Jeroen Hendrikse

Background and ObjectivesEnlarged perivascular spaces (EPVSs) in the basal ganglia (BG-EPVS) are an important marker of cerebral small vessel disease (cSVD), and EPVS in the centrum semiovale (CSO-...

The reliable visualization of deep cerebral perforating arteries is a challenge. Despite the advancements in high resolution imaging, the optimal modality to depict deep cerebral perforating arteries remains unclear. The purpose of this systematic review was to provide an overview of the imaging modalities used to visualize perforating arteries and to evaluate their diagnostic performance. EMBASE, Ovid Medline, the Web of Science Core Collection, Google Scholar, and Cochrane Central Register of Controlled Trials were searched from database inception to April 15th, 2025. Original studies using imaging modalities to depict perforating arteries in human subjects were included. The search strategy yielded 3,857 articles of which 1,725 remained after removing duplicates. After the initial sift phase, 215 articles remained to be assessed for eligibility based on full-text sifting. Finally, 106 studies (6,785 participants;14,041 hemispheres) were included. As a performance measure, detection rate was defined as the percentage of participants in which at least one perforating artery in one arterial territory could be detected. Pooled estimate (%) of detection rate per imaging modality was calculated using a random-effects model with a 95% confidence interval (CI). Studies used 7T MRA/MRI (n=56), 3T MRA/MRI (n=32), 1.5T MRA/MRI (n=6), CTA (n=4), CT (n=1), DSA (n=14), and transcranial ultrasound (n=3). Pooled estimates found a detection rate of 94% (95% CI 0.90-0.97; I²=64%) for 7T MRA/MRI and 85% (95% CI 0.63-0.95; I²=84%) for 3T MRA/MRI. One 3T MRA study performed an in-vivo diagnostic validation study by direct intraoperative evaluation. Our findings have not been validated in-vivo. Multiple imaging modalities including 7T, 3T, and 1.5T MRA/MRI, CTA, and DSA, have been used to visualize deep cerebral perforating arteries. High-resolution MRI, particularly 7T MRA, shows the highest detection rates (up to 94%), although most studies were performed in healthy volunteers and lacked anatomical validation. Consequently, reported detection rates may overestimate true vessel visualization. Future research should prioritize rigorous in-vivo validation of these techniques to establish their clinical reliability.

Background: Reirradiation is a widely accepted option for second-line treatment in patients with recurrent glioma. However, no standard radiation regimen has been defined. Hypofractionation is aimed at reducing patients’ burden while maintaining the survival benefit, but may increase the risk of radionecrosis. The primary objective of this study is to determine if reirradiation in just 4 fractions is non-inferior to 10 fractions, regarding survival after reirradiation. Methods: RISING trial A was an open label, randomized, non-inferiority, phase III trial with 1:1 allocation for 130 patients among all participating centers but failed to recruit according to planning. RISING trial B will be a phase II, multi-center, clinical trial with a historic control group. The experimental group receives 4 stereotactic fractions. The historic control group has received 10 fractions (standard-of-care) with a biologically equivalent dose on surrounding brain tissue. The primary endpoint is overall survival after reirradiation. The key secondary endpoint is progression-free survival. Other secondary endpoints are recurrence patterns, toxicity (specifically clinically relevant radionecrosis) and anti-edema treatment. We will collect and report on Health-Related Quality of Life (HRQoL) data in the experimental arm. Discussion: We expect to demonstrate the non-inferiority and safety of a 4-fraction hypofractionation schedule for reirradiation of gliomas. This schedule may then become a standard-of-care option with minimal burden for patients with recurrent glioma, and limited use of scarce healthcare resources. Trial registration: Registered at Netherlands Trial Register (NTR), Trial ID: NL72766.041.20, registered at 07-04-2020, https://www.onderzoekmetmensen.nl/en/trial/52643

