Search Results (493)

Background: Imaging-transcriptomic analysis, through the integration of multimodal magnetic resonance imaging (MRI) and transcriptomic data, provides complementary structural, functional, and molecular information that is crucial for the early detection and mechanistic exploration of Alzheimer’s disease (AD). However, effectively extracting features from heterogeneous multimodal data and capturing the associations between microscopic molecular variations and macroscopic brain alterations remain key challenges. Recent advances in deep learning and multimodal integration have enhanced the ability to model nonlinear cross-modal relationships, enabling more accurate identification of imaging-transcriptomic biomarkers and subtypes. Developing robust multimodal frameworks is therefore essential for early AD detection, subtype identification, and advancing precision medicine in neurodegenerative diseases. Methods: In this study, a two-stage method of multimodal Feature Extraction based on Association Analysis and Graph Convolutional Network with Self-Attention and Self-Expression framework (MFEAA-GCNSASE) for early diagnosis of AD and effective identification of subtypes of MCI with different progression to AD is proposed. In the first stage, the MFEAA model is applied to integrate multiple association analysis methods on sMRI, PET, and transcriptomic data to identify key multimodal biomarkers for AD and mild cognitive impairment (MCI). In the second stage, the GCNSASE model enhances classification accuracy between AD and MCI patients through self-attention and self-expression layers. Additionally, unsupervised clustering was performed on MCI samples using top multimodal biomarkers to explore subtype heterogeneity and conversion risk. Reliable MCI subtypes were also identified through a consensus clustering approach. Results: The proposed algorithm integrates sMRI, PET, and transcriptomic data, identifying robust biomarkers including the Left Hippocampus, Left Angular Gyrus, and key genes such as SLC25A5 and GABARAP. To ensure statistical robustness given the extreme class imbalance, we employed a rigorous repeated stratified cross-validation (RSCV) framework. GCNSASE achieved state-of-the-art discrimination performance with mean AUC values ranging from 0.946 to 0.961 across feature subsets (10–50%), significantly outperforming MOGONET (mean AUC: 0.844–0.875, p < 0.001) and conventional machine learning models with tighter 95% confidence intervals, indicating superior stability despite the limited AD sample size. Clustering analysis revealed two distinct MCI subtypes with divergent molecular landscapes: Subtype A was enriched in energy metabolism and cellular maintenance pathways, whereas Subtype B was enriched in neuroinflammatory and aberrant signaling pathways. Notably, the majority of MCI patients who subsequently converted to AD were concentrated in the immune-inflammatory Subtype B. These findings highlight that neuroinflammation coupled with bioenergetic failure constitutes a critical mechanism driving the conversion from MCI to AD. Conclusions: The proposed methods not only provide the key multimodal biomarkers and enhance the accuracy of the classification model for early AD diagnosis but also identify biologically and clinically meaningful MCI subtypes with distinct molecular signatures and conversion risks. Exploring these associated multimodal biomarkers and MCI subtypes is of great significance, as they help elucidate the heterogeneous mechanisms underlying AD onset and progression, enable the identification of high-risk individuals likely to convert to AD, and provide a foundation for targeted therapeutic strategies and individualized clinical management. These findings have important implications for understanding disease heterogeneity, discovering potential intervention targets, and advancing precision medicine in neurodegenerative diseases. Full article

Background/Objectives: This study explored the heterogeneous distribution pattern of translocator protein 18kDa (TSPO)-PET/MRI using radioligand [¹⁸F] DPA-714 in temporal lobe epilepsy patients and identified clinical factors influencing imaging outcomes. Methods: The TSPO imaging in individual patient was evaluated with both visual reading and quantitative assessment using an asymmetry index based on cerebellum-normalized standardized uptake values. The association between clinical factors and TSPO imaging outcomes was assessed. Pathological evaluation was conducted in three patients. Results: Twenty-nine TLE patients and ten healthy controls were enrolled. Visual evaluation revealed increased [¹⁸F] DPA-714 uptake in twenty patients as compared to controls, predominantly in a unilateral regional brain, while the remaining nine patients showed visually undetectable uptake of [¹⁸F] DPA-714. Consistently, quantitative analysis revealed that 69% (20/29) patients exhibited at least one brain area with significant asymmetry index, notably in the temporal lobe (85%, 17/20). A high asymmetry index could also be observed in the parietal (13.8%, 4/29) and occipital lobe (17.2%, 5/29). Significant associations were identified between the asymmetry index and seizure frequency (p = 0.045, OR = 7.994), and the interval from last seizure to PET scan (p = 0.033, OR = 6.712). Moreover, we confirmed the pathology in three patients via immunohistochemistry, which underscored the potential of TSPO-PET in detecting minor lesion. Conclusions: TSPO-PET reveals patient-specific and network-level neuroinflammatory heterogeneity in MRI-negative TLE, supporting its potential role as a complementary tool for presurgical evaluation. Full article

