Citations

Background: Anterior cervical discectomy and fusion (ACDF) is a widely performed procedure for treating degenerative cervical spine conditions. While it effectively addresses neural decompression and restores segmental alignment, the interplay of baseline alignment and implant-specific factors on postoperative segmental alignment remains underexplored. This study evaluates the influence of preoperative cervical alignment and interbody cage design on segmental alignment changes following 1- to 3-level ACDF. Methods: Following institutional review board approval, we identified 258 patients undergoing ACDF for degenerative pathology between 1 January 2010 and 31 December 2023. Preoperative and postoperative radiographs were analyzed for cervical alignment, disc height, and segmental lordosis. Cage dimensions, lordosis, and positioning relative to vertebral landmarks were recorded. Multivariable linear regression models evaluated predictors of postoperative disc height, segmental lordosis, and their respective changes. Results: Postoperative disc height was positively associated with greater cage height (β = 1.13 mm per mm, p < 0.001) and negatively associated with greater cage lordosis (β = −0.10 mm per °, p = 0.001). Segmental lordosis was positively influenced by cage height (β = 0.78° per mm, p = 0.002) and lordosis (β = 0.42° per °, p = 0.002) but was negatively correlated with the distance of the cage from the anterior edge of the cranial vertebra (β = −1.76° per mm, p = 0.004). Greater preoperative segmental kyphosis predicted more significant postoperative lordosis correction (β = −1.07° per °, p < 0.001). Conclusions: This study underscores the importance of preoperative alignment and interbody cage design in achieving optimal segmental correction following ACDF. While cage height primarily drives disc height restoration, surgical technique, particularly anterior placement of the cage, is pivotal for enhancing segmental lordosis. These findings support personalized surgical planning to optimize alignment and patient outcomes.

Background/Objectives: To evaluate risk factors for postoperative neurological deficits following cervical endoscopic unilateral laminotomy for bilateral decompression (CE-ULBD) and to determine whether intraoperative neurophysiological monitoring (IONM) can predict neurological compromise. Methods: A multicenter retrospective review was performed on 42 CE-ULBD procedures conducted between 2016 and 2024; 33 cases met the inclusion criteria with available imaging and electromyography data. Demographic, operative, and neurophysiological variables were analyzed. Preoperative stenosis severity was graded using the Kang MRI system. Intraoperative IONM data, including electromyography firing and motor evoked potential (MEP) changes, were correlated with new postoperative weakness. Results: The cohort (69.1% male, mean age 70.2 ± 1.7 years, mean BMI 29.6 ± 1.1) included 56 decompressed levels. The most common operative levels were C3-4 (37%) and C4-5 (24%). Postoperative weakness occurred in four patients (12.1%), all of whom had severe (Grade 3) preoperative stenosis. Among these, 50% exhibited preoperative weakness. Neuromonitoring changes correlated significantly with postoperative weakness (Fisher’s Exact, p < 0.001); 100% of patients with new post-operative weakness had sustained MEP decrease at the time of closure. Conclusions: Patients with severe cervical stenosis and preoperative weakness are at heightened risk of postoperative neurological deficits following CE-ULBD. Elevated epidural pressure from continuous irrigation in a constricted canal may exacerbate cord compression, particularly in those with preexisting myelopathy. IONM changes strongly correlate with new deficits and may exacerbate cord compression, particularly in those with preexisting myelopathy, and may serve as an early warning system for impending neurological injury. Surgeons should exercise caution and maintain low irrigation pressures in patients with severe stenosis undergoing endoscopic cervical decompression.

