Neuro-Oncology Advances

Purpose: Limited CNS bioavailability and pharmacodynamics are obstacles to effective systemic therapies for glioblastoma. One strategy to overcome these challenges is drug combinations enhancing CNS penetration and/or tumor chemosensitivity. LP-184, a synthetic acylfulvene class alkylator, induces DNA damage and inhibits glioblastoma cell viability in pre-clinical models. LP-184 is a prodrug converted to active metabolites by intracellular prostaglandin reductase 1 (PTGR1) that is over-expressed in >70% of glioblastoma. DNA damage induced by LP-184 is MGMT agnostic and reversed by transcription-dependent NER. Patients: LP-184 was evaluated in a Phase 1a study (NCT05933265) in 63 adult patients with advanced malignancies including 16 patients with recurrent glioblastoma. All patients with glioblastoma received prior standard-of-care therapy and most had received 1 or more additional therapies before enrollment. Results: Patients with glioblastoma experienced more frequent transaminitis, Grade 1-2 nausea and a trend towards more frequent and severe thrombocytopenia compared to the non-glioblastoma cohort. Otherwise, overall toxicity profiles were similar. Clinical pharmacokinetic analysis combined with published pre-clinical intra-tumoral bioavailability data (~20% penetration) predicted that LP-184 at the recommended dose for expansion (RDE) would achieve cytotoxic levels if combined with spironolactone, a BBB permeable ERCC3 degrader and TC-NER inhibitor that sensitizes glioblastoma cells to LP-184 3-6-fold. We show that three daily doses of spironolactone deplete orthotopic glioblastoma PDX ERCC3 protein by ~ 80% and increases tumor LP-184 cytotoxicity 2-fold. Conclusions: LP-184 is well tolerated at the RDE, and we establish a clinically translatable scheme for dosing spironolactone in combination with LP-184 for a future Phase 1b clinical trial.

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults. Radioresistance, partly mediated by glioma stem-like cells, represents a major clinical challenge which could be overcome by the identification of the modulators of radioresistance. Existing CRISPR screens in human GBM models have largely used two-dimensional cultures with short-term viability readouts, failing to capture the long-term clonogenic behaviour underlying tumour recurrence after radiotherapy. Method: We developed ClonoScreen3D-CRISPRi, combining CRISPRi-mediated gene knockdown with three-dimensional clonogenic survival assays. Two GBM cell lines (G7 and GBML20), differing in MGMT promoter methylation status, were engineered to express the KRAB-dCas9 editor. Nine candidate radiosensitivity modifiers, selected through transcriptomic analysis, pharmacological studies, and literature review, were examined in both lines. Target validation was performed using full radiation dose-response assays and a pharmacological inhibitor. Results: The majority of candidate genes significantly altered survival fraction following irradiation in both cell lines. Knockdown of NFKB2, RELB, and CDK9 produced the most potent radiosensitization, with sensitizer enhancement ratios of 1.39-1.70 in validation studies, exceeding those of established radiosensitizers including PARP and ATM inhibitors. Notably, knockdown of these genes induced no significant cytotoxicity in the absence of radiation. Pharmacological validation using an IKK inhibitor confirmed these findings, implicating non-canonical NF-{kappa}{beta} signalling and CDK9-dependent transcriptional elongation as critical adaptive mechanisms in GBM radioresistance. Conclusions: ClonoScreen3D-CRISPRi is a scalable, physiologically relevant platform for identifying genetic modifiers of radioresistance. The non-canonical NF-{kappa}{beta} pathway and CDK9 represent promising radiosensitizing targets, and larger screens could enable systematic prioritisation of candidates for clinical translation.