Objective: To compare metastatic lesion detection on [68Ga]Ga-DOTA.SA.FAPi and [18F]FDG PET/CT in metastatic breast and lung cancers and to assess the relationship between PET-derived imaging parameters and progression-free survival (PFS). Methods: In this prospective dual-cohort study, 45 patients (23 breast cancer, 22 lung adenocarcinoma) underwent paired [68Ga]Ga-DOTA.SA.FAPi and [18F]FDG PET/CT within four weeks. Semiquantitative (SUVmax, SUVmean) and volumetric (MTV, TLG, STV, TLF) PET parameters were measured. Metastatic detection was compared, and correlations with PFS were assessed. Results: In breast cancer, [18F]FDG demonstrated higher primary tumor uptake, whereas [68Ga]Ga-DOTA.SA.FAPi showed lower background activity, resulting in higher tumor-to-background ratios for brain and bone metastases. Whole-body volumetric indices (wbTLG, wbTLF) showed strong inverse correlations with PFS. In lung adenocarcinoma, volumetric FAPi-derived parameters (wbTLF, wbSTV) demonstrated modest but significant correlations with PFS. [68Ga]Ga-DOTA.SA.FAPi PET/CT detected more brain metastases than [18F]FDG PET/CT in both cohorts (breast: 15/15 vs. 8/15; lung: 14/14 vs. 4/14). Conclusions: [68Ga]Ga-DOTA.SA.FAPi and [18F]FDG PET/CT provide complementary diagnostic and prognostic information. In metastatic breast cancer, FAPi-derived volumetric parameters strongly correlate with PFS and improve detection of brain metastases. In lung adenocarcinoma, [68Ga]Ga-DOTA.SA.FAPi PET/CT offers low background uptake and prognostically relevant stromal metrics. These findings support a potential role for integrating [68Ga]Ga-DOTA.SA.FAPi PET/CT into disease staging, prognostication, and treatment monitoring. This study did not involve prospective assignment to health-related interventions and therefore did not require clinical trial registration. Full article

BNCT (Boron Neutron Capture Therapy) is a binary radiotherapeutic modality in which high LET (Linear Energy Transfer) particles are generated from ¹⁰B(n,α)⁷Li reaction, ideally within boron-loaded tumour cells, so the therapeutic outcome depends critically on the pharmacokinetics and biodistribution of boron carriers. In this review, boron-containing agents for BNCT, with a focus on ADMET (absorption, distribution, metabolism, excretion and toxicity) and model-informed design, were examined. Low-MW (low-molecular-weight) compounds, peptide conjugates, polymeric and nanostructured platforms and cell-based vectors were surveyed and how physicochemical properties, transporter engagement and nano–bio interactions govern tumour uptake, subcellular localisation and normal tissue exposure were discussed. A shift from maximising boron content towards optimising exposure profiles using PET (Positron Emission Tomography), PBK (physiologically based pharmacokinetic) modelling and in silico ADMET tools to define irradiation windows was also discussed. Classical agents such as BPA (Boronophenylalanine) and BSH (Sodium Borocaptate) are contrasted with newer polymeric and metallacarborane-based carriers, with attention to brain penetration, endosomal escape, linker stability, biodegradation and elimination routes, as well as platform-specific toxicities. Incontestably, further progress in BNCT will highly depend on integrating imaging-derived kinetics with PBPK-informed dose planning and engineering subcellularly precise yet degradable carriers, and that ADMET-guided design and spatiotemporal coordination are central to achieving reproducible clinical benefit from BNCT’s spatial selectivity. Full article

