLCM entropy, NGTDM complexity; p < 0.05). Stochastic tumors were enriched for radiomic heterogeneity (e.g.), while expansive tumors displayed homogeneous imaging textures, suggesting convergent phenotypic adaptation in genomically diverse tumors. Functional annotation using neuromaps demonstrated that tumors with high molecular distance exhibited positive correlations with differentiation- and angiogenesis-related metrics (SA_axis, genePC1, CBV), and negative associations with expression of neurotransmitter receptors (acetylcholine, serotonin, dopamine). Stochastic tumors correlated with increased Magnetoencephalography (MEG) derived timescales, implicating higher neural network engagement and spatial fluctuations. These findings suggest a link between type of clonal expansion, radiomic-based structural imaging phenotype, and functional neuroanatomy in GBM, providing a multidimensional framework to interpret tumor behavior and identify clinically relevant radiogenomic signatures.

Given the changing treatment landscape in IDH-mutant gliomas, prognostic stratification is pivotal to guide postoperative treatment decisions. Overall, 457 patients with IDH-mutant glioma and [18F]fluoroethyltyrosine or [11C]methionine positron emission tomography (PET) prior to radiotherapy or systemic treatment were included in this retrospective, bicentric study. Maximum and mean tumor-to-background ratios (TBRmax/TBRmean) and PET-positive volume (PET volume) were measured according to PET RANO 1.0 criteria, and their associations with time to next intervention (TTNI) and overall survival (OS) were evaluated. In total, 251 (54.9%) patients with astrocytoma and 206 (45.1%) with oligodendroglioma were included. In patients with astrocytoma undergoing PET before resection, measurable disease was associated with shorter TTNI compared to no/non-measurable disease (median 46.0 vs. 67.9 months; p=0.004). Univariable analysis showed an association of TTNI with TBRmax (Hazard ratio [HR]: 1.44 [95%CI: 1.23-1.68]), TBRmean (HR: 1.91 [95%CI: 1.34-2.71]) and PET volume (HR: 1.16 [95%CI: 1.07-1.26] per 10 mL increase). Multivariable analysis in astrocytoma adjusting for clinical factors such as age, WHO grade, extent in magnetic resonance imaging (MRI), extent of resection, and postoperative treatment confirmed the findings for TBRmax (HR 1.48 [95%CI: 1.09-2.01]) alongside T2/FLAIR extent (HR 1.03 [95%CI: 1.02-1.05] per 1 cm2 increase of product of maximum perpendicular diameters). In univariable OS analysis in astrocytoma, an association with TBRmax (HR: 1.40 [95%CI: 1.13-1.74]), TBRmean (HR: 1.97 [95%CI: 1.21-3.22]), and PET volume (HR: 1.23 [95%CI: 1.10-1.37]) was observed. Univariable TTNI analysis in oligodendroglioma showed an association with PET volume (HR: 1.11 [95%CI: 1.05-1.19]) which remained in multivariable analysis (HR 1.18 [95%CI: 1.03-1.36]). Further analyses considering timepoint of PET showed consistent results. In this retrospective analysis, associations of quantitative PET parameters with outcome were observed after adjusting for known prognostic factors. Prospective validation in clinical trials including PET imaging is needed to establish PET-based prognostic signatures.

The prognosis of diffuse gliomas previously classified as “lower-grade” is heterogeneous and complicates clinical decisions. We aimed to investigate the molecular profile of clinical outliers to gain insight into biological drivers of long and short-term survivors. Here, patients aged ≥ 18 years and diagnosed with diffuse glioma, WHO grade II/2 or III/3 were included. Short-term survivors (STS) were defined as overall survival (OS) < 1 years, and long-term survivors (LTS) as OS > 10 years. DNA methylation profiling was performed using the Illumina EPIC 850k platform. In total, 385 patients (294 LTS, 91 STS) were included. Median overall survival was 234 months (95