Abstract: Purpose: Recurrence in high grade glioma (HGG) predominantly occurs within the high dose radiation field, raising the question of whether treatment failure reflects limitations in radiation target delineation or is driven by intrinsic tumor biology. This study evaluated recurrence patterns following standard chemoradiotherapy and their treatment implications. Material and Methods: This retrospective single center study included 41 patients with histologically confirmed HGG treated with surgery followed by radiotherapy with concurrent and adjuvant temozolomide (TMZ). Patients were followed through August 2018; those with recurrence were included in the analysis. Recurrence patterns were classified based on their spatial relationship to the 60 Gy isodose line as central, infield, marginal, or distant. Survival outcomes were estimated using the Kaplan-Meier method and compared using the log rank test. Results: The most common pattern of recurrence was central (15 patients, 36.5%), followed by infield (11, 26.8%), distant (6, 14.6%), marginal (5, 12.1%), and multicentric (4, 9.8%). Central and in field recurrences (local failures) accounted for 26 patients (63%). Median overall survival (OS) was 27 months, and median progression-free survival (PFS) was 12 months. Survival differed significantly by recurrence pattern (log-rank p = 0.018), with marginal recurrence associated with more favorable outcomes. Conclusion: The predominance of central and infield recurrences within the high-dose region suggests that treatment failure in HGG is not solely explained by inadequate target delineation and may also be driven, in part, by intrinsic tumor biology, including radioresistant subpopulations and tumor heterogeneity. Future strategies may benefit from incorporating biologically guided approaches alongside optimization of radiation treatment parameters.

Glioblastoma (GBM) is the most prevalent primary brain cancer, with poor prognosis and limited therapeutic options available. The genetic and cellular heterogeneity characteristic of GBM contributes to poor response rates. Activating mutations of the epidermal growth factor receptor (EGFR) gene are among the most frequent alterations in GBM, occurring in roughly half of cases. Despite the prevalence of EGFR mutations, EGFR inhibition has shown limited success in GBM. The transcription factor C/EBP{beta} is a master regulator of the mesenchymal transformation in GBM, an aggressive state characterized by increased invasiveness and resistance to chemotherapy. Lucicebtide is a C/EBP{beta} antagonist peptide with demonstrated single agent activity in patients with recurrent GBM that is currently being evaluated in a clinical trial in combination with radiation and temozolomide in patients with newly-diagnosed GBM (NCT04478279), with emerging data supporting clinical activity in that setting. Here we show that in the TCGA-GBM dataset, patients with EGFR mutations display significant enrichment of a high C/EBP{beta} activity signature. Functionally, genetic inactivation of EGFR by CRISPR results in synthetic lethality in the presence of lucicebtide in GBM cell lines, and synergistic in vitro cytotoxicity and suppression of C/EBP{beta} target gene expression was observed in combination experiments with lucicebtide and EGFR inhibitors. Finally, enhanced anti-tumor activity was demonstrated in vivo in the combination setting, as combined subpharmacologic dose levels of lucicebtide and the EGFR inhibitor osimertinib potently suppressed GBM xenograft growth. These data identify EGFR and C/EBP{beta} dependencies in GBM and support lucicebtide combination with EGFR inhibitors as a potential therapeutic option for a sizable fraction of GBM patients.