OBJECTIVE Surgical site infection (SSI) is a leading cause of morbidity following posterior spine surgery. The optimal choice of skin closure technique depending on patient- and procedure-specific characteristics remains to be elucidated. METHODS The Neurosurgery Enterprise Registry (NER) at Mayo Clinic was queried for patients who underwent posterior spine surgery from 2017 to 2023. Propensity matching was performed to match patients on the basis of the development of SSI and baseline characteristics, including age, sex, race, and comorbidities. Perioperative outcomes were analyzed using data from the NER and medical records. RESULTS A total of 18,842 cases of posterior spine surgery were identified in the NER, and SSI was observed in 154 cases (0.8%). Of these patients, 308 patients were included in the 1:1 propensity score–matched cohort analysis, with 154 patients included in each group. Sutures were used in 224 patients (72.7%) and staples in 84 patients (27.3%) (p < 0.01). Female patients comprised 45.1% of the sutures group and 46.4% of the staples group (p = 0.8). The mean ± SD age was 59.8 ± 15.4 years in the sutures group and 60.8 ± 11.9 years in the staples group (p = 0.6). Fusion was performed in 27.2% of patients in the sutures group and 44% in the staples group (p < 0.01). Multilevel fusion comprised 68.9% of fusion cases in the sutures group and 67.6% in the staples group (p = 0.8). Comorbidities, such as diabetes (p = 0.6), hypertension (p = 0.1), and the use of disease-modifying antirheumatic drugs (DMARDs) (p = 0.5) and immunosuppressants (p = 0.2), did not differ between groups. Univariate analysis performed in the propensity score–matched cohort showed that SSI was observed in 53.6% of patients in the sutures group and 40.5% in the staples group (p = 0.04). No significant differences were noted regarding 30-day (p = 0.3), 90-day (p = 0.2), and 1-year (p = 0.3) readmissions and 30-day (p = 0.7), 90-day (p = 0.8), and 1-year (p = 0.8) reoperations. On multivariable logistic regression, SSI was not significantly associated with the choice of skin closure technique (p = 0.3). CONCLUSIONS After adjustment for spinal fusion, number of fused levels, the use of immunosuppressants, and other risk factors, SSI development was not significantly associated with the use of sutures versus staples following posterior spine surgery in our institution. Additionally, no significant differences were observed regarding baseline characteristics and other perioperative outcomes. The authors’ analysis shows that skin closure technique did not significantly affect SSI rates and other outcomes in these patients.

Abstract High-grade gliomas (HGG) have significant spatial heterogeneity, complicating therapeutic approaches. While multiparametric MRI (mpMRI) enables noninvasive assessment of this heterogeneity, most predictive models lack biological interpretability when used to classify or predict outcomes. Here, we developed an unsupervised framework that identifies novel mpMRI feature patterns through dimensional reduction, linking them to clinically relevant molecular signatures. We analyzed 18 advanced mpMRI features from 73 stereotactic biopsies across 20 HGG patients. Imaging data were normalized, followed by dimensional reduction using an iterative UMAP across varying parameters. Density-based clustering was used to generate stable image-derived clusters through consensus co-clustering. RNA-seq data from matched biopsies were analyzed using DESeq2 and GSEA to identify differentially expressed genes and enriched pathways. We then constructed a network linking specific imaging features to these transcriptional signatures. Validation was performed in an independent cohort, allowing us to extend clusters across full brain mpMRI. Unsupervised clustering of mpMRI features produced nine stable image-based clusters. These clusters were not fully explained by contrast enhancement, metabolic class, or tumor purity, shown by intraclass correlation (ICC) analyses, suggesting they capture novel biological variation. Transcriptomic analysis showed each cluster was associated with distinct gene expression patterns and enriched biological pathways, including neuronal differentiation, immune chemotaxis, and chromatin remodeling. Key imaging features (sRCBV, MTT, K2)—combined in cluster-specific patterns—enabled construction of an interpretable imaging–transcriptomic network. This was validated in an independent cohort. We then applied to full brain mpMRI, mapping spatially distinct regions to biological trends and patient outcomes. Addressing a research gap in imaging HGG, we present an interpretable network that connects specific patterns of mpMRI to clinically meaningful molecular signatures. This approach advances our understanding of tumor heterogeneity and supports future translational work in precision patient care.

Abstract BACKGROUND Glioblastoma (GBM) is a molecularly and phenotypically heterogeneous tumor, with regional invasion patterns driving treatment resistance and universal recurrence. In infiltrative regions, GBM cells adopt at least two predominant invasive phenotypes: Neuronal-enriched (NEU), with synaptic and neurodevelopment programs, and Glycolytic/Plurimetabolic (GPM), with heightened immune and metabolic activity. These subtypes differ in therapeutic vulnerability and microenvironmental interactions; features not captured by conventional contrast-enhanced (T1+C) MRI due to minimal blood-brain barrier (BBB) disruption. Our group sought to develop predictive models to distinguish variability of NEU/GPM subtypes within infiltrative GBM, utilizing graph convolutional networks (GCN), advanced multiparametric MRI (mpMRI), and spatially-matched image-localized biopsies with transcriptomic profiling. METHODS We analyzed 18 mpMRI features —including conventional, diffusion-weighted, and Dynamic Susceptibility Contrast (DSC) perfusion MRI— of 101 image-localized biopsies from MRI non-enhancing infiltrative margins of 64 GBM patient tumors. Features were hierarchically clustered per modality; each cluster was represented as a node in a Neo4j graph. Edges connected nodes sharing overlapping samples. Nodes were annotated with NEU or GPM subtype proportion. GCNs were trained to classify subtype-enriched nodes using graph topology and node features. RESULTS GCNs achieved high predictive performance on held-out test data (AUC = 0.95 for NEU, 0.88 for GPM; 70:30 train-test split) with similar 5-fold cross-validation performance and generalized well to an independent cohort (AUC &gt; 0.85, n = 45). Importantly, performance was robust without relying on T1+C imaging, enabling prediction beyond BBB-disrupted regions. GCNs outperformed traditional classifiers (logistic regression, random forest). CONCLUSION Our results demonstrate that GCN-modeling of mpMRI features enables non-invasive prediction of invasive GBM subtypes at the tumor margin. Unlike convolutional neural networks (CNN), which rely on spatial continuity, GCNs leverage relational structure between heterogeneous imaging features, making them ideal for modeling sparse, non-grid data. This may support non-invasive regional vulnerability detection, surgical targeting, and improved response assessment.

