Search Results (785)

Background/Objectives: MRI-guided neurovascular interventions could benefit from lower-field systems due to reduced magnetic and radiofrequency hazards. However, safety and practical visibility of commonly used neurointerventional devices at 0.55 T remain insufficiently characterized. We evaluated magnetic field interactions, RF-induced heating, and qualitative device visibility in 11 commercially available and commonly used neurovascular devices on a 0.55 T MRI system. Methods: Eleven devices, including stent retrievers, guidewires, catheters, and one embolization implant, were tested at 0.55 T. Magnetostatic interactions were quantified using the American Society for Testing and Materials (ASTM)-guided deflection methods for translational force (ASTM-F2052) and a two-string suspension apparatus for torque (adapted from Stoianovici et al.). RF-induced heating was measured in an in vitro perfused cerebral vessel phantom using a 15 min high-specific absorption rate spin echo sequence under static and flow conditions. Qualitative device visibility was assessed using a turbo spin echo (TSE) and balanced steady-state free precession (bSSFP) imaging on each device individually. Results: Eight of eleven devices passed the translational force test, while three devices (D, E, and G), containing significant ferromagnetic components, failed with deflection angles > 45°. Eight devices passed torque testing, remaining below the critical threshold in all rotation positions; three devices (D, G, and J) failed by exceeding the 54° criterion, including one guidewire and two devices with braided/coiled metallic structures. Under static conditions, RF-induced heating ranged from negligible to 10.4 °C (maximum in device D) and generally decreased under flow; in the flow configuration, temperature rise remained below 2 °C for 6/11 devices. Qualitative imaging performance differed by sequence, with bSSFP enabling improved delineation of device structure (best for devices A, C, and H), whereas devices D, E, F, and J produced extensive signal voids that precluded reliable visualization in both sequences. Overall, three devices satisfied all safety criteria while remaining clearly visible under MRI. Conclusions: Devices that pass safety thresholds at 0.55 T can serve as candidates for further sequence optimization and preclinical workflow development, enabling the design of low-SAR, device-compatible imaging protocols tailored for neurointerventional workflows. These results provide key safety data supporting the feasibility of MR-guided neurovascular procedures at 0.55 T. Full article

Introduction and Aim: Assessment of antitumor activity in preclinical models remains challenging when relying solely on conventional size-based imaging, particularly for complex agents such as cannabinoids, whose biological effects may not translate into early volumetric tumor changes. Cannabinoid formulations, including the synthetic cannabinoid JWH-182, Cannabixir^® Medium dried flowers, and Cannabixir^® THC full extract, exhibit diverse and potentially subtle effects on tumor biology. Radiomics enables high-throughput extraction of quantitative imaging features that capture intratumoral heterogeneity beyond gross tumor volume. The primary aim of this study was to evaluate the utility of MRI-based radiomics as a sensitive tool for detecting cannabinoid-associated tumor phenotypic modulation in a preclinical breast cancer model. Methods: Orthotopic breast tumors were induced in mice using the 4T1 cell line. Animals received cannabinoid formulations in combination with chemotherapy according to a predefined protocol. Tumor burden was assessed at baseline and post-treatment using ultrasonography and whole-body MRI to calculate tumor doubling time. T1- and T2-weighted MRI datasets were segmented and analyzed using radiomics to extract morphometric and signal-based features. Results: Conventional imaging revealed no significant differences in tumor doubling time between most cannabinoid-treated groups and controls, except for accelerated growth in animals treated with Cannabixir^® THC full extract. In contrast, radiomics identified distinct, compound-specific tumor phenotypes, including structural features consistent with reduced aggressiveness, in JWH-182-treated tumors, despite similar volumetric growth patterns. Conclusion: MRI-based radiomics sensitively captures cannabinoid-associated tumor phenotype alterations beyond volumetric assessment, supporting its value as a pharmaco-imaging tool for characterizing treatment-related tumor biology in preclinical oncology. Full article