The glymphatic system supports metabolic waste clearance during sleep and is essential for brain homeostasis. Disruption of this system has been linked to sleep disorders, yet the overall prevalence of sleep disorders in populations showing impaired glymphatic-related function remains unclear. This systematic review and meta-analysis evaluated the prevalence of sleep disorders in human cohorts with structural, functional, or biochemical imaging markers of impaired glymphatic activity. Following PRISMA guidelines, major databases were searched up to August 2025. Eligible observational studies reported sleep disorder prevalence in populations characterized by enlarged perivascular spaces, white matter hyperintensities, DTI-ALPS (DTI-ALPS: Diffusion tensor image analysis along perivascular space) alterations, ultrafast fMRI (fMRI: functional magnetic resonance) indices, or CSF/PET (CSF: cerebrospinal fluid; PET: positron emission tomography) clearance deficits. Random-effects models generated pooled estimates, and heterogeneity, publication bias, and moderators were examined using I² statistics, Egger’s test, trim-and-fill, and meta-regression. Nineteen studies (≈4500 participants) met the inclusion criteria. The pooled prevalence of sleep disorders in populations with impaired glymphatic-related function was 44.9% (95% CI: 34.9–55.3%), with substantial heterogeneity (I² ≈ 95%). Meta-regression identified older age and case–control design as significant contributors, while imaging modality, sex distribution, and sample size were not. Publication bias was minimal. Sleep disorders are common among individuals with impaired glymphatic-related markers, reflecting co-occurrence rather than causality. Standardized longitudinal studies are needed to clarify mechanisms and clinical relevance. Full article

Objectives: Glioblastoma multiforme (GBM) is considered the most aggressive primary brain tumor, which often exhibits tumor heterogeneity. Hypoxia is a key aspect of intratumoral heterogeneity that contributes to poor prognosis in GBM. In this study, we aimed to develop machine learning (ML) models using radiomics and clinical features for the prediction of one-year survival for GBM. Methods: Data from 35 patients in the ACRIN 6684 trial, including fluoromisonidazole (FMISO)-positron emission tomography (PET), magnetic resonance (MR) (T1, T2, and fluid-attenuated inversion recovery (FLAIR)) images, and clinical information, were retrieved from The Cancer Imaging Archive (TCIA). Three ML algorithms, namely, support vector machine (SVM), random forest (RF), and linear regression (LR), were utilized to analyze selected features. Receiver-operating characteristic (ROC) curves were utilized to evaluate the predictive performance of the models. Several statistical analyses, namely, the permutation test, the permutation importance of selected features, Fisher’s exact test, and the unpaired t-test, were performed to analyze the models and features. Results: FMISO achieved the best performance in radiomics models, with an area under the curve (AUC) of 0.870. The clinical data model achieved the best performance of all models, with an AUC of 0.921, outperforming the combined all sequential forward selection (SFS) model (AUC: 0.862). Female sex (p = 0.030) and younger age (p = 0.0043) were significantly associated with better prognosis. Conclusions: Our proposed models have the potential to predict the one-year survival of GBM and facilitate personalized therapy. Future studies with a larger sample size are needed to confirm the generalizability of the models. Full article

Objectives: This retrospective, multi-center study analyzed pre-existing anonymized clinical data from electronic health records and imaging archives. The analysis utilized real-world clinical data from 200 patients across four tertiary care centers, without additional patient recruitment or interventions. This study aims to investigate the impact of metabolic and physiological factors—specifically blood glucose levels, cortisol concentrations, fasting duration, and tumor histology—on the quality and diagnostic reliability of ¹⁸F-FDG PET/CT imaging in patients with primary brain tumors and inflammatory lesions. Methods: A total of 200 patients with primary brain tumors (including astrocytoma, glioblastoma, meningioma, and oligodendroglioma) were evaluated across four institutions using standardized protocols. The study examined the effects of prolonged fasting (>12 h), hyperglycemia (>150 mg/dL), and strict fasting (4–6 h) on tumor-to-background contrast and visual analog scale (DQS) scores. Results: Prolonged fasting was associated with elevated cortisol levels (correlation +0.54, p < 0.001), while hyperglycemia significantly reduced tumor SUVmax by up to 20% (r = −0.35, p = 0.012). Strict fasting and glucose control resulted in improved tumor-to-background contrast and DQS scores (r = +0.83, p < 0.001). Glioblastomas exhibited the highest SUVmax (9.1 ± 3.5), indicating aggressive metabolic activity, whereas meningiomas showed elevated cortisol levels (20.5 ± 6.8 µg/dL) linked to disruption of the hypothalamic–pituitary axis. Regression analysis confirmed that both cortisol and glucose levels independently degraded image quality (β = −0.25 and −0.18, respectively; p < 0.05). Conclusions: The findings highlight the necessity for harmonized patient preparation protocols. Recommendations are in alignment with the SNMMI Procedure Standard/EANM Practice Guideline for Brain [¹⁸F] FDG PET imaging. Full article