Purpose To evaluate glutamate (Glu) and glutamine (Gln) concentrations in patients with glioma using 7-T MR spectroscopic imaging, identify significant differences in metabolic ratios between tumor and peritumoral regions, and assess associations of Glu and Gln with tumor-associated epilepsy and other tumor characteristics. Materials and Methods This retrospective study included data from patients with gliomas who underwent 7-T MR spectroscopic imaging in a single university hospital between September 2018 and April 2021. Median values for nine metabolic ratios were calculated within the visible tumor and peritumoral shell, and Dice similarity coefficients were used to assess the spatial overlap of elevated metabolic regions between these compartments. Statistical significance between regions of interest and between glioma attributes (eg, isocitrate dehydrogenase status) was assessed. Results Thirty-six patients (median age, 52 years [IQR, 23 years]; 22 male, 14 female) were included in the study. The Glu to total creatine (Glu/tCr) median was significantly higher in the peritumoral volume of interest (median, 1.13) compared with the tumor (median, 0.92; P = .00015) and normal-appearing white matter (NAWM; median, 0.87; P < .00011), while the Gln/tCr median was highest in the tumor (median, 0.77; peritumoral: median, 0.44; P < .00011; NAWM: median, 0.33; P < .00011). Glu to total choline was higher in the peritumoral region as well (median, 3.44; tumoral: median, 2.23; P < .00011; NAWM: median, 2.06; P < .00011). Peritumoral Dice similarity coefficients for Glu/tCr and Gln/tCr hotspots were comparable (0.51 to 0.53). Specific metabolic ratios were significantly different between isocitrate dehydrogenase mutant and wild-type gliomas (eg, tumoral Glu/total N-acetylaspartate [tNAA], P = .0054), oligodendroglioma and astrocytoma (eg, tumoral Gln/tNAA, P = .0033), and oligodendroglioma and glioblastoma (eg, tumoral Glu/tNAA, P = .0034). Conclusion The 7-T MR spectroscopic imaging revealed increased Glu and Gln metabolic ratios within the peritumoral region compared with NAWM of patients with glioma distinct from intratumoral changes. Keywords: Glioma, 7 T, MR Spectroscopic Imaging, MRSI, Infiltration, Iisocitrate Dehydrogenase, IDH Supplemental material is available for this article. © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license.

BACKGROUND:Improved prognostic stratification, including imaging-based parameters, is needed to guide treatment decisions in IDH-mutant glioma. METHODS:In this bicentric retrospective study, 457 patients with IDH-mutant glioma and [18F]fluoroethyltyrosine or [11C]methionine positron emission tomography (PET) prior to radiotherapy or systemic treatment were included. Associations of maximum and mean tumor-to-background ratios (TBRmax/TBRmean) and PET-positive volume (PET volume) with time to next intervention (TTNI) and overall survival (OS) were analyzed. RESULTS:Overall, 251 (54.9%) patients with astrocytoma and 206 (45.1%) with oligodendroglioma were included. In patients with astrocytoma who underwent PET before resection, measurable disease according to PET RANO 1.0 criteria was associated with shorter TTNI compared to no/non-measurable disease (median 46.0 vs. 67.9 months; P = .004). Univariable analysis showed an association of TTNI with TBRmax, TBRmean, and PET volume in astrocytoma and PET volume in oligodendroglioma. Multivariable analyses including age, WHO grade, extent of resection, postoperative treatment, and magnetic resonance imaging (MRI)-based tumor extent indicated an association of TTNI with TBRmax (HR 1.48 [95%CI: 1.09-2.01]) and TBRmean (HR 1.93 [95%CI: 1.14-3.27]) in astrocytoma and PET volume (HR [10 ml increase]: 1.18 [95%CI: 1.03-1.36]) in oligodendroglioma. In astrocytoma, also OS was related to TBRmax (HR: 1.40 [95%CI: 1.13-1.74]), TBRmean (HR: 1.97 [95%CI: 1.21-3.22]), and PET volume (HR: 1.23 [95%CI: 1.10-1.37]) in univariable analysis. Further analyses considering timepoint of PET showed consistent results. CONCLUSIONS:In this retrospective study, amino acid PET parameters were associated with outcome in newly diagnosed IDH-mutant glioma. Future clinical trials should include PET imaging to define imaging-based prognostic signatures.

OBJECTIVE The extent of resection (EOR) and postoperative residual tumor (RT) volume are prognostic factors in glioblastoma. Calculations of EOR and RT rely on accurate tumor segmentations. Raidionics is an open-access software that enables automatic segmentation of preoperative and early postoperative glioblastoma using pretrained deep learning models. The aim of this study was to compare the prognostic value of manually versus automatically assessed volumetric measurements in glioblastoma patients. METHODS Adult patients who underwent resection of histopathologically confirmed glioblastoma were included from 12 different hospitals in Europe and North America. Patient characteristics and survival data were collected as part of local tumor registries or were retrieved from patient medical records. The prognostic value of manually and automatically assessed EOR and RT volume was compared using Cox regression models. RESULTS Both manually and automatically assessed RT volumes were a negative prognostic factor for overall survival (manual vs automatic: HR 1.051, 95% CI1.034-1.067 [p < 0.001] vs HR 1.019, 95% CI1.007-1.030 [p = 0.001]). Both manual and automatic EOR models showed that patients with gross-total resection have significantly longer overall survival compared with those with subtotal resection (manual vs automatic: HR 1.580, 95% CI1.291-1.932 [p < 0.001] vs HR 1.395, 95% CI1.160-1.679 [p < 0.001]), but no significant prognostic difference of gross-total compared with near-total (90%-99%) resection was found. According to the Akaike information criterion and the Bayesian information criterion, all multivariable Cox regression models showed similar goodness-of-fit. CONCLUSIONS Automatically and manually measured EOR and RT volumes have comparable prognostic properties. Automatic segmentation with Raidionics can be used in future studies in patients with glioblastoma.