Radiogenomics examines associations between imaging phenotypes and underlying biological characteristics across cancer types. This structured narrative review focuses on oropharyngeal squamous cell carcinoma (OPSCC) and evaluates how genomic programs characteristic of HPV-positive and HPV-negative tumors have been investigated across computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) as variations in heterogeneity, diffusion patterns, perfusion and metabolic activity. A structured literature search was conducted in PubMed/MEDLINE, Scopus and Web of Science to identify studies on radiomics and radiogenomics in OPSCC and related head and neck cancers. After screening and eligibility assessment, 81 studies were included in the narrative synthesis. The reviewed literature indicates that imaging-derived features have been associated with HPV status, hypoxia-related signatures, extranodal extension and treatment outcomes. However, the current evidence base remains heterogeneous and is largely composed of retrospective, single-institution studies with relatively small cohorts. Methodological challenges, including variability in imaging acquisition, segmentation and feature harmonization, limit reproducibility and generalizability. Although cone-beam computed tomography (CBCT) is not used for primary OPSCC staging and no CBCT-based radiogenomic studies in OPSCC have been reported, existing radiomics research in dentomaxillofacial imaging suggests its potential as a hypothesis-generating modality for future investigation. Overall, current evidence supports the biological plausibility of radiogenomic imaging signatures in OPSCC, while emphasizing the need for larger multicenter datasets, standardized imaging protocols and prospective validation before clinical implementation. Full article

Background: Breast cancer brain metastases (BCBM) lack effective treatments, contributing to breast cancer-related morbidity and mortality. Integrating translational animal models and advanced non-invasive imaging can accelerate the development of urgently needed therapies. Method: In this study, we developed an intracarotid method mimicking BCBM and compared it to the stereotactic model in terms of animal welfare, tumour establishment, and blood–brain barrier (BBB) permeability. BCBM was established through intracarotid or stereotactic inoculation of BT474 and MDA-MB-231.Luc2 cells in NMRI nude mice. We utilised magnetic resonance imaging (MRI) and bioluminescence imaging (BLI) to monitor tumour growth and BBB permeability, supported by fluorescent immunohistochemistry for validation. Finally, light sheet microscopy (LSM) was employed to visualise tumour establishment in intact brains. Results: Both inoculation methods achieved a survival rate > 70%, with animals recovering within a week post-surgery. MRI and BLI effectively visualised tumour growth with stereotactic implantation, resulting in single tumours, while intracarotid inoculation led to micro-seeding of up to seven tumours in one brain. Tumour growth was rapid and homogenous in the stereotactic model, whereas the intracarotid model exhibited slower, heterogenous growth. Notably, BBB permeability was significantly higher in small tumours in the stereotactic model when compared to the intracarotid model (p = 0.003). Ex vivo analyses validated these findings with the identification of multiple metastasis in the intracarotid model and single tumours in the stereotactic model. Conclusions: We developed an animal model that closely mimics BCBM, highlighting extravasation and micro-seeding while maintaining animal welfare. Our established imaging protocols enable longitudinal evaluations of BBB permeability and treatment response, creating a translational platform for testing novel anti-cancer therapies. Full article

Magnetic Resonance Imaging (MRI) plays a pivotal role in evaluating treatment response following radiation-based therapies for hepatocellular carcinoma (HCC). As radiation modalities such as stereotactic body radiotherapy (SBRT) and transarterial radioembolization (TARE) gain prominence, understanding the underlying mechanisms of radiation-induced cellular senescence is essential for accurate interpretation of imaging. The physiological changes of radiation treatment manifest as altered diffusion characteristics and delayed regression of enhancement and tumor volumes on MRI, challenging conventional response criteria. Herein, functional and temporal imaging biomarkers are necessary. However, current imaging strategies lack standardization and robust validation, underscoring the need for prospective studies to correlate MRI findings with treatment outcomes. This review synthesizes emerging evidence on MRI-based evaluation of radiation-treated HCC, explores the physiological rationale linking senescence to imaging phenotypes, and advocates for optimized imaging protocols and criteria to enhance post-treatment surveillance and therapeutic decision-making. Full article