Objective: Aneurysm wall enhancement (AWE) may predict aneurysm growth and rupture in the short‐term, but there is a lack of long‐term follow‐up studies. We aimed to determine whether unruptured intracranial aneurysms (UIAs) with AWE have a higher probability of aneurysm instability during long‐term follow‐up compared with those without AWE. Methods: For this longitudinal cohort study, we obtained individual patient data from two international cohorts. We included patients with ≥ 1 untreated UIA who underwent gadolinium‐enhanced aneurysm wall imaging and magnetic resonance angiography (MRA) between 2012 and 2016, and had follow‐up imaging and/or had a rupture during follow‐up. The outcome was aneurysm instability, defined as time to growth, morphological change, or rupture during follow‐up. We calculated the 7.5‐year probability of aneurysm instability with 95% confidence interval (CI) with the Kaplan–Meier estimator. We used Cox regression survival analysis to calculate the crude hazard ratio (HR) with corresponding 95% CI of AWE for aneurysm instability, and the HR adjusted for age and aneurysm size at baseline. Results: We included 198 patients (median age = 58 years, interquartile range = 50–68, 139 women, 70%) with 224 aneurysms. Aneurysm instability was observed in 15 of 72 aneurysms (21%) with AWE and 13 of 152 aneurysms (9%) without AWE during a median follow‐up duration of 6.8 years (IQR = 3.3–7.9). The 7.5‐year probability of instability was 29.2% (95% CI = 14.5%–41.3%) in aneurysms with AWE and 9.3% (95% CI = 2.9%–15.3%) in aneurysms without AWE (crude HR = 4.29, 95% CI = 1.90–9.72, adjusted HR = 5.06, 95% CI = 2.13–12.02). Interpretation: UIAs with AWE have a higher probability of 7.5‐year aneurysm instability compared with those without AWE. ANN NEUROL 2026;99:863–870

MRI markers of cerebral small vessel disease (cSVD) in patients with spontaneous intracerebral hemorrhage (sICH) have provided insight in the underlying vascular pathology. Cortical cerebral microinfarcts (CMIs) are a relatively novel marker of cSVD. We aimed to assess presence and location within the cortex of CMIs in patients with sICH on 3T and 7T MRI, and to determine their association with hematoma location and other neuroimaging markers of cSVD. From a multicenter prospective observational cohort study, we included consecutive adult patients who underwent 3T and/or 7T MRI within 3 months after sICH. We determined presence and location of CMIs within the cortex (superficial or deeper layers; on 7T MRI) and assessed associations between presence of cortical CMIs and vascular risk factors, hematoma location, and other MRI markers of cSVD with multiple regression analyses. We included 135 patients (mean age 63 years; 30% female). On 3T MRIs, we found 100 cortical CMIs in 57 patients (42%, median number 2, interquartile range [IQR] 1-3; lobar ICH 24 of 58 patients [41%]; non-lobar ICH 33 of 77 patients [43%]). On 7T MRI images, we found 59 cortical CMIs in 28 of 40 patients (70%; median number of 2, IQR 1-3; lobar ICH 8 of 12 patients [67%]; non-lobar ICH 20 of 28 patients [71%]). Presence of cortical CMIs was associated with history of TIA/ischemic stroke (relative risk [RR] 2.7, 95% CI 1.1-6.4), but not with other vascular risk factors or any of the MRI markers of cSVD. On 7T MRI, CMIs were located in the superficial part of the cortex in 30% of patients with lobar vs 5% of patients with non-lobar ICH (RR 2.7, 95% CI 1.5-5.0). Cortical CMIs are a common finding in patients with sICH on both 3T and 7T MRIs with a similar frequency in patients with lobar and non-lobar sICH. Patients with lobar sICH, more often have CMIs in the superficial part of the cortex than those with non-lobar sICH. Location of CMIs within the cortex might provide insight into the underlying type of small vessel disease in sICH.

