Luca Regli

Background/Objectives: Carotid endarterectomy (CEA) is a standard surgical treatment for high-grade symptomatic and asymptomatic internal carotid artery (ICA) stenosis. The Transit Time Flowmeter (TTF) provides an easy-to-use tool to quantify intraoperative ICA blood flow in mL/min. This study aimed to (1) describe post-CEA ICA flow quantified with TTF, (2) identify predictors of post-CEA ICA flow, and (3) examine its association with contralateral steno-occlusive disease. Methods: We queried data from our prospective registry of patients who underwent a CEA between January 2019 and July 2024 and had intraoperative registered post-CEA ICA flow measurements using TTF. Preoperative ICA stenosis was graded according to the NASCET criteria. The clinical data was retrospectively collected. Results: Data from 196 CEAs in 190 patients (median age 74 [67–81]; 25% female) were included. Most patients (86%) had a symptomatic ICA stenosis. The median post-CEA ICA flow was 170 mL/min [IQR 140–242; range 40–800]. Post-CEA ICA flow was not associated with age, sex, comorbidities, symptomatic stenosis, left ventricular ejection fraction (LVEF), or ipsilateral stenosis severity but was significantly higher in patients requiring intraluminal shunt placement during CEA (p = 0.010) as well as in those with contralateral steno-occlusive disease (p < 0.001). Conclusions: TTF is a practical intraoperative tool for quantitative flow assessment during CEA. Higher post-CEA ICA flow in patients with an intraoperative shunt placement or with contralateral steno-occlusive disease may reflect a compensatory hyperperfusion, as the ipsilateral ICA serves as a key supplier when collateral pathways are incomplete.

Neuroepithelial tumors, including diffuse gliomas and embryonal neoplasms, remain among the most lethal cancers despite major advances in molecular diagnostics and some novel targeted therapies for subsets of patients. A persistent barrier to progress is the lack of a unifying framework that integrates their cellular hierarchy, spatial heterogeneity, and distinctive patterns of progression. In this review, we synthesize converging evidence that neuroepithelial tumor biology represents a pathologic re-enactment of brain ontogeny. Integrating molecular, single-cell, histoarchitectonic, and anatomic observations, we propose that neoplastic transformation, aberrant lineage commitment, and dedifferentiation mirror and invert physiological ontogenetic trajectories. This ontogenetic logic provides a coherent explanation for key hallmarks of neuroepithelial tumors, including intratumoral mosaicism, directional anatomic spread, and the emergence of therapy-resistant tumor-propagating cell states. Ontogenetic multimodal integrated therapy is an anatomy-guided strategy that aligns neurosurgery, radiotherapy, and pharmacotherapy with tumor stage and ontogenetic architecture. By shifting emphasis from radiologically visible tumor bulk to compartment-based developmental logic, this framework offers testable hypotheses and a clinically oriented paradigm for diagnosis, treatment planning, and longitudinal monitoring in patients with neuroepithelial tumors.

Background: Diffuse cerebral gliomas are among the most common malignant brain tumors in adults. Blood oxygen level-dependent (BOLD)–MRI has been widely investigated for assessing functional, vascular, and metabolic alterations in this patient population. However, a comprehensive overview of its biological underpinnings, clinical applications, and emerging translational approaches is lacking. Methods: In the present study we conducted a narrative review on the role of BOLD–MRI in diffuse glioma imaging, from historical and clinical applications to recent translational studies including approaches to detect vascular and metabolic alterations as well as its expanding potential to enhance tumor infiltration assessment and treatment surveillance. Results: Task-based and resting-state BOLD–MRI enable localization of eloquent cortical areas and assessment of functional reorganization, though their accuracy is challenged by neurovascular uncoupling and patient-related variability. Combination with vasoactive stimuli allows cerebrovascular reactivity mapping to uncover vascular dysfunction propagating beyond visible tumor margins, correlating with tumor grade and hemodynamic impairment. Quantitative and calibrated BOLD approaches as well as oxygen modulation during BOLD sequence acquisition provide noninvasive biomarkers of tissue hypoxia, oxygen metabolism, and tumor aggressiveness. Conclusions: Despite extensive research, BOLD contrast holds unexploited potential for enhancing glioma diagnosis and management by capturing critical vascular and metabolic alterations. Although challenges remain pertaining to accuracy and standardization, emerging techniques offer substantial potential for the assessment of tumor infiltration extent and therapy surveillance. Advancing clinical integration will require robust standardization, large-scale validation, and correlation with clinical, molecular, and histopathological data.