BACKGROUND AND OBJECTIVES: Aneurysm risk prediction remains an imprecise science that places patients at risk for either over or undertreatment. Machine learning (ML) models may improve clinical practice by adding precision to risk assessment. This study aims to comprehensively assess the current landscape of ML applications in predicting the risk of aneurysm rupture and compare the performance with the widely used PHASES score. METHODS: A systematic review of PubMed, Scopus, and Web of Science was conducted. All studies using ML tools to predict the rupture risk of intracranial aneurysms were included. Meta-analysis was conducted with consideration to the ML algorithms and compared with the PHASES score. RESULTS: Thirty-six studies involving 22 462 patients were included in the final analysis. ML techniques, including 124 models using 25 algorithms, were employed. Among various ML models, while they had comparable diagnostic performance, deep learning exhibited a slightly better performance profile (sensitivity = 0.792, specificity = 0.788, and accuracy = 0.778 in external validation). Based on our analysis, ML, regardless of the algorithm, provides comparable sensitivity (0.743 vs 0.771, P = .60) and higher specificity (0.763 vs 0.507, P < .01) compared with the PHASES score. Consistently, pooling the area under the receiver operating characteristic curve (AUC) for 60 ML models and 5 PHASES score data, ML models exhibited higher AUC (0.84 vs 0.64, P < .01). Using hemodynamic parameters as input for models improved specificity ( P < .01) in the test sets without any significant changes in the sensitivity. The later improvement was not observed in the external validation sets. CONCLUSION: ML techniques have the potential to enhance the prediction of intracranial aneurysm rupture compared with traditional approaches, like the PHASES score. Incorporating hemodynamic parameters may further enhance the accuracy of ML models. Feature prospective studies are required to validate the utility of ML models for clinical integration.

OBJECTIVE While the relationship between smoking and subarachnoid hemorrhage is well established, data regarding the probability of detecting unruptured intracranial aneurysms (UIAs) in smokers remain sparse. The aim of this systematic review and meta-analysis is to provide a comprehensive understanding of the relationship between smoking and the likelihood of identifying UIAs in healthy asymptomatic patients who underwent brain imaging for indications unrelated to UIAs. METHODS A systematic review was conducted following the PRISMA guidelines. The PubMed and Scopus databases were searched for studies published before March 2024 that reported on the presence of UIAs in healthy asymptomatic patients who had undergone brain imaging for indications unrelated to UIAs. Three independent reviewers assessed the eligibility of all retrieved studies. Risk of bias for the included observational studies was assessed using the methodological index for non-randomized studies. Data on UIA prevalence, smoking status, and patient and aneurysm characteristics were extracted. The association between smoking and the presence of UIA was studied. A prevalence proportional meta-analysis was conducted across the included studies. A meta-analysis was performed with a random-effects model by using summary statistics from the included studies. RESULTS Six studies involving 47,788 patients who had undergone brain imaging were identified for quantitative analysis. The pooled UIA prevalence in the sample was 3.07% (95% CI 2.27%–4.16%). Patients who smoked had higher rates of aneurysm detection than the patients who did not smoke (pooled prevalence of 2.96%, 95% CI 2.68%–3.27% vs 2.23%, 95% CI 2.08%–2.39%). Although the relation between the two was not statistically significant (p = 0.06), smoking was associated with higher odds for UIA detection, yielding an OR of 1.34 (95% CI 1.07–1.67) with an I 2 value of 53%. CONCLUSIONS This study suggests a potentially higher risk of UIAs in patients who smoked than in those who never smoked. However, the results of this meta-analysis revealed that smoking was not statistically associated with higher UIA detection. This result could be explained by the limited number of studies published on this topic. If these findings reach statistical significance in future larger studies, it could justify revising guidelines to include cigarette smokers in intracranial aneurysm screening.