Diabetes is associated with cognitive decline, but the underlying mechanisms are complex and their relationship with Alzheimer’s Disease biomarkers is not fully understood. We assessed the association of small vessel disease (SVD) and amyloid burden with cognitive functioning in 47 non-demented older adults with type-2 diabetes from the Israel Diabetes and Cognitive Decline Study . FLAIR-MRI, Vizamyl amyloid-PET, and T1W-MRI quantified white matter hyperintensities as a measure of SVD, amyloid burden, and gray matter (GM) volume, respectively. Mean hemoglobin A1c levels and duration of type-2 diabetes were used as measures of diabetic control. Cholesterol level and blood pressure were used as measures of cardiovascular risk. A broad neuropsychological battery assessed cognition. Linear regression models revealed that both higher SVD and amyloid burden were associated with lower cognitive functioning. Additional adjustments for type-2 diabetes-related characteristics, GM volume, and cardiovascular risk did not alter the results. The association of amyloid with cognition remained unchanged after further adjustment for SVD. Our findings suggest that SVD and amyloid pathology may independently contribute to lower cognitive functioning in non-demented older adults with type-2 diabetes, supporting a multimodal approach for diagnosing, preventing, and treating cognitive decline in this population. Various factors have been suggested to contribute to this association, including altered insulin signaling, hyperglycemia, advanced glycation, chronic low-grade inflammation, small vessel disease (SVD), large vessel disease, and Alzheimer’s disease (AD) pathology2, but specific pathways and their relationship with AD biomarkers are complex and not fully understood. Brain imaging correlates for some of these pathological mechanisms include white matter hyperintensities (WMH) as a measure of SVD, total gray matter (GM) thickness/volume as a measure of atrophy, and quantified amyloid-beta (Aβ) load on amyloid-PET.Diabetes is consistently associated with cognitive decline and dementia3. Diabetes-related abnormalities in small vessels, such as seen throughout the body in patients with diabetes, may be the cause of such microangiopathic brain changes. In agreement, WMHs have been shown to be more prevalent in patients with diabetes and linked to cognition and cognitive decline4, though were found insufficient to explain all the associations between diabetes and cognition4.WMHs are thought to reflect demyelination and axonal loss as a consequence of chronic ischemia caused by cerebral SVD 5, though most clinico-pathological studies have failed to show such an association6. Other studies even found lower AD pathology in brains of patients with diabetes8. In line with the clinico-pathological studies, many AD biomarker-based studies encompassing PET imaging or cerebro-spinal fluid (CSF) found no association between diabetes and AD biomarkers9.The association between diabetes and AD is controversial. Some studies have shown that diabetes is associated with increased risk for clinical AD10, while others showing no such association11. There is scarce knowledge about the impact of amyloid deposition on cognition in patient with diabetes12.The association of amyloid pathology and cognitive functioning is also controversial with some studies showing that higher amyloid pathology is associated with lower cognitive functioningIn this work we aim to assess the association between SVD, amyloid burden measured by PET imaging, and GM volume with cognitive functioning in older adults with type-2 diabetes (T2D). We hypothesize that both pathological biomarkers are associated with cognition in patients with diabetes, contributing to the lower cognitive functioning and higher dementia rates seen in this population.The cohort consistent of 47 IDCD participants that had both amyloid-PET and MRI , suggesting that amyloid deposition affects the brain globally in a similar way. Further adjustment for total GM volume did not alter the results .A secondary analysis showed no quadratic association between Aβ-SUVR and cognition  indicating a linear association.Greater WMH volume was associated with lower global cognitive functioning, when adjusting for demographics, MRI-to-cognitive testing time interval, and ICV . Results remained essentially unchanged when adding type-2 diabetes characteristics to the model , and when adding cardiovascular risk and depression to the models  and approached significance with lower executive functions and language functioning .In this cross-sectional study of non-demented older adults with type-2 diabetes we found that higher amyloid pathology and WMH volume were associated with lower cognitive functioning after adjusting for sociodemographic variables, type-2 diabetes related characteristics, GM volume, and cardiovascular risk. Further adjustment for WMH showed that the association of amyloid with cognitive functioning remains significant. Taken together, our findings suggest that amyloid and SVD are distinct pathological mechanisms that independently