Background/Objectives: Central nervous system (CNS) involvement in acute myeloid leukemia (AML) is a rare but important complication linked to poor outcomes. Diagnosing it is difficult because neurological symptoms are often subtle or nonspecific, and conventional cytology and imaging have limitations. This review summarizes current evidence on the neuroradiologic features of CNS infiltration in AML and explores the growing role of artificial intelligence (AI) in enhancing detection and characterization. Methods: A thorough narrative review was conducted using PubMed, Scopus, and Embase, employing key terms related to AML, CNS involvement, MRI, CT, PET, AI, machine learning, deep learning, and radiomics. Of several thousand records, 138 relevant studies were selected and analyzed across four main areas: neuroradiologic patterns, imaging biomarkers, AI and radiomics applications, and emerging computational trends. Results: Imaging findings in AML mainly include myeloid sarcomas (isointense on T1, hyperintense on T2/FLAIR, restricted diffusion) and leptomeningeal enhancement. Secondary ischemic or hemorrhagic lesions may indicate brain leukocytosis. MRI proved more sensitive than CT, while PET/CT helped detect extramedullary disease. Recent AI and radiomics models showed high tumor classification and prognosis accuracy in similar CNS conditions, indicating significant potential for application in AML-CNS. Conclusions: Combining AI-based image analysis with multimodal neuroimaging could significantly improve diagnostic accuracy and personalized treatment for CNS involvement in AML. Progress is still challenged by the rarity of the condition and the lack of large, annotated datasets. Full article

Objective: We present the first clinical experience with the BIOGRAPH One next-generation PET/MRI system scanner, evaluating its performance for body and brain imaging in patients across multiple tracers. Methods: A total of 59 patients were scanned on the BIOGRAPH One PET/MRI following standard clinical PET/CT (n = 52) or first-generation PET/MRI (Biograph mMR, n = 7). Scans comprised 30 total body (TB), whole body (WB), or regional scans with [¹⁸F]FDG, and 29 brain scans with either [¹⁸F]FDG (n = 5), [¹⁸F]FE-PE2I (n = 10), [¹⁸F]FET (n = 4), or [⁶⁸Ga]Ga-DOTATOC (n = 10). The PET image quality was visually assessed using a 5-point Likert scale (1 = very good to 5 = very bad) and compared with clinical scans acquired on either a current-generation digital PET/CT or a first-generation PET/MRI system, including evaluation of diagnostic concordance. PET quantification and image noise was compared in brain and WB/TB [¹⁸F]FDG PET scans. Results: PET image quality was rated as good or very good in 93% of scans with a median [inter-quartile range] score of 1.5 [1.5;2]. In 99% of cases, image quality was judged equal to or better than the clinical reference scan (median score 3 [2.5;3]). Diagnostic concordance was observed in 99% of readings. Imaging metrics revealed the anticipated regional bias in brain imaging, while no significant bias was observed in body imaging. Image noise was comparable to that observed with digital PET/CT and demonstrated superiority over first-generation PET/MRI despite potential degradation related to isotope decay in BIOGRAPH One PET/MRI acquisitions scans performed at the end of the imaging workflow. Conclusions: Within the study limitations related to sequential imaging, the BIOGRAPH One PET/MRI scanner demonstrated improved PET sensitivity and workflow potential over its first-generation predecessor, which may allow for broader clinical and research applications. Full article