OBJECTIVE:Glioblastoma is an aggressive brain tumor that is more common and has a worse outcome in males. Recently, the observed sex differences have been linked to tumor biology, prominently highlighting fundamental differences in gene expression programs. Here, the authors advance this concept to epigenome-based DNA methylation patterns across primary and recurring glioblastoma. METHODS:The authors leveraged their 614 publicly available DNA methylation datasets comprising 252 female and 362 male patients with glioblastoma. They applied a joint and individual variation explained analysis to explore clusters among tumors in males and females in an unsupervised way. Their prognostic association was explored using Kaplan-Meier analysis and a Cox proportional hazards model. Their findings were validated using The Cancer Genome Atlas (TCGA) dataset. RESULTS:Clustering of the individual, sex-specific components yielded two distinct clusters in males and females, which were predictive of overall survival in males (p = 0.0098). Among differentially regulated genes in males, the 20 most consistently altered genes resulted in a targeted panel, which predicted overall survival in males and females at the first surgery (p < 0.0001 and p = 0.013) but not at recurrence (p = 0.3 and p = 0.85, respectively). These findings were validated in TCGA dataset. The authors translated the observed differences in survival to networked pathways prominently highlighting protein metabolism in males and oxidative phosphorylation in females. CONCLUSIONS:In summary, the authors report sex-specific differences in DNA methylation patterns among male and female cases of glioblastoma that converge on a set of 20 genes that have a prognostic impact in both sexes at the first surgery. Sex-specific networks of pathways suggest prominent roles for protein processing and antigen presentation in males and metabolism in females. The study findings provide new insights in sex-specific tumor biology to further improve individual gender-based patient management and estimation of disease prognosis.

Background:Accurate prognosis of glioblastoma is crucial for better-informed treatment decisions, potentially leading to improved disease management. We investigated whether clinical variables, tumor size, and location, can serve as prognostic factors. Methods:A retrospective, multicenter study enrolled 1318 adult patients with histopathologically confirmed glioblastoma undergoing first-time surgery, with survival censored for 188 patients. Pre-operative brain MRIs were used to compute tumor size and derive advanced radiological features describing tumor location, later refined by expert-based opinion. Post-operative MRIs were used to measure the enhancing residual tumor volume. The prognostic quality of all variables, measurements, and features was assessed as inputs of three survival regression models (CoxPH, Random Survival Forests, DeepSurv) to predict overall survival, under five timepoints of patient treatment: onset presentation, assessment by multidisciplinary board, intervention planning, post-intervention evaluation, and chemoradiotherapy planning. Model evaluation was performed with the C-index, Brier Score over Time, and Integrated Brier Score. Results:Multivariable Cox analysis identified most clinical variables and tumor size as strong predictors of patient survival, with varying hazard ratios across timepoints. DeepSurv was consistently the top performing model under all possible inputs and at all timepoints, yielding mean test C-index scores ranging from 61.71% to 70.29%, and mean Integrated Brier Scores ranging from 8.57% to 7.63%. Conclusion:Clinical variables, tumor size, and location carry prognostic value for the overall survival of patients with glioblastoma. The best predictive performance was observed under a Deep Survival model using all variables at the stage of chemoradiotherapy planning.

The natural compound Artemisinin and its derivative Artesunate are widely used anti-malarial drugs. Due to their cytotoxic activity, they have also been investigated as anti-cancer agents, while their precise mechanism of action and key host cell targets have remained largely elusive. Using an innovative forward genetic screening approach, we identified porphyrin biosynthesis as the critical pathway governing Artemisinin’s cytotoxicity. Genetic or pharmacological modulation of porphyrin production, e.g. by supplementation of the porphyrin precursor 5-aminolevulinic acid (5-ALA), defines Artesunate’s cytotoxicity in multiple eukaryotic cells, including human cancer cells. 5-ALA, a clinically approved photodynamic porphyrin enhancer, is used as a surgical fluorescence marker, which specifically labels tumorigenic brain areas due to differential uptake and metabolism. Combining Artesunate with 5-ALA therefore provides an additional level of selectivity towards malignant tissue. We translated the screening results into clinically relevant model systems of brain tumor development: glioblastoma models in engineered cerebral organoids, patient-derived brain tumor spheroids, and orthotopic xenograft models using freshly isolated patient cells. The 5-ALA-Artesunate combination showed a strong and selective antineoplastic activity in all three model systems. At AACR 2025 we will present a comprehensive preclinical data-package, including a new delivery system with animal pharmacokinetics and efficacy data, paving the way for the clinical application of this promising therapeutic approach. Michael Orthofer, Marianna Rozsova, Ferdinand Baumgartner, Barabara Kiesel, Georg Widhalm, Moritz Horn, Josef M. Penninger. From unbiased genetic screen to therapy: Combination of 5-ALA and artesunate for the treatment of primary glioblastoma multiforme [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 525.