contribute to lower cognitive functioning in non-demented older adults with type-2 diabetes.12, a clinic-based cohort that recruits non-demented older adults, showed that SVD, neurodegeneration, and amyloid pathology are independently associated with lower cognition and that the association between diabetes and cognition is mainly mediated by greater neurodegeneration. Our work is in line with these findings, expands them to a population-based cohort, accounts for HbA1c, and adds the aspect of non-atrophy-dependent contribution of amyloid pathology to cognition.Recent work from MEMENTO13 but cross-sectional studies show mixed results. Our findings are in line with previous studies demonstrating an association between higher amyloid and cognitive impairment14, though other studies did not find such an association11. This complex relationship between amyloid deposition and cognitive functioning may depend on the specific cohort characteristics and disease stage. This is consistent with the robust association we observed between amyloid and executive functions, the cognitive domain most affected in diabetes15.Longitudinal studies indicate that cognitive decline is faster in amyloid positive cognitively normal adults16. Such lower amyloid positivity rates have been suggested before in patients with type-2 diabetes17 and warrant further investigation in larger cohorts. Our results suggest that the association of amyloid with cognition does not depend on frank amyloid positivity since the best model fit was linear, rather than quadratic and does not depend on the specific localization of amyloid deposition. Taken together, our findings suggest that though lower rates of amyloid pathology may be seen in patients with type-2 diabetes, the presence of amyloid may be clinically important as it contributes to lower cognition independent of glycemic control level, SVD or brain atrophy.The rate of amyloid positivity in our cohort  were lower than the accepted rates for this age range, ~35% for 80Y with normal cognition3, prospective studies demonstrate that SVD as indicated by WMHs is associated with, and directly contributes to, cognitive decline in the general population3. It has also been shown that type-2 diabetes is associated with higher levels of SVD4. Consistent with our findings, some studies demonstrate an association between higher degree of SVD and lower cognitive functioning in patients with diabetes12.Although data from cross-sectional studies have been ambiguousStrengths of our study include measurable criteria (rather than self-reported) for type-2 diabetes diagnosis, a broad cognitive battery, amyloid-PET and MRI performed on the same patients with quantifiable measurements for both. Limitations include the cross-sectional design, relatively small number of patients, risk for selection bias, and relatively small number of amyloid-positive scans.In conclusion, we found that higher amyloid and SVD burden are independently associated with lower cognitive functioning, after adjusting for glycemic control. Our findings suggest that multiple factors may independently contribute to cognitive decline in non-demented older adults with type-2 diabetes, indicating a multimodal and individualized approach for the prevention, diagnosis, and treatment of cognitive decline in this population.18 that were randomly selected to have both amyloid-PET and brain MRI (scanned between 2013 and 2019). The IDCD is a longitudinal community-based cohort study that recruits cognitively normal, older adults (>65Y), with type-2 diabetes, from the Maccabi Healthcare Services, the second largest HMO in Israel, providing detailed medical information on each participant, including diagnoses, medications, and blood exams.This is a cross-sectional study utilizing the subset of subjects from the Israel Diabetes and Cognitive Decline Study (IDCD) cohortThe research was conducted in accordance with the relevant ethical guidelines and regulations. Signed informed consent was obtained from all participants. The Research Ethics Committee of Icahn School of Medicine at Mount Sinai, Sheba Medical Center, and Maccabi Healthcare Services approved the study.Hemoglobin A1c (HbA1c) data was provided by Maccabi. All HbA1c levels available in Maccabi up to PET-date for amyloid analysis and MRI-date for WMH analysis were averaged for each participant and used as a measure of glycemic control.Mean cholesterol levels and systolic and diastolic blood pressure measurements were provided by Maccabi and added to the statistical models. The score of the geriatric depression scale (GDS) at baseline was used as a measure of depression.3) 3-dimensional spoiled gradient recalled echo (SPGR) T1-weighted sequence and T2-weighted fluid attenuated inversion recovery (FLAIR) sequence19. Amyloid-PET scans were performed on a Philips-Vereos PET/CT scanner in 3D acquisition mode with a low-dose CT scan for attenuation correction. Acquisition began 90 minutes post-injection 4–5 millicurie of [F18]Flutemetamol Vizamyl TM  and took 20 minutes.Imaging was performed at Sheba Medical Center. MRI on a 3T scanner  included