BackgroundGangliogliomas (GGs) are low-grade glioneuronal tumors that frequently present with drug-resistant epilepsy. Although their indolent course contrasts with their high epileptogenic potential, the oncogenic mechanisms sustaining neuronal precursor-like populations within the tumor microenvironment remain poorly defined. MethodsWe performed spatial transcriptomic profiling on eight histologically confirmed GGs and matched healthy cortex to map the cellular and molecular architecture of the tumor microenvironment. Integrated analysis with weighted gene correlation network analysis (WGCNA) defined recurrent oncogenic programs and spatially resolved tumor-stroma interactions. ResultsEight conserved gene modules emerged, encompassing physiological cortical, reactive glial, and oncopathological programs. The latter captured extracellular matrix (ECM) remodeling, vascular-immune signaling, and persistence of immature, proliferative neuronal-like states. Spatial modeling revealed that these oncopathological programs form structured niches at the tumor-brain interface, where radial glia-derived neuronal-like tumor cells coexist with immune and stromal elements engaged in ECM turnover and cytokine signaling. ConclusionsGanglioglioma represents a hybrid glioneuronal neoplasm in which developmental neuronal programs are co-opted by tumor-associated stromal and immune cues. This convergence establishes a permissive oncogenic niche that sustains precursor-like tumor cells and provides a mechanistic basis for both the tumors benign growth and its intrinsic epileptogenicity. Key PointsO_LISpatial transcriptomics identifies reproducible transcriptional programs that define the ganglioglioma microenvironment. C_LIO_LITumor-associated regions show transcriptional programs consistent with immature neuronal states together with ECM remodelling and immune activity. C_LIO_LISingle-cell reference data indicate that immature neuronal programs in ganglioglioma resemble radial glia-derived developmental states. C_LI Importance of the StudyGanglioglioma is a low-grade glioneuronal tumor that combines benign growth with pronounced epileptogenicity, yet the molecular basis of this dual behavior remains poorly understood. Through spatial transcriptomics integrated with single-cell analysis, we reveal that ganglioglioma architecture is defined by two interacting transcriptional axes: a residual glioneuronal network and a tumoral niche enriched for extracellular-matrix, vascular, and immune programs. Within these niches, immature neuronal-like tumor cells persist in a developmentally arrested state maintained by ECM-immune signaling. This spatially organized interplay between physiological and pathological programs explains both the low oncologic aggressiveness and high excitability of these lesions. Our findings provide molecular signatures that may refine diagnostic classification within the LEAT spectrum, delineate epileptogenic zones, and identify candidate pathways for therapeutic modulation of the ganglioglioma microenvironment.

Focal cortical dysplasias (FCDs) are one of the most common structural causes of drug-resistant epilepsy in children but are frequently subtle and difficult to detect on conventional MRI. Many automated lesion detection methods have therefore been proposed to support neuroradiological assessment. In this study, we externally validated two recently developed deep-learning approaches for FCD detection, MELD Graph and 3D-nnUNet, in a pediatric cohort. In this retrospective single-center study, brain MRI scans of 71 children evaluated for epilepsy were analyzed, including 35 MRI-positive patients with suspected FCD and 36 MRI-negative cases based on the primary radiology reports. Both models were applied to standard 3D T1-weighted and 3D FLAIR images. Detected lesions were reviewed by an experienced pediatric neuroradiologist and classified as true positive, false positive, or false negative. Clinical semiology and EEG findings were additionally evaluated for cases with false-positive detections. At the lesion level, MELD Graph achieved a precision of 0.85 and recall of 0.52, while 3D-nnUNet achieved a precision of 0.91 and recall of 0.48. In the MRI-negative patients, MELD Graph produced more false-positive detections than 3D-nnUNet (0.53 vs. 0.14 false-positive lesions per patient). At the patient level, MELD Graph showed slightly higher sensitivity than 3D-nnUNet (0.63 vs. 0.54), whereas 3D-nnUNet demonstrated markedly higher specificity (0.86 vs. 0.56). Improved FLAIR image quality was associated with trends toward improved model performance. Both models demonstrated high precision but moderate sensitivity, indicating that they are valuable decision-support tools but cannot replace expert neuroradiological evaluation. Optimized MRI acquisition protocols are needed to further improve automated lesion detection in pediatric epilepsy.

Background: Medical imaging, especially computed tomography and magnetic resonance imaging, is essential in clinical care of patients with renal cell carcinoma (RCC). Artificial intelligence (AI) research into computer-aided diagnosis, staging and treatment planning needs curated and annotated datasets. Across literature, The Cancer Genome Atlas (TCGA) datasets are widely used for model training and validation. However, re-annotation is often necessary due to limited access to public annotations, raising entry barriers and hindering comparison with prior work. Methods: We screened 1915 CT scans from three TCGA-RCC databases and employed a segmentation model to annotate kidney lesion. After a meta-data-based exclusion step, we hosted a reader study with all papillary (n=56), chromophobe (n=27) and 200 randomly selected clear cell RCC cases. Two students quality checked and corrected the data as well as annotated tumors and cysts. Uncertain cases were checked by a board-certified radiologist. Results: After data exclusion and quality control a total of 142 annotated CT scans from 101 patients (26 female, 75 male, mean age 56 years) remained. This includes 95 CTs with clear cell RCC, 29 with papillary RCC and 18 with chromophobe RCC. Images and voxel-level annotations of kidneys and lesions are open sourced at https://zenodo.org/records/19630298. Conclusion: By making the annotations open-source, we encourage accessible and reproducible AI research for renal cell carcinoma. We invite other researchers who have previously annotated any of these cohorts to share their annotations.