Background and Objectives: Brainstem infarctions remain challenging to identify due to their small size, complex anatomy, and known limitations of conventional axial diffusion-weighted imaging (DWI), particularly in the posterior fossa. Thin-section coronal DWI may improve lesion conspicuity by providing higher spatial resolution and an orthogonal imaging perspective. To evaluate whether 3 mm thin-section coronal DWI improves lesion visualization and delineation compared with standard 4 mm axial DWI in patients with MRI-confirmed acute brainstem infarction. Materials and Methods: In this retrospective single-center study, 125 consecutive patients with isolated brainstem infarction confirmed by MRI (January 2021–January 2024) were included. All patients underwent both axial and coronal DWI acquisitions. Lesions were classified by anatomical location and by the imaging plane providing better visualization (“coronal better” vs. “equal”). Lesion volumes were calculated using manual segmentation. Image interpretation was performed independently by two neuroradiologists. Interobserver agreement was assessed using Cohen’s kappa and intraclass correlation coefficient (ICC). Statistical analysis included both parametric and nonparametric tests, with confidence intervals reported. Results: Coronal DWI provided improved or equivalent lesion visualization in all cases. Improved visualization was most frequent in midbrain infarctions (100%) and in a subset of medullary lesions (26.7%). Lesions better visualized on coronal DWI were significantly smaller than those equally visualized (mean volume ~0.23 mL vs. ~0.55 mL, p < 0.0001). Twelve midbrain and eight medullary lesions were identified only on coronal DWI within the imaging protocol, all showing confirmation on ADC and/or FLAIR correlation. Interobserver agreement was substantial to excellent. Conclusions: Thin-section coronal DWI improves visualization and delineation of small brainstem infarctions, particularly in anatomically compact regions. These findings support its role as a complementary sequence rather than a replacement for standard axial imaging. Full article

Background/Objectives: To evaluate the efficacy of platelet-rich plasma (PRP) as an adjunctive treatment in anterior cruciate ligament reconstruction (ACLR) and its impact on key clinical outcomes. Methods: A systematic search was conducted across five databases until 11 November 2024, including 33 randomized controlled trials (RCTs) that investigated PRP in ACLR. Outcomes analyzed included ligamentization (MRI hypointensity grades), pain VAS scores, functional scores (IKDC, Lysholm, Tegner), knee stability (KT-1000 arthrometer), and tunnel characteristics. Subgroup analyses were performed based on PRP application site, graft type, risk of bias, and follow-up duration. Results: PRP significantly enhanced ligamentization, particularly at 12 months, with marked reductions in MRI hypointensity grades. Patellar tendon grafts demonstrated the most substantial benefits. PRP also significantly reduced postoperative pain, with effects most pronounced in the early recovery period (1–9 months). However, the analgesic benefits diminished over time. Improvements in IKDC scores were observed only in studies with a high risk of bias, while Lysholm and Tegner scores showed no consistent differences between PRP and controls. Knee stability improved significantly with PRP, but this effect was limited to early follow-up periods (3 months). The heterogeneity in PRP preparation methods, application protocols, and patient populations limited the generalizability of the findings. Conclusions: PRP enhances ligamentization and provides short-term pain relief and stability benefits in ACLR. However, its impact on long-term functional recovery and other clinical outcomes remains limited and inconsistent. Standardization of PRP protocols and further high-quality research are necessary to refine its application and therapeutic potential. Full article

Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide, and its management is limited by heterogeneous risk profiles, suboptimal surveillance performance, diagnostic uncertainty in chronically diseased livers, and difficulty individualizing prognosis after treatment. The aim of this narrative review was to critically evaluate artificial intelligence (AI) applications across the HCC care continuum, with emphasis on their intended clinical role, reported performance, evidence maturity, and barriers to implementation. A major strength of this review is that it moves beyond a descriptive catalog of models by structuring the literature around clinically relevant decision points and by explicitly distinguishing emerging proof-of-concept tools from applications with stronger translational potential. Across risk stratification, surveillance, imaging-based diagnosis, pathology, treatment-response prediction, and prognostication, we found that AI consistently demonstrates promise, particularly for identifying patients at higher future HCC risk, improving lesion detection and characterization on ultrasound, CT, MRI, and contrast-enhanced ultrasound, assisting histopathologic classification, and predicting outcomes such as microvascular invasion, recurrence, survival, and response to locoregional therapies. However, we also found that the evidence base remains highly uneven: many diagnostic studies are retrospective and lesion-enriched rather than embedded in true surveillance populations, many prognostic models lack robust external validation and calibration assessment, and reference standards, imaging protocols, and dataset composition vary substantially across studies. These findings are clinically relevant because they highlight both where AI may offer near-term value and why most published systems are not yet ready for routine use. Overall, AI in HCC should be viewed as a rapidly evolving but still transitional field. Its future impact will depend not only on higher-performing algorithms but on clearly defined clinical use cases, multicenter and prospective validation, transparent reporting, workflow-aware evaluation, and implementation strategies that support safe, equitable, and scalable adoption. Full article