high-resolution (1mmhttp://www.fil.ion.ucl.ac.uk/spm/ext/#VBMtools)20 and implemented in Statistical Parametric Mapping (SPM8) as previously described21. Total GM volume adjusted to total intracranial volume (ICV) was used for analysis. WMH volume was extracted from FLAIR images using SPM8 and its Lesion Segmentation Toolbox (LST) with k=0.15 as previously described19.T1-weighted images were processed using voxel-based morphometry  were created using whole cerebellum as reference region. Frontal, parietal, cingulate, temporal, and global Aβ-SUVR values were extracted in native space using Freesurfer18. Each test score was converted to z-score, normalized based on the corresponding baseline mean and standard deviation. The z-scores of the tests within a domain were averaged and then normalized again by its mean and standard deviation to create a domain specific composite score. A global cognition z-score was obtained by averaging the domains z-score. The cognitive assessment closest to PET or MRI, were used.The IDCD study administers a broad neuropsychological battery covering four cognitive domains - memory, attention/working memory, executive functions, and semantic categorizationLinear regression models were tested to assess the associations of amyloid and WMH burden with cognition. Covariates were added in a sequential manner with demographics , the time interval between PET/MRI and cognitive testing  first, then type-2-diabetes related characteristics , and then cardiovascular risk  and depression. For the analyses of amyloid, total GM volume  was further added. Finally, WMH was added as a secondary analysis to evaluate the independent contributions of WMH and Aβ-SUVR to the cognitive outcomes. Spearman rank-order correlation coefficient were used to assess the correlations between WMH, amyloid and GM volume. Statistical significance was defined by p<0.05. Complete-case analysis was performed with SAS software, Version [9.4] . Global Aβ-SUVR was used as the quantitative measure of amyloid burden. Secondary analyses tested associations of SUVR in specific brain regions with cognition.


Attenuation compensation (AC) is beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). However, traditional AC methods require the availability of a transmission scan, most often a CT scan. This approach has the disadvantage of increased radiation dose, increased scanner costs, and the possibility of inaccurate diagnosis in cases of misregistration between the SPECT and CT images. Further, many SPECT systems do not include a CT component. To address these issues, we developed a Scatter-window projection and deep Learning-based AC (SLAC) method to perform AC without a separate transmission scan. To investigate the clinical efficacy of this method, we then objectively evaluated the performance of this method on the clinical task of detecting perfusion defects on MPI in a retrospective study with anonymized clinical SPECT/CT stress MPI images. The proposed method was compared with CT-based AC (CTAC) and no-AC (NAC) methods. Our results showed that the SLAC method yielded an almost overlapping receiver operating characteristic (ROC) plot and a similar area under the ROC (AUC) to the CTAC method on this task. These results demonstrate the capability of the SLAC method for transmission-less AC in SPECT and motivate further clinical evaluation. However, these CT-based AC (CTAC) methods have multiple disadvantages, such as increased radiation dose, higher scanner costs, and possible misregistration between the SPECT and CT images potentially leading to inaccurate diagnosis.6 Further, many SPECT systems often do not have a CT component. For example, SPECT systems in smaller community hospitals and physician offices, as well as mobile SPECT systems facilitating use in remote locations are often SPECT-only. The emerging solid-state-detector-based SPECT systems, which provide higher sensitivity, energy, temporal, and spatial resolution compared to conventional SPECT systems, often do not have CT imaging capability either.8 For these reasons, there is an important need to develop transmission-less AC (Tx-less AC) methods for SPECT.Attenuation of photons is a major image-degrading effect that adversely impacts image quality in single-photon emission computed tomography (SPECT). Multiple studies have shown that attenuation compensation (AC) is beneficial for clinical interpretations of SPECT myocardial perfusion images.11 and methods that operate on the iterative inversion of the forward mathematical models of SPECT systems.13 More recently, deep learning (DL)-based methods have shown significant promise for Tx-less AC.19 Shi et al. recently reported promising performance of a conditional generative adversarial network for Tx-less AC for myocardial perfusion SPECT (MPS).18 Chen et al. developed strategies for generating attenuation maps using emission data for dedicated cardiac SPECT with small field-of-view and found that their strategies outperformed the method that directly predicts AC images from non-attenuation-corrected images.19 While the performance of these