Background: Classification of central nervous system (CNS) tumors has become increasingly complex, raising concerns about the sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to replace multiple diagnostic assays. Methods: We performed nWGS on DNA extracted from 90 adult CNS tumor samples (58 retrospective, 32 prospective) and compared the results to findings from standard of care (SoC) diagnostic work-up. Analysis was done through an automated workflow that consolidated diagnostically and therapeutically relevant genomic alterations, including copy-number variation, structural, and single-nucleotide variants, chromosomal aberrations, gene fusions, and methylation-based classification. Results: nWGS supported final diagnostic classification in all samples with >15% tumor cell content, requiring ~3 hours of hands-on library preparation, parallel sample processing, and sequencing times within 72 hours. Methylation-based classification was available within 1 hour and was concordant with the integrated final diagnosis in 89% of cases (80/90). All diagnostically relevant copy-number variations, single-nucleotide variants, and gene fusions were concordant with SoC testing. MGMT promoter methylation status matched in 94% of cases. In addition, nWGS identified prognostic and potentially actionable variants that were not reported or covered by SoC. Conclusions: nWGS delivers comprehensive genetic and epigenetic results with a fast turn-around compared to standard methods. This enables efficient, accurate, and scalable molecular diagnostics of CNS tumors using a single platform. This data supports its implementation in routine clinical practice and may be extended to other cancer types requiring complex genomic profiling.

BackgroundAcute basilar artery occlusion (BAO) causes devastating strokes. Despite the benefit of endovascular treatment, the optimal management remains sometimes controversial, such as for patients with mild deficits, and would benefit from robust prognostic tools. Given the dense white matter networks within the posterior fossa, we tested whether quantifying disconnections from acute diffusion-weighted imaging (DWI) could improve outcome prediction and responders to recanalization compared with conventional metrics. MethodsWe conducted a secondary analysis from a prospective multicenter stroke registry, including consecutive patients (2017-2024) with BAO and admission MRI. Ultra-high-resolution diffusion MRI was acquired in healthy participants to build normative tractograms with optimized posterior fossa quality. Patient infarcts delineated on DWI were projected onto these tractograms to estimate disconnected fiber volume. The primary outcome was 90-day modified Rankin Scale (mRS) 0-3 vs 4-6. Predictive performance of disconnected fiber volume was compared with baseline NIHSS, infarct volume, and posterior circulation ASPECTS (pc-ASPECTS) using logistic regressions and areas under receiver operating characteristic curves (AUC). Ordinal regressions tested associations across the full mRS spectrum, stratified by recanalization status. Analyses were repeated in patients with NIHSS [&le;]10. ResultsAmong 201 patients (median age 70; NIHSS 10), 97 (48.3%) had poor outcome. Despite small median infarct volume (4.75 mL), disconnected fiber volume was substantial (median 25.15 mL). Disconnected fiber volume achieved an AUC of 0.84, outperforming NIHSS (0.67; p<0.0001), infarct volume (0.75; p=0.00059), and pc-ASPECTS (0.76; p=0.0127). Low disconnected fiber volume predicted better outcomes across the full mRS (OR=0.12 [95% CI, 0.065-0.204]) and greater benefit from successful recanalization (OR=0.33 [95% CI, 0.15-0.70]). In patients with NIHSS [&le;]10 (n=102), disconnected fiber volume remained the strongest predictor (AUC=0.83). ConclusionsDisconnected fiber volume derived indirectly is a robust prognostic marker of BAO outcomes that outperforms conventional predictors and may support future treatment decisions. Registrationhttps://clinicaltrials.gov - NCT03776877.