BACKGROUND AND PURPOSE: Previous studies showed that intracranial aneurysm wall enhancement (AWE) is associated with aneurysm growth or rupture. These studies assessed growth with manual 2D measurements or eyeballing, both of which are prone to interobserver variability. To minimize this variability, we assessed the association between AWE and semi-automatically quantified 3D morphological changes in aneurysms during long-term follow-up. MATERIALS AND METHODS: We included patients with an unruptured intracranial aneurysm who had baseline MR aneurysm wall imaging and were followed with MR or CT angiography for ≥1 year. We used in-house-developed software to measure six 3D morphological parameters on paired baseline and follow-up scans and determined changes over time. We compared the proportion of aneurysms showing morphological change (modified Z-score <-3.5 or >+3.5) between aneurysms with and without AWE. The risk difference with 95% CI was calculated for each morphological parameter. For parameters with a statistically significant change difference between aneurysms with and without AWE, we calculated ORs with 95% CI in a univariable logistic regression model, and adjusted for aneurysm size in a bivariable model. RESULTS: Sixty-two patients with 72 unruptured intracranial aneurysms met inclusion criteria. Twenty aneurysms (28%) in 18 patients showed AWE at baseline. Median follow-up was 5.8 years (IQR 4.6–6.6). For the parameter curvedness, the proportion of aneurysms showing an increase was higher in aneurysms with AWE (6 of 20, 30%) than aneurysms without AWE (2 of 52, 4%), with a risk difference of 26% (95%CI 9–49%). For the other five morphological parameters, the proportion of aneurysms with morphological change was comparable between aneurysms with and without AWE. In logistic regression analysis, AWE was associated with curvedness increase (crude OR 10.7 [95%CI 2.2–78.9], adjusted OR 6.1 [95%CI 1.01–50.3]). CONCLUSIONS: AWE was associated with aneurysm shape change during long-term follow-up, with an increase in 3D quantified curvedness that was independent of aneurysm size. This reinforces previous findings that AWE is associated with aneurysm instability, in particular curvedness increase, and suggests that curvedness could be a suitable parameter to capture aneurysm instability. Future studies need to investigate whether an increase in this parameter predicts aneurysmal rupture. ABBREVIATIONS: AWE = aneurysm wall enhancement; AWI = aneurysm wall imaging; IBSI = imaging biomarker standardization initiative; UIA = unruptured intracranial aneurysm.

BACKGROUND AND PURPOSE: The performance of current prediction models for intracranial aneurysm growth and rupture is suboptimal, and new markers are needed to improve prediction. There is a strong need for longitudinal studies that use standardized morphological parameters. In this longitudinal study, we aimed to identify standardized three-dimensional (3D) quantified morphological parameters as predictors of aneurysm growth or rupture during long-term follow-up. MATERIALS AND METHODS: We used a database of consecutive patients with saccular unruptured intracranial aneurysms diagnosed between 2008–2018. Employing a retrospective case-cohort design, we included a computer-generated random sample of aneurysms from the full cohort and aneurysms with growth or rupture during follow-up outside the random sample. The case-cohort design is efficient for low-incidence outcomes while maintaining the temporal association between exposure and outcome. Aneurysms were annotated on baseline CTA or MRA images, and 3D morphological parameters were quantified. Univariable and multivariable Cox proportional hazards models were used to identify 3D morphological predictors of either aneurysm growth or aneurysm rupture. An inverse sampling probability weight was applied to obtain unbiased estimates of the hazard ratios. RESULTS: We included 278 patients (median age, 59 years [IQR50-66]; 209 women) with 327 aneurysms, of which 239 aneurysms were stable during follow-up (73%), 68 grew without subsequent rupture (21%), 7 grew with subsequent rupture (2%), and 13 ruptured without preceding growth (4%). In the multivariable model for growth prediction (median follow-up 4.1 years [IQR1.9–7.1]), 2 parameters were retained: major axis (HR 1.16, 95%CI: 0.84–1.61) and shape index (HR 1.53, 95%CI: 0.76–3.08), with a c-statistic of 0.56 (95%CI: 0.49–0.63). The same parameters were retained in the multivariable model for prediction of rupture (median follow-up 4.5 years [IQR2.1–7.3]): major axis (HR 2.27, 95%CI: 1.36–3.80) and shape index (HR 3.33, 95%CI: 0.95–11.62), with a c-statistic of 0.85 (95%CI: 0.77–0.94). CONCLUSIONS: We identified major axis and shape index as candidate 3D quantified morphological predictors of both aneurysm growth and rupture, but only for rupture these had good discriminative power in our cohort. These parameters will need external validation and should be integrated with existing clinical prediction models. ABBREVIATIONS: ELAPSS = Earlier subarachnoid hemorrhage, Location of the aneurysm, Age, Population, Size of the aneurysm, and Shape of the aneurysm; IBSI = imaging biomarker standardization initiative; PHASES = Population, Hypertension, Age, Size of aneurysm, Earlier SAH from another aneurysm, and Site of aneurysm; UIA = unruptured intracranial aneurysm.