methods is promising, these DL-based methods have typically been evaluated using figures of merit (FoM) that measure the fidelity between the images reconstructed using the DL-based approach with a reference standard, which is typically the image reconstructed with the CT-based AC method. Medical images are acquired for specific clinical tasks. Thus, clinical translation of these Tx-less AC methods requires that they be evaluated in reference to the clinical task.23 However, studies have shown that evaluation using fidelity-based FoMs may not correlate with performance on clinical tasks in myocardial perfusion imaging (MPI).26 Thus, it is crucial to evaluate these methods on the specific clinical tasks for which the images are acquired.Given this high significance, multiple Tx-less AC methods have been proposed, including methods that use SPECT emission data to estimate attenuation maps27 Based on this premise, we had proposed a DL-based Tx-less AC method for SPECT.28 In this paper, we advance upon this idea to propose a Scatter-window projection and DL-based Tx-less AC (SLAC) method for myocardial perfusion SPECT (MPS) that uses only SPECT emission data in photopeak and scatter windows. We objectively evaluate the method on the clinical task of myocardial defect detection in a retrospective study.We have shown that scatter-window data in SPECT contains information to estimate the attenuation distribution.2.2.118 Thus, the scatter-window projection was reconstructed using an ordered-subsets expectation maximization (OSEM)-based approach, yielding an initial estimate of attenuation map.30 Then, in this study, we used a DL-based technique to segment the initial estimate of attenuation maps. U-Net-based approaches have shown promise in biomedical image segmentation problems.33 Thus, we used a U-Net-based approach, namely, multi-channel input and multi-encoder U-Net (McEUN), to segment the initial estimate of attenuation maps. The McEUN was trained to segment the initial estimate of attenuation maps into six regions, including skin and subcutaneous adipose, muscles and organs, lungs, bones, patient table, and background. The McEUN mainly consists of two components: an encoder with muti-channel input and an assembly of six decoders. To stabilize the network training and leverage salient regions, skip connections with attention gate (AG) were implemented between the output of layers in the encoder and each decoder.34 Dropout was applied to prevent overfitting.35 The network was designed to input the whole 3-D image and maximize the amount of information learned in a global sense. The network was trained to minimize the cross entropy between estimated and true segmentation.The overall framework of the SLAC method is shown in 2.223 The description of the evaluation study consists of four parts, including data collection and curation, network training and method implementation, process to extract task-specific information, and figures of merit used.We evaluated the SLAC method on the clinical task of detecting cardiac perfusion defects in a retrospective Institutional Review Board-approved evaluation study with anonymized stress SPECT MPI data. We compared the performance of our method with activity maps reconstructed using a CT-based AC (CTAC) method and to images obtained without AC, referred to as no-AC (NAC) approach. We followed the recently proposed Recommendations for EvaLuation of AI in NuClear-medicinE (RELAINCE) guidelines to lend rigor to our evaluation.2.2.199mTc-tetrofosmin. SPECT emission data were collected in photopeak (126–154 keV) and scatter windows (114–126 keV). CT images were acquired at 120 kVp on a GE Optima CT 540 system integrated with the GE Discovery NM/CT 670. To avoid misalignment between CT and SPECT scans, CT images were registered to the SPECT space using MIM Maestro . CT-defined attenuation maps were calculated from the CT scans using a bi-linear model.36The dataset used in this study consisted of N = 648 anonymized clinical SPECT/CT stress MPI studies scanned between January 2016 and July 2021, with SPECT projection data and CT images along with clinical reports. As per the clinical reports, we categorized patients diagnosed with normal rest and stress myocardial perfusion function as healthy patients, while patients diagnosed with ischemia in a left ventricular wall as diseased patients. MPI scans were acquired on a GE Discovery NM/CT 670 system after the injection of 37 We designed 27 types of clinically realistic defects with three radial extents, three severities, and at three locations. A summary of defect types is shown in For evaluating the SLAC method on the clinical task of detecting cardiac defects, knowledge of the existence and location of the defects was needed. The clinical records have limitations in providing this information. To address this issue, we implemented a strategy that introduces synthetic cardiac defects in healthy patient images.The whole dataset was divided into the training dataset (N = 508) and the testing dataset (N = 140). Because we need the ground truth of defects in the test dataset