This research demonstrates that the trans-aqueduct approach is a feasible, minimally invasive access pathway to the third ventricle, offering a potential route to the deep brain for therapeutic technologies. Further pre-clinical investigation is required to thoroughly evaluate physiological tolerance, trauma risk, and the long-term implications of intraventricular implantation. The third ventricle is a high-value site for neuromodulation due to its proximity to deep-brain targets, including the subthalamic nucleus (STN) and globus pallidus internus (GPi). This study defined the anatomical pathway; and evaluated the technical feasibility of retrograde access to the third ventricle via the cerebral aqueduct using minimally invasive interventional techniques. Evaluation was conducted in three phases using human MRI datasets (n=16; mean age 48.4 years) and cadaveric specimens (n=6; mean age 88.2 years). Phase 1 involved morphometric MRI analysis of the aqueduct and ventricles. Phase 2 tested trans-aqueduct access on cadaver specimens via fluoroscopically guided guidewires and catheters. Phase 3 utilized direct anatomical dissections on cadaver specimens (n=3) to morphometrically measure the third ventricular cavity and its relationship to deep-brain nuclei. Measurements across the sample groups showed a mean aqueduct diameter of 1.6 mm (SD=0.14). Third ventricle dimensions averaged 27.6 mm (ventral-dorsal), 19.9 mm (caudal-cranial), and 5.7 mm (lateral). Successful access to the third ventricle was achieved in 83% (5/6) of cadaveric specimens. The optimal technical configuration utilized a 0.018'' angled-tip guidewire and 5-6 Fr catheters; the aqueduct accommodated diameters up to 2.0 mm with minimal resistance. The STN and GPi were localized within 5-20 mm of the ventricular volumetric centroid. The trans-aqueduct approach is a technically feasible, minimally invasive pathway for accessing the third ventricle. This route offers a potential alternative for the delivery of therapeutic neurotechnologies. Further research is required to assess physiological tolerance, trauma risk, and the long-term safety of intraventricular implantation.

Conflicting evidence on scatter correction (SC) methods plagues quantitative myocardial perfusion SPECT (MPI), hindering standardized clinical protocols. This simulation study, utilizing the SIMIND Monte Carlo program and a highly realistic 4D XCAT phantom, systematically evaluates Dual Energy Window (DEW, with k=0.5) and Triple Energy Window (TEW) SC techniques. We uniquely investigate their performance across various photopeak window widths (2, 4, and 6 keV) and novel overlapped/non overlapped configurations specifically for Tc 99m MPI parameters largely unexplored in realistic cardiac models. Images were reconstructed with OSEM under uncorrected (UC), SC, and combined attenuation and scatter corrected (ACSC) conditions. Quantitative analysis focused on signal to noise ratio (SNR), contrast to noise ratio (CNR), defect contrast, and relative noise to background (RNB). Our findings consistently show ACSC's superior performance in CNR, SNR, and defect contrast, confirming its critical role. Interestingly, SC alone reduced noise but compromised defect contrast relative to UC, highlighting a potential trade-off without attenuation correction. Crucially, this study reveals minimal influence of photopeak window width and overlap configuration on image quality, and no significant difference between DEW and TEW across most metrics. These results provide essential evidence for optimizing quantitative MPI protocols, suggesting that for Tc 99m, the choice between DEW and TEW, and specific window settings, may be less critical than ensuring robust attenuation correction.