Deep learning models for brain tumor classification from magnetic resonance imaging (MRI) often achieve high in-dataset accuracy but exhibit substantial performance degradation when evaluated on unseen clinical data due to domain shift arising from variations in imaging protocols and intensity distributions. Existing approaches largely rely on architectural scaling or parameter-level regularization, which do not explicitly constrain the stability of learned feature representations. This manuscript proposes Feature Space Stability Regularization (FSSR), a lightweight and model-agnostic training framework that enforces consistency in latent feature representations under realistic, MRI-safe-intensity perturbations. FSSR introduces an auxiliary feature space loss that minimizes the ${\ell }_2$ distance between normalized embeddings extracted from the input MRI images and their intensity-perturbed counterparts, alongside standard cross-entropy supervision. This manuscript evaluated FSSR across three convolutional backbones, ResNet-18, ResNet-34, and DenseNet-121, trained exclusively on the Kaggle Brain MRI dataset. Feature space analysis demonstrates that FSSR consistently reduces mean feature deviation and variance across architectures, indicating more stable internal representations. Generalization is assessed via zero-shot evaluation on the fully unseen BRISC-2025 dataset without retraining or fine-tuning. On the source domain, the best-performing configuration achieves 97.71% accuracy and 97.55% macro-F1. Under domain shift, FSSR improves external accuracy by up to 8.20 percentage points and the macro-F1 by up to 12.50 percentage points, with DenseNet-121 achieving a 96.70% accuracy and 96.87% macro-F1 at a domain gap of only 0.94%. Confusion matrix analysis further reveals the reduced class confusion and more stable recall across challenging tumor categories, demonstrating that feature-level stability is a key factor for robust brain MRI classification under domain shift. Full article

Background/Objectives: Cardiac magnetic resonance (CMR) imaging at 7 Tesla provides a substantially higher intrinsic signal-to-noise ratio compared with conventional 1.5 T and 3 T systems, potentially enabling higher spatial resolution, improved tissue contrast, and advanced metabolic imaging. However, clinical translation remains limited by technical challenges associated with ultra-high-field operation. This systematic review aimed to synthesize current human in vivo evidence on the feasibility, applications, and methodological limitations of 7-T cardiovascular MRI. Methods: A PRISMA-guided systematic search of PubMed, Cochrane Library, Web of Science, and Scopus was conducted from database inception through January 2025. Studies reporting human in vivo cardiovascular MRI at 7 Tesla were included. Data regarding study design, sample characteristics, imaging applications, feasibility, quantitative findings, and reported limitations were extracted and qualitatively synthesized. Results: Sixty-five studies met inclusion criteria, predominantly small prospective cohorts (mean sample size = 13), largely involving healthy volunteers. Across diverse applications—including coronary MR angiography, cine imaging, valvular assessment, vascular imaging, flow quantification, myocardial tissue characterization, and multinuclear (³¹P, ²³Na, ³⁹K) imaging—7-T CMR was consistently feasible and capable of producing high-quality images. Quantitative ventricular and vascular measurements were generally concordant with lower field strengths. Incremental benefits were most apparent in high-resolution structural imaging and metabolic applications, whereas routine functional and flow assessments showed limited additional advantages. No serious adverse events were reported. Conclusions: Human cardiovascular MRI at 7 Tesla represents a technically feasible research and early translational platform with selective advantages over established field strengths. Further advances in radiofrequency technology, protocol harmonization, and larger disease-focused studies are required to clarify its potential clinical role. Full article