Lung cancer remains a major public health challenge due to high incidence and mortality. The chemokine receptor CXCR4 and its ligand CXCL12 (SDF-1) constitute a critical axis in tumor biology, influencing tumor cell proliferation, invasion, angiogenesis, and immune evasion. Aberrant CXCR4 expression is frequently observed in lung cancer and is closely associated with adverse prognosis, enhanced metastatic potential, and therapeutic resistance. Mechanistically, CXCR4 activates signaling pathways including PI3K/AKT, MAPK/ERK, JAK/STAT, and FAK/Src, promoting epithelial–mesenchymal transition, stemness, and survival. The CXCL12/CXCR4 axis also orchestrates interactions with the tumor microenvironment, facilitating chemotaxis toward CXCL12-rich niches (e.g., bone marrow and brain) and modulating anti-tumor immunity via regulatory cells. Regulation of CXCR4 occurs at transcriptional, epigenetic, and post-transcriptional levels, with modulation by hypoxia, inflammatory signals, microRNAs, and post-translational modifications. Clinically, high CXCR4 expression correlates with metastasis, poor prognosis, and reduced response to certain therapies, underscoring its potential as a prognostic biomarker and therapeutic target. Therapeutic strategies targeting CXCR4 include small-molecule antagonists (e.g., AMD3100/plerixafor; balixafortide), anti-CXCR4 antibodies, and CXCL12 decoys, as well as imaging probes for patient selection and response monitoring (e.g., 68Ga-pentixafor PET). Preclinical and early clinical studies suggest that CXCR4 blockade can impair tumor growth, limit metastatic spread, and enhance chemotherapy and immunotherapy efficacy, although hematopoietic side effects and infection risk necessitate careful therapeutic design. This review synthesizes the molecular features, regulatory networks, and translational potential of CXCR4 in lung cancer and discusses future directions for precision therapy and biomarker-guided intervention. Full article

Chronic pain is a pervasive and debilitating condition that affects millions of individuals worldwide. Unlike acute pain, which serves a protective physiological role, chronic pain persists beyond routine tissue healing and often arises without a discernible peripheral cause. Accumulating evidence indicates that chronic pain is not merely a symptom but a disorder of the central nervous system, underpinned by interacting molecular, neurochemical, and network-level alterations. Molecular neuroimaging using PET and MR spectroscopy has revealed dysregulated excitatory–inhibitory balance (glutamate/GABA), altered monoaminergic and opioidergic signaling, and neuroimmune activation (e.g., TSPO-indexed glial activation) in key pain-related regions such as the insula, anterior cingulate cortex, thalamus, and prefrontal cortex. Converging multimodal imaging—including functional MRI, diffusion MRI, and EEG/MEG—demonstrates aberrant activity and connectivity across the default mode, salience, and sensorimotor networks, alongside structural remodeling in cortical and subcortical circuits. Parallel advances in neuromodulation, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), deep brain stimulation (DBS), and emerging biomarker-guided closed-loop approaches, provide tools to perturb these maladaptive circuits and to test mechanistic hypotheses in vivo. This review integrates neuroimaging findings with molecular and systems-level mechanistic insights into chronic pain and its modulation, highlighting how imaging markers can link biochemical signatures to neural dynamics and guide precision pain management and individualized therapeutic strategies. Full article

The “Neuroimaging and Pathology Biomarkers in Parkinson’s Disease” course held on 12–13 September 2025 in Milan, Italy, convened an international faculty to review state-of-the-art biomarkers spanning neurotransmitter dysfunction, protein pathology and clinical translation. Here, we synthesize the four themed sessions and highlights convergent messages for diagnosis, stratification and trial design. The first session focused on neuroimaging markers of neurotransmitter dysfunction, highlighting how positron emission tomography (PET), single photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI) provided complementary insights into dopaminergic, noradrenergic, cholinergic and serotonergic dysfunction. The second session addressed in vivo imaging of protein pathology, presenting recent advances in PET ligands targeting α-synuclein, progress in four-repeat tau imaging for progressive supranuclear palsy and corticobasal syndromes, and the prognostic relevance of amyloid imaging in the context of mixed pathologies. Imaging of neuroinflammation captures inflammatory processes in vivo and helps study pathophysiological effects. The third session bridged pathology and disease mechanisms, covering the biology of α-synuclein and emerging therapeutic strategies, the clinical potential of seed amplification assays and skin biopsy, the impact of co-pathologies on disease expression, and the “brain-first” versus “body-first” model of pathological spread. Finally, the fourth session addressed disease progression and clinical translation, focusing on imaging predictors of phenoconversion from prodromal to clinically overt stages of synucleinopathies, concepts of neural reserve and compensation, imaging correlates of cognitive impairment, and MRI approaches for atypical parkinsonism. Biomarker-informed pharmacological, infusion-based, and surgical strategies, including network-guided and adaptive deep brain stimulation, were discussed as examples of how multimodal biomarkers may inform personalized management. Across all sessions, the need for harmonization, longitudinal validation, and pathology-confirmed outcome measures was consistently emphasized as essential for advancing biomarker qualification in multicentre research and clinical practice. Full article