Objectives: Academic medical institutions are the gatekeepers of the physician workforce and shape the future of medicine by regulating medical school admissions as well as residency training. Although broadly the field of medicine is seeing more representation from traditionally underrepresented groups, the critical decision-making platform of academic medicine continues to be uncharacteristically homogeneous, represented mainly by white males. This is even more pronounced in surgical subspecialties, such as academic neurosurgery. This study aims to quantify this phenomenon, uncover its driving factors, and define opportunities for improvement. Methods: Using a mixed research methodology, academic neurosurgical faculty in the U.S were identified, and their demographic data was collected. An internet search using Google Scholar and Scopus was conducted to determine scholarly activity using number of publications and h-index. Results: We found a significant increase in female faculty in academic neurosurgery within the last decade. Comparing the faculty rank amongst male and female faculty, we found that the majority of female faculty are at the assistant professor level (n=36/79; 45.6%) while male faculty are more at the full professor rank (n=265/582; 45.5%). A similar trend was seen for under-represented minority neurosurgery faculty. Strong scholarly activity corelated with a departmental chair position for male faculty, however, this trend was not true for female faculty. There was a significant difference in the number of publications and h-index in female vs male faculty, but only when including male faculty outliers at the full professor level. Conclusion: Slowly but steadily, academic neurosurgery is making progress towards a more diverse and representative workforce in the U.S that better reflects the patient population. Facilitating timely progression of females and URM neurosurgeons into senior professorship and academic leadership roles will further advance this essential progress.

PURPOSE: Bispecific antibodies (BsAbs) represent a major advancement in the management of relapsed/refractory multiple myeloma (RRMM), offering high response rates even in heavily pretreated patients. However, their use presents operational, safety, and supportive care complexities that require coordinated care teams, and evolving infrastructure. This manuscript summarizes best practice recommendations for adverse event (AE) management, outpatient operational models, referral pathways, and emerging strategies to optimize long-term tolerability. METHODS: Medlive, A PlatformQ Health Brand, conducted qualitative interviews of academic and community-based clinicians. Discussions focused on BsAb implementation, patient selection and counseling, and AE management. Experts provided recommendations on team-based protocols, transitions of care, and inpatient versus outpatient considerations. RESULTS: Ten hematologists/oncologists (academic n=4; community n=6) described practice patterns, barriers, and perspectives on BsAb use. BsAbs were consistently regarded as highly effective across multiple lines of therapy, particularly for patients without alternatives. Cytokine release syndrome (CRS) was the most common acute toxicity, generally low grade and managed effectively with early tocilizumab, including prophylactic use in outpatient settings. Immune effector cell-associated neurotoxicity syndrome (ICANS) was rare, mild, and best mitigated through early recognition and caregiver support. Infections, largely from BCMA-associated hypogammaglobulinemia, frequently interrupted therapy, necessitating antiviral prophylaxis, pneumocystis jirovecii pneumonia (PJP) prophylaxis, and intravenous immunoglobulin (IVIG). Outpatient step-up dosing is expanding, supported by prophylactic strategies and academic-community collaboration. Timely referral was emphasized to preserving eligibility. Major outpatient challenges included sequencing, infrastructure readiness, and standardized caregiver and staff education. CONCLUSION: Effective community implementation of BsAbs requires multidisciplinary coordination, standardized AE protocols, infection prevention, and infrastructure to support monitoring, referrals, and equitable access. These measures are critical to ensure safe, sustainable integration of bispecific therapies and to optimize patient outcomes.

Adenoid cystic carcinoma (ACC) of salivary gland is a "immune-cold" tumour. Annexin A3 (ANXA3) is an apoptotic protein found to be participating in immune cell infiltration in tumour microenvironment (TME) of various cancer cases. Significant low expressions of ANXA3 protein found in adenoid cystic carcinoma. We hypothesized overexpressing ANXA3 transforms ACC "cold" TME to "hot". We cultured UM-HACC-2A and UFH2 spheroids on extracellular matrix and co cultured them with peripheral blood mononuclear cells. We functionalized FDA (The Food and Drug Administration) approved Poly(lactic-co-glycolic acid) PLGA nanoparticles with anti-cMyb antibody and ANXA3 recombinant protein using streptavidin-biotin conjugation. Upon overexpressing ANXA3 in ACC spheroids in immune coculture model using functionalized nanoparticles, significant increase of tumour infiltrating lymphocytes and decrease in the size of the ACC spheroids observed. Apoptotic profiler assay further confirmed significant upregulation of apoptotic proteins, some of them participate in immune infiltration. Overall, this project exhibits promising results showing potential approach to convert ACC into an immune "hot" tumour.