Moyamoya disease (MMD) is a chronic, progressive cerebrovascular disorder characterized by steno-occlusive changes in the intracranial internal carotid arteries and the development of fragile collateral networks. Imaging plays a pivotal role in diagnosis, disease staging, and management, yet the expanding range of available imaging modalities has resulted in heterogeneous evidence that remains difficult to synthesize. This scoping review aimed to systematically map and critically appraise imaging-based diagnostic approaches used in MMD, summarizing their diagnostic performance, clinical utility, and limitations. A comprehensive literature search was conducted across major databases, and original studies evaluating imaging modalities in human MMD were included. Thirty-three studies published between 1995 and 2023 were analyzed, encompassing digital subtraction angiography, magnetic resonance imaging and angiography, perfusion and functional MRI, computed tomography-based techniques, nuclear medicine, ultrasound, neurophysiological methods, and emerging artificial intelligence applications. Digital subtraction angiography remains the diagnostic reference standard, particularly for disease confirmation and surgical planning. However, noninvasive modalities provide critical complementary information. Magnetic resonance-based techniques offer multiparametric assessment of vascular morphology, hemodynamics, vessel wall pathology, and parenchymal injury. Computed tomography angiography and perfusion imaging provide accessible alternatives with high sensitivity for vascular changes, while functional and neurophysiological methods contribute additional hemodynamic and regional assessments. Artificial intelligence applications show promising diagnostic performance but remain in early validation stages. The evidence base is limited by methodological heterogeneity, inconsistent reference standards, incomplete reporting of diagnostic accuracy metrics, and a scarcity of longitudinal and multimodal studies. Collectively, the findings support a multimodal imaging strategy in MMD, integrating structural and functional information to inform diagnosis and management. Future research should prioritize standardized protocols, longitudinal designs, and clinically validated imaging biomarkers to enable evidence-based diagnostic pathways. Full article

Duropathies represent a spectrum of disorders associated with spinal dural tears and cerebrospinal fluid (CSF) leaks. Diagnosis and treatment is often complicated by overlapping clinical manifestations. This review aims to synthesize current literature on duropathies, focusing on their clinical, neuroradiological, and pathophysiological features. A comprehensive literature review was conducted, analyzing various conditions classified as duropathies, including spontaneous intracranial hypotension (SIH), superficial siderosis (SS), spinal cord herniation, and, as added issue, arachnoid webs. The review emphasized the importance of imaging techniques such as MRI and CT myelography in diagnosing these conditions. Duropathies can arise from congenital anomalies, trauma, and degenerative changes, with SIH being characterized by orthostatic headaches and neurological deficits. Imaging typically reveals specific patterns, such as a widened dorsal subarachnoid space and ventral displacement of the spinal cord. Syringomyelia was frequently associated with arachnoid webs, and complications like SS and bibrachial amyotrophy were noted in patients with persistent ventral spinal CSF leaks. The unifying concept of duropathies is proposed, emphasizing the need for timely intervention to mitigate long-term neurological consequences. Enhanced diagnostic strategies are crucial for improving patient outcomes, and a multidisciplinary approach is recommended for the management of these complex disorders. Further research is warranted to clarify the pathophysiological mechanisms underlying duropathies and to establish standardized treatment protocols. Full article

Background/Objectives: Rapid and accurate classification of ring-enhancing brain lesions (REBLs) into infection or neoplasm is key to clinical triaging for expedited diagnostics in the former to enhance treatment outcomes, especially in the immunocompromised patients. High-resolution three-dimensional (3D) T1 post-contrast (T1+C) MRI provides high-dimensional volumetric data for radiomics analysis. While radiomics is useful in brain neoplasm characterization, its utility in central nervous system infection remains under-explored. In this exploratory study, we aim to determine if a radiomics-machine learning model, based solely on a 3D T1+C MRI dataset, can distinguish infective from neoplastic REBLs. Methods: 92 patients (infection, n = 26; neoplasm, n = 66) with 402 REBLs, who fulfilled criteria for “definite” or “probable” infective or neoplastic REBLs, were identified from scans performed at our hospital over four years and formed the training/validation dataset. All REBLs were manually annotated on T1+C MRI images under radiological supervision. In total, 1197 radiomics features were extracted, feature selection performed using mutual information, and nine machine learning classifiers applied to assess patient-level infection vs. neoplasm classification performance. End-to-end 2D CNN baselines and hybrid radiomics–CNN configurations were additionally evaluated under the same protocol for comparative benchmarking. Model performance was tested on an external holdout dataset of 57 patients (infection, n = 25; neoplasm, n = 32) with 454 REBLs from another hospital. Results: The Multi-layer Perceptron (MLP) model using the Original + LoG + Wavelet feature group demonstrated superior performance. In the cross-validation cohort, it achieved a mean AUC of 0.80 ± 0.02, sensitivity of 0.83 ± 0.09, specificity of 0.77 ± 0.08, and balanced accuracy of 0.80 ± 0.02. On external holdout data, the same configuration showed stable and sustainable performance with an AUC of 0.84, sensitivity of 0.84, specificity of 0.75, and balanced accuracy of 0.80. Conclusions: Our radiomics-machine learning model, based solely on a high-resolution 3D T1+C dataset, shows potential for distinguishing infective REBLs from neoplastic REBLs. Further study, with additional MR sequences and clinical data in a multimodal MRI radiomics-machine learning model, is warranted. Full article