Background: Postoperative outcome assessment is often based on discrete follow-up visits, limiting characterization of individual recovery trajectories, and the timely identification of adverse events (AEs). Longitudinal smartphone-based monitoring may overcome these limitations by enabling frequent, resource-efficient collection of patient-reported outcomes and complications throughout recovery. Such data may provide a more patient-centered understanding of the postoperative course and complement conventional clinical surveillance. Objective: This study aimed to evaluate the feasibility of smartphone-based longitudinal monitoring of quality of life, subjective well-being, and AEs after elective neurosurgery and compare postoperative recovery trajectories and agreement between patient- and clinician-reported AEs. Methods: This interim analysis of a prospective cohort study included adult patients undergoing elective lumbar decompression, lumbar fusion, supratentorial craniotomy, or infratentorial craniotomy at a Swiss tertiary referral center between June 2023 and January 2025. Participants used a smartphone app to longitudinally report subjective well-being (Subjective Well-Being Index; 0‐10), quality of life (EQ-5D-5L), and AEs for up to 1 year postoperatively. Complications were self-reported using the Therapy-Disability-Neurology (TDN) classification and retrospectively adjudicated by physicians. Descriptive analyses assessed data density, engagement, and concordance between patient- and clinician-reported events. Mixed-effects models were used to evaluate factors associated with postoperative well-being. Results: Of the 100 enrolled patients (median age 64.0, IQR 52.95‐71.6 years; n=45, 45% women), 86 (86%) provided postoperative data. During a median follow-up of 3.2 (IQR 0.2‐11.3) months, participants submitted 4354 longitudinal well-being entries. Patients reported 22 unique AEs, whereas physicians identified 44 AEs, with overlap for 9 (20.5%) events. Most physician-reported AEs were mild (30/44, 68.2%; TDN grade 1‐2), and no grade 4 or 5 events occurred. Patient-reported AEs primarily reflected symptomatic and functional impairments, whereas physician-reported events more often included clinically detected or subclinical findings. In mixed-effects models, time since surgery was associated with improved well-being, and no other factors were statistically significant. Conclusions: Smartphone-based postoperative monitoring was feasible in this elective neurosurgical cohort and generated dense longitudinal patient-reported data beyond routine follow-up. Patient and clinician AE reporting captured partly distinct aspects of postoperative recovery, suggesting that smartphone-based self-reporting may complement rather than replace clinical surveillance.

Purpose: Hypoxia-targeted blood oxygen level–dependent (BOLD) MRI is an emerging non-contrast imaging technique for characterizing tumor biology in isocitrate dehydrogenase-wildtype glioblastoma. Beyond the magnitude of BOLD signal changes, signal heterogeneity may provide valuable information about tumor aggressiveness and microenvironmental complexity. The aim was to assess both the magnitude and heterogeneity of hypoxia-targeted BOLD signal changes across subregions of glioblastomas. Methods: In this prospective pilot study, hypoxia-targeted BOLD MRI data from 12 patients with histopathologically confirmed glioblastoma were analyzed. Tumor subregions were defined using an automated segmentation framework (Oncohabitats) based on conventional MRI and dynamic susceptibility contrast (DSC) MR perfusion imaging. Voxel-wise BOLD signal changes were calculated within the following volumes of interest: highly angiogenic tumor (HAT), low angiogenic tumor (LAT), peritumoral edema, and mirrored contralateral control tissue (mCET). The magnitude of BOLD signal changes was assessed using median BOLD signal changes, while heterogeneity was quantified using interquartile ranges (IQR), and kernel density estimation-derived entropy. Friedman tests with Dunn post-hoc tests were used to assess parameter differences between the VOIs. Results: HAT demonstrated the largest BOLD signal changes compared with LAT, edema, and mCET (all FDR-adjusted p ≤ 0.017). Heterogeneity metrics revealed significantly increased IQR and entropy in HAT compared with edema and mCET (all FDR-adjusted p < 0.008), with LAT showing intermediate values. Conclusion: Hypoxia-targeted BOLD MRI reveals both greater BOLD signal changes and elevated intratumoral heterogeneity in highly angiogenic and likely aggressive glioblastoma subregions. These findings suggest that heterogeneity metrics may provide complementary information to BOLD signal magnitude alone.

Introduction: Meningiomas are the most common primary intracranial tumors and are frequently monitored over extended periods. Volumetric assessment typically requires manual segmentation, which is time-consuming and associated with interrater variability. This study aimed to develop and validate a deep learning-based model for the automated segmentation of meningiomas and associated peritumoral edema on preoperative magnetic resonance imaging (MRI). Methods: We trained a standard nnU-Net deep learning model on contrast-enhanced T1-weighted and FLAIR MRI scans from 100 patients treated at the University Hospital of Zurich. The model was then externally validated on 88 cases from the meningioma SEG-Class dataset from the Cancer Imaging Archive. Segmentation performance was assessed using the Dice similarity coefficient, Jaccard index, and 95th percentile Hausdorff distance. Results: The model achieved mean Dice scores of 0.87 ± 0.23 for meningioma segmentation and 0.63 ± 0.38 for peritumoral edema in internal cross-validation. On the external validation set, the model achieved scores of 0.86 ± 0.17 for meningioma segmentation and 0.31 ± 0.35 for edema. Conclusion: The deep learning model demonstrated high accuracy in segmenting meningiomas and modest performance for peritumoral edema. These results support the potential utility of automated segmentation tools in clinical workflows. Future work should focus on validating model performance across larger multi-center datasets.