Neurological, neurodegenerative, and psychiatric disorders impose substantial morbidity and disability worldwide, yet their molecular basis remains incompletely understood, in part due to limited access to human brain tissue. The Danish Brain Collection, comprising brains from individuals who lived in Danish psychiatric institutions from the 1940s to the 1980s, represents a unique but largely untapped resource for retrospective molecular investigation. Here, we assess the feasibility of extracting and sequencing DNA and RNA from decades-old FFPE brain tissue. We systematically evaluate how extraction and library preparation strategies influence nucleic acid yield and quality, and show that RNA end-repair prior to library preparation substantially enhances transcript diversity, improving data quality from highly degraded samples. Despite extensive fragmentation, we recover biologically informative transcriptomic profiles, including protein-coding and microRNA expression profiles that retain clear tissue specificity. These results establish the Danish Brain Collection as a viable resource for genomic and transcriptomic analyses and demonstrate the broader potential of archival FFPE tissues for large-scale molecular studies.

Optimizing radiotherapy dose distributions remain a resource-intensive bottleneck. Existing AI-based dose prediction methods often have limited generalizability because they rely on small, heterogeneous datasets. We present nnDoseNetv2, an auto-configured, end-to-end framework for dose prediction across diverse disease sites (head and neck, prostate, breast, and lung), prescription levels (1.5-84 Gy), and treatment modalities (IMRT, VMAT, and 3D-CRT). By integrating machine-specific beam geometry with 3D structural information, the framework is designed to generalize across varied clinical scenarios. A single multi-site model was trained on 1,000 clinical plans. On sites seen during training, performance was comparable to specialized site-specific models. On unseen sites (liver and whole brain), the model outperformed site-specific models, with mean absolute errors of 2.46% and 6.97% of prescription, respectively. These results suggest that geometric awareness can bridge disparate anatomical domains while eliminating the need for site-specific model maintenance, providing a scalable and high-fidelity approach for personalized radiotherapy planning.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy but can cause serious immune-related adverse events (irAEs), with pneumonitis (ICI-P) being among the most severe. Early identification of high-risk patients before ICI initiation is critical for closer monitoring, timely intervention, and improved outcomes. Purpose: To develop and validate a deep learning foundation model to predict ICI-P from baseline CT scans in patients with lung cancer. Methods: We designed the Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR (CIPHER), a deep learning foundation model that combines contrastive learning with a transformer-based masked autoencoder to predict ICI-P from baseline CT scans in patients with lung cancer. Using self-supervised learning, CIPHER was pre-trained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients to capture heterogeneous lung parenchymal patterns. After pre-training, the model was fine-tuned on an internal NSCLC cohort for ICI-P risk prediction, using images from 254 patients for model development and 93 patients for internal validation. We compared CIPHER with classical radiomic models and further evaluated it on an external NSCLC cohort of 116 patients. Results: In the internal immunotherapy cohort, CIPHER consistently distinguished patients at elevated risk of ICI-P from those without the event, with AUCs ranging from 0.77 to 0.85. In head-to-head benchmarking, CIPHER achieved an AUC of 0.83, outperforming the radiomic models. In the external validation cohort, CIPHER maintained strong performance (AUC = 0.83; balanced accuracy = 81.7%), exceeding the radiomic models (DeLong p = 0.0318) and demonstrating higher specificity without sacrificing sensitivity. By contrast, the radiomic model showed high sensitivity (85.0%) but markedly lower specificity (45.8%). Confusion matrix analysis confirmed the robust classification performance of CIPHER, correctly identifying 80 of 96 non-ICI-P cases and 16 of 20 ICI-P cases. Conclusions: We developed and externally validated CIPHER for predicting future risk of ICI-P from pre-treatment CT scans. With prospective validation, CIPHER may be incorporated into routine patient management to improve outcomes.