IMPORTANCE: Postoperative delirium (POD) is a multietiological condition and affects 20% of older surgical patients. It is associated with poor clinical outcome and increased mortality. OBJECTIVE: We aimed to develop and validate a risk prediction algorithm for POD based on a multimodal biomarker database exploiting preoperative data (predisposing factors) and procedural factors as well as perioperative molecular changes associated with POD (precipitating factors). DESIGN: BioCog is a prospective cohort study conducted from November 2014 to April 2017. Patients were followed up for seven postoperative days after surgery for POD. Gradient-boosted trees (GBT) with nested cross-validation was used for POD prediction. SETTING: Patients aged ≥65 years were enrolled at the anesthesiologic departments of two tertiary care centers. EXPOSURE: All patients underwent surgery with an expected duration of at least 60min. Clinical, neuropsychological, neuroimaging data and blood were collected and clinically well established as well as non-established biomarkers (e.g., gene expression profiling) were measured pre- and postoperatively. MAIN OUTCOME: POD according to DSM 5 until the seventh postoperative day RESULTS: 184 of 929 (20%) patients experienced POD. A GBT algorithm using both preoperative data, characteristics of the intervention and postoperative changes in laboratory parameters achieved the highest area under the curve (0.83, [0.79; 0.86]) with a Brier score of 0.12 (0.12; 0.13). CONCLUSIONS AND RELEVANCE: Models combining predisposing factors with precipitating factors predict POD best. Non-routine laboratory data provide useful information for POD risk prediction, providing relevant results for future studies on the molecular factors of POD. In addition, possibly relevant molecular mechanisms contributing to the development of POD were identified, mostly indicating a dysregulated postoperative immune response. This study constitutes the basis for future hypothesis-driven analyses or implementation of prediction expert system for clinical practice.

BACKGROUND AND PURPOSE: Treatment-induced effects are difficult to differentiate from progressive disease in radiologically progressing diffuse gliomas after treatment. This retrospective, single-center cohort study investigated the diagnostic value of arterial spin-labeling perfusion in differentiating progressive disease from treatment-induced effects in irradiated patients with a high-grade glioma. MATERIALS AND METHODS: Adults with a high-grade glioma diagnosed between January 1, 2012, and December 31, 2018, with a new or increasing contrast-enhancing lesion after radiotherapy with or without chemotherapy and arterial spin-labeling were consecutively included. Arterial spin-labeling is part of the routine follow-up examinations of patients with a high-grade glioma. The outcomes of progressive disease or treatment-induced effects were defined after histologic or >6 weeks radiologic follow-up. Two neuroradiologists graded the arterial spin-labeling visually as negative (hypointense to gray matter) or positive (iso-/hyperintense). Additionally, the arterial spin-labeling signal intensity in the enhancing lesion was compared quantitatively with that in the contralateral normal brain. Diagnostic test properties and the Cohen κ inter- and intrarater reliability were determined. We present data according to the time after radiation therapy. RESULTS: We included 141 patients with 173 lesions (median age, 63 years). Ninety-four (54%) lesions showed treatment-induced effects, and 79 (46%), progressive disease. For visual analysis, the ORs of an arterial spin-labeling positive for progressive disease in the group with progression within 3, between 3 and 6, and after 6 months after radiation therapy were 0.65 (95% CI, 0.28–1.51; P = .319), 3.5 (95% CI, 0.69–17.89; P = .132), and 6.8 (95% CI, 1.48–32; P = .014). The areas under the curve were 0.456, 0.652, and 0.719. In quantitative analysis, the areas under the curve were 0.520, 0.588, and 0.587 in these groups. Inter- and intrarater reliability coefficients were 0.67 and 0.62. CONCLUSIONS: Arterial spin-labeling performed poorly in differentiating progressive disease from treatment-induced effects in high-grade gliomas within 6 months after radiation therapy, with fair performance after this period. Arterial spin-labeling may need to be combined with other imaging features and clinical information for better performance.