Purpose: Simple generalized thresholds for assessing functional impairment in clinical testing are limited, as they fail to consider patient-specific properties such as age, body height, and body mass index. A previously developed machine learning-based model for personalized testing using the five-repetition sit-to-stand (5R-STS) test, that estimates personalized upper limits of normal (ULN) to identify objective functional impairment (OFI) was externally validated to evaluate its performance and generalizability across cohorts. Methods: Only healthy individuals were included in the study. After the machine learning-based model was applied to this external dataset, expected and observed 5R-STS test times were compared using standardized performance assessment metrics including root mean square error (RMSE), mean absolute error (MAE), and R ² values. Additionally, a Bland–Altman analysis was performed to assess agreement between observed and expected values. Validation of the expected ULN involved comparing the proportion of individuals exceeding their personalized thresholds with the corresponding proportion based on the generalized threshold. Subgroup analyses by test setting and country of residence were additionally performed, along with a graphical assessment of model performance. Results: Application of the model to 171 healthy individuals resulted in an RMSE of 2.33 (95% CI: 1.93 to 2.73) seconds, MAE of 1.70 (95% CI: 1.47 to 1.94) seconds, and R ² of 0.064 (95% CI: -0.25 to 0.15). The Bland–Altman analysis demonstrated a mean bias of -1.1 s. Based on the personalized ULNs, OFI was classified in 17.5% of individuals, compared to 6.4% when using the generalized threshold of 10.4 s. These analyses indicated limited external generalization, with acceptable approximation for some faster and mid-range test times but systematic underestimation of slower test times. Multivariable regression analyses showed that remote testing was independently associated with greater prediction bias and higher odds of personalized ULN exceedance compared with supervised testing (adjusted mean difference: − 0.90 s; adjusted OR: 10.7). Exploratory country-of-residence analyses showed differences across the three largest national subgroups. 130 (76.1%) rated ease of use as excellent, and 145 (84.9%) rated clarity of instructions as excellent. 143 participants (83.6%) indicated that they prefer the 5R-STS over a battery of questionnaires. Conclusions: In the context of personalized testing, moving toward individually focused precision assessment of patients requires rigorous external validation to ensure the robustness of such applied computational methods. In this external validation, the model demonstrated limited generalization including a systematic underestimation of slower test times and insufficient personalized ULN calibration. These findings indicate that external validity of models derived from single-center data can be limited, underscoring the importance of comprehensive external validation and, potentially, multicenter retraining before clinical implementation.

Instability of the craniocervical junction is a potentially life-threatening condition requiring surgical stabilization. Traditional occipital plate fixation carries risks of construct loosening and intracranial complications due to variable skull thickness, particularly after posterior fossa decompression where plate fixation is challenging. Occipital condyle screws (OCS) provide direct fixation into the occipital condyles (OCs). However, comprehensive outcome data remains sparse. This systematic review and meta-analysis evaluated anatomical parameters, technical aspects, and surgical outcomes of OCS fixation in craniocervical stabilization. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, PubMed/MEDLINE, Embase, and Scopus were searched for studies reporting techniques and outcomes of occipitocervical fixation using OCS. Two reviewers independently extracted data, and study quality was assessed using the Newcastle-Ottawa Scale, when possible. Random-effects meta-analysis was performed. The primary endpoint was to characterize the technical aspects of craniocervical fixation using OCS and to ascertain its overall feasibility, defined by morphometric suitability, technical success rates, and complication rates. Thirty studies met inclusion: 12 cadaveric (618 specimens), 10 imaging (1,604 participants), and 8 surgical (284 patients). Morphometry consistently showed larger OC in male populations. Bicortical screw placement achieved 100% technical success. Standard 3.5-mm screws (18–24 mm) were commonly used. Recommended trajectories varied (sagittal with 18°–28° angulation; axial with 22°–37° angulation). No major symptomatic vascular or permanent neurological complications occurred. Meta-analytic data revealed significant differences in morphometric measurements of the OC and differences in the OCS length and angulation parameters. OCS fixation appears to be an anatomically feasible and technically promising fixation strategy in selected patients when anatomy and technique are carefully evaluated. Population-specific morphometric variability mandates individualized preoperative assessment. Future comparative studies should define long-term outcomes, fusion rates, and optimize region-specific surgical parameters.