1.Quantitative neuropathology has advanced through whole-slide imaging and digital histology platforms. Yet, these measurements rarely align with neuroimaging coordinate frameworks that may be useful for spatial modeling and other applications. QNPtoVox, short for quantitative neuropathology to voxels, is a reproducible, modular pipeline that transforms quantitative metrics generated by digital pathology software (HALO) into voxel-based maps registered to a standard common coordinate (MNI) template. The workflow integrates digital histopathology, gross tissue photography, ex-vivo MRI, and nonlinear registration to generate spatially standardized 3D pathology representations. This Methods article provides a complete procedural description, including required materials, step-wise instructions, operator-dependent checkpoints, expected outputs, reproducibility evaluation, and troubleshooting. QNPtoVox enables voxel-level integration of neuropathology with neuroimaging tools, unlocking existing histopathology datasets for computational modeling and cross-cohort harmonization.

BackgroundTransposable elements (TEs) account for over half of the human genome and are often derepressed in cancer. TEs can add cryptic splice sites, undergo exonization, and generate gene-TE fusion transcripts, but the combined effects of TEs on RNA processing and translation in glioblastoma stem cells (GSCs) remains incompletely elucidated. ResultsWe combined long-read RNA sequencing with polysome profiling in four patient-derived GSCs and two neural stem cell (NSC) controls to resolve TE-associated transcript diversity and its relationship to ribosomal engagement. Across GSCs, we identified 13,421 alternative splicing (AS) events, 3,077 of which contained TEs within 150 bp of splice junctions. AS sites proximal to TEs were associated with increased isoform switching compared to non-TE-associated AS sites (odds ratio 2.9 - 4.3). Moreover, AS isoforms generated from TE-proximal sites were more likely to exhibit altered ribosomal association (odds ratio 2.54). Directional shifts were observed, with shorter isoforms associating with monosome fractions and longer isoforms with polysome fractions. To enable systematic detection of gene - TE chimeric transcripts, we developed FuTER (Fusion TE Reporter), a long-read-based framework for identifying TE-associated fusions. Application to GSC datasets identified 78 GSC enriched fusion transcripts, several supported by breakpoint-spanning reads in polysome fractions, consistent with ribosome association. ConclusionsOur data suggest that TEs correlate with abnormal splicing activity and altered ribosome engagement in glioblastoma stem cells. By integrating long-read sequencing with polysome profiling and fusion detection, we establish a framework for analysis of TE-induced transcript diversity and its effects on cancer evolution and plasticity.

Background: Even for experienced operators, endovascular treatment of unruptured intracranial aneurysms involves intraoperative uncertainty that may lead to adjustments in strategy, prolong the procedure, and potentially cause inefficiency and device waste. This study aimed to evaluate whether pre-procedural testing (PPT) of endovascular treatment using patient-specific models was associated with increased operator confidence and perceived clinical utility, including improvements in procedural efficiency and reduced resource waste. Methods: We enrolled a cohort of patients who underwent PPT before endovascular treatment for complex unruptured intracranial aneurysms and compared their outcomes with a control group treated without PPT. The primary outcome was the Training Fidelity Score, a composite of three operator-reported Likert items defined a priori. Secondary outcomes included perceived clinical utility, intraoperative strategy changes, procedural time, radiation exposure, device waste and safety. Results: A total of 85 patients met the inclusion criteria (PPT=40; control=45). The Training Fidelity Score was high across the PPT group (median, 4.33/5). Perceived clinical utility was high and further increased significantly after the procedure. A significant reduction was observed in intraoperative strategy changes, with no changes recorded in the PPT group, compared to 6/45 in the control group (RR 0.09; p=0.027). Reductions in treatment time, radiation exposure and device waste were also noted. Conclusion: PPT using patient-specific models was associated with increased operator confidence, fewer intraoperative strategy changes, improved procedural efficiency, and reduced device waste without compromising safety. These findings support its use in pre-interventional preparation, but require prospective multicenter validation.