Glioblastoma (GBM) remains one of the most treatment-resistant human malignancies, largely due to the interplay between disrupted fluid dynamics, immune evasion, and the structural complexity of the tumor microenvironment; in addition to these, treatment resistance is also driven by intratumoral heterogeneity, glioma stem cell persistence, hypoxia-induced metabolic and epigenetic plasticity, adaptive oncogenic signaling, and profound immunosuppression within the tumor microenvironment. Emerging evidence shows that dysfunction of the glymphatic system, mislocalization of aquaporin-4, and increased intracranial pressure compromise cerebrospinal fluid–interstitial fluid exchange and impair antigen drainage to meningeal lymphatics, thereby weakening immunosurveillance. GBM simultaneously remodels the blood–brain barrier into a heterogeneous and permeable blood–tumor barrier that restricts uniform drug penetration yet enables tumor progression. These alterations intersect with profound immunosuppression mediated by pericytes, tumor-associated macrophages, and hypoxic niches. Advances in imaging, including DCE-MRI, DTI-ALPS, CSF-tracing PET, and elastography, now allow in vivo characterization of glymphatic function and interstitial flow. Therapeutic strategies targeting the fluid-immune interface are rapidly expanding, including convection-enhanced delivery, intrathecal and intranasal approaches, focused ultrasound, nanoparticle systems, and lymphatic-modulating immunotherapies such as VEGF-C and STING agonists. Integrating barrier modulation with immunotherapy and nanomedicine holds promise for overcoming treatment resistance. Our review synthesizes the mechanistic, microenvironmental, and translational advances that position the glymphatic–immune axis as a new frontier in glioblastoma research. Full article

Brain metastases have a distinctive vascular ecosystem—shaped by sprouting angiogenesis, vessel co-option, vasculogenic mimicry, and tumor cell transdifferentiation—that governs tumor perfusion, drug exposure, and therapeutic responsiveness. These heterogeneous vascularization patterns exhibit characteristic differences in enhancement morphology, perfusion levels, and metabolic uptake on contrast-enhanced MRI, perfusion imaging, and amino acid PET, providing crucial imaging cues for identifying routes of blood supply, inferring the state of the blood–tumor barrier, and guiding individualized therapeutic strategies. Anti-VEGF therapy is primarily used to alleviate cerebral edema and radiation necrosis, yet it confers limited survival benefit, underscoring the spatiotemporal heterogeneity of the blood–tumor barrier and the persistence of non-classical vascularization pathways. Building on the concept of “vascular normalization,” combinations of anti-angiogenic therapy with immunotherapy, radiotherapy, or targeted agents have shown encouraging intracranial activity in selected settings—most robustly in melanoma brain metastases—but remain insufficiently validated in randomized, brain-metastasis-focused trials. By integrating mechanistic, imaging, and therapeutic perspectives, this review outlines how vascular-ecosystem-based stratification and physics-informed drug-delivery strategies may help transition anti-vascular therapy from symptomatic control toward mechanism-driven precision intervention. Full article

Background: Iatrogenic cerebral amyloid angiopathy (iCAA) is a rare form of CAA occurring decades after neurosurgical procedures involving cadaveric dural grafts. While typically associated with recurrent lobar intracerebral hemorrhages, recent reports suggest a possible overlap with CAA-related inflammation (CAAri). We report a case of iCAA with features indicative of active neuroinflammation that demonstrated a positive response to immunosuppressive therapy. Methods: Over a 12-year natural history, the patient underwent a comprehensive work-up, including serial clinical assessments, brain MRIs, core CSF biomarker analysis, amyloid PET imaging, and next-generation sequencing panel testing. Results: Previous clinical charts confirmed the use of cadaveric graft (Lyodura) in a neurosurgical intervention thirty years before. During hospitalization for seizures, brain MRI revealed, along with a severe form of CAA, an area of vasogenic edema. Given the suspicion of an active inflammatory process, corticosteroid and subsequent methotrexate maintenance therapy were introduced, leading to clinical and radiological improvement. Over 30 months of follow-up, the patient has remained clinically and radiologically stable, with no new hemorrhagic or inflammatory events. Conclusions: This case highlights the potential interplay between iCAA and neuroinflammation. The absence of new hemorrhages following immunosuppression suggests a possible disease-modifying effect, warranting further investigation into the role of neuroinflammation in iCAA and its therapeutic implications. Full article