Background: We tested whether a contrast-free protocol can reproduce contrast-enhanced VI-RADS scoring and whether reader expertise influences results. Methods: In this retrospective single-center study (January–December 2024), 65 patients (69 lesions) underwent bladder multiparametric MRI. Two blinded radiologists assigned VI-RADS scores using only T2-weighted and diffusion-weighted imaging (biparametric, non-contrast MRI): an expert (>15 years in urogenital radiology) in genitourinary MRI and a non-expert (5 years of experience in genitorurinary radiology). Two complementary reference standards were used. For reproducibility analysis, the reference standard was the VI-RADS score from the original clinical report based on the full multiparametric examination including contrast-enhanced imaging. For diagnostic accuracy analysis, histopathology was used as the reference standard for muscle-invasive versus non-muscle-invasive disease. Agreement was evaluated with confusion matrices, overall agreement, and weighted Cohen’s kappa. Discrimination for high likelihood of muscle invasion (VI-RADS ≥ 4) was assessed with receiver operating characteristic analysis. Results: Reference scores were VI-RADS 2 (34.8%), 3 (14.5%), 4 (20.3%), and 5 (30.4%). Agreement was higher for the expert than the non-expert (73.9% vs. 56.5%; weighted kappa 0.74 [95% confidence interval 0.56–0.89] vs. 0.58 [0.37–0.75]). The area under the curve for VI-RADS ≥ 4 was 0.87 (0.78–0.95) for the expert and 0.81 (0.69–0.91) for the non-expert. Sensitivity at a biparametric threshold of VI-RADS ≥ 4 was 88.6% for both readers; specificity was 85.3% vs. 73.5%. Post-resection cases showed more discrepancies, mainly overstaging. Conclusions: Contrast-free biparametric MRI may approximate multiparametric VI-RADS scoring only in treatment-naïve pre-TURBT cases with clearly low-risk, non-equivocal imaging features, but performance is reader-dependent and less reliable in equivocal, higher-risk, and post-resection examinations. Contrast-enhanced multiparametric MRI remains preferred for staging. Full article

Background: Current target volume delineation protocols for glioblastoma utilise uniform or isotropic expansion around the surgical cavity and residual tumour, without considering specific sites at risk for infiltration. Tumours arising in different neuroanatomical sites may demonstrate distinct patterns of infiltration. This study aims to review the infiltration and progression sites for the occipital lobe glioblastoma to identify sites potentially at risk. Methods: Patients with occipital lobe glioblastoma managed according to the EORTC-NCIC protocol were identified through a prospective database. Based on MRI analysis, a qualitative description of sites of tumour infiltration and subsequent progression was performed. These were categorised into neuroanatomical subsites adjacent to the occipital lobe: level 1 related to the origin gyrus; level 2 related to adjacent gyral subsites; and level 3 related to subsites that involved distant regions. Patients could be classified in more than one level where multifocal involvement was present at diagnosis or progression. Spatial patterns were assessed in relation to three major white matter tracts: inferior longitudinal fasciculus, cingulum, and corpus callosum. Results: A total of 46 patients were analysed. At diagnosis, 20 patients (43.5%) had medial occipital lobe involvement and 26 (56.5%) had lateral involvement. Level 2 and level 3 infiltration were observed in 33 (71.7%) and 27 (58.7%) patients. Progression occurred in 43 patients (93.5%), with involvement at level 1 in 28%, level 2 in 77%, and level 3 in 98%. Lateral tumours demonstrated proportionately higher progression in the trigone (75% vs. 52.6%) and anterior temporal lobe (50% vs. 15.8%, p = 0.026), while medial tumours more frequently involved the splenium (47.3% vs. 16.7%, p = 0.046). Conclusions: Infiltration and progression of occipital lobe glioblastoma may demonstrate distinct neuroanatomical patterns, with spatial distribution corresponding to major white matter tracts. Full article