for our evaluation study, the testing dataset only consisted of patients that were diagnosed as healthy according to the clinical records, and synthetic defects were introduced in 71 of these 140 healthy test patients, referred to as defect-present samples. The remaining 69 healthy test patients were referred to as defect-absent samples. We generated 27×71 = 1917 defect-present samples in both photopeak and scatter energy windows. We also generated 27×69 = 1863 defect-absent samples, although they were identical if from the same patient.38 The average attenuation coefficients of each region were calculated and served as the predefined attenuation coefficients.As mentioned in 2.2.239 Biases were initialized to a constant of 0.03. The McEUN was trained to minimize a weighted cross entropy loss between predicted and CT-based segmentations using Adam optimizer.40 We optimized the weight parameters to yield the best segmentation performance. Five-fold cross-validation was implemented to prevent overfitting. The training and validation were performed using Keras A total of N = 508 samples were used for the network training. The kernel weights of the McEUN were initialized using the Glorot normal initializer.30 The final estimates of attenuation maps were used for AC. The reconstructed activity images had a size of 64×64×64 with a voxel size of 0.68 cm. Following the clinical protocols, the reconstructed activity images were reoriented into short-axis slices and filtered by a Butterworth filter with an order of 5 and cutoff frequency 0.44 cm−1. CTAC-based images and NAC-based images were obtained using the same OSEM-based reconstruction approach and post-processing procedures as used in the SLAC method but with different AC approaches.As mentioned in 2.2.343 In our evaluation study, the location of the defect was chosen to be in the inferior left ventricular wall in the test dataset. Then, we had a patient population where the defect extent and severity were varying, but the location of the defect was the same in the entire population in the test dataset. Therefore, there were 9×71 = 639 defect-present samples and 9×69 = 621 defect-absent samples in the evaluation study. Previous studies have shown that the channelized Hotelling observer (CHO) with rotationally symmetric frequency channels can emulate human-observer performance on the task of detecting perfusion defects from MPS images in this setting.45 Thus, in this study, we used this model observer. We extracted a 32×32 region from the middle 2-D slice of the short-axis images that had the defect centroid at the center and applied the CHO to this region to yield test statistics. These statistics were calculated for each defect-present and -absent image in the test set using a leave-one-out strategy.We objectively evaluated the performance of the SLAC method on the task of detecting myocardial perfusion defects in an observer study. While ideally, such evaluation should be performed with human observers, this is time-consuming and tedious. Model observers provide an easy-to-use in silico approach to perform such evaluation and identify methods for evaluation with human observers. Thus, multiple studies use model observers to evaluate imaging systems and methods.2.2.447 using LABROC4 program.48 The AUC measures the performance of methods on the task of defect detection. A higher AUC indicates better performance. We calculated the AUC with 95% confidence intervals (CIs) for SLAC, CTAC, and NAC methods.The test statistics generated were compared to a threshold to classify the image into the defect-present or defect-absent class. By varying the threshold, the ROC curve was plottedTo assess the performance of our method using visual fidelity-based criterion, we calculated root mean-square-error (RMSE) and structural similarity index (SSIM) with CIs between images obtained using the SLAC and the CTAC method, as well as between images obtained using the NAC and the CTAC method.3.3.1p < 0.05) the NAC method.3.23.34.This study represents a step along the pathway for developing a method for AC in SPECT without requiring a transmission scan. Our results demonstrate that the proposed SLAC method did not just yield activity images that were visually similar to those yielded by the CTAC method  , but als53 More specifically, it has been quantitatively shown that list-mode data contains more information compared to binned data for the task of estimating attenuation coefficients.27 Thus, advancing the method to process data in list-mode format directly may lead to even more improved performance.Our evaluation results motivate evaluation of the SLAC method with data from different scanners and from different institutions to assess for generalizability. Another area of future research is advancing the method to use list-mode data instead of sinogram data to reconstruct the attenuation map. Previous studies have shown that processing data in list-mode format in SPECT can yield improved performance on clinical tasks compared to processing data in binned format.55 Integrating a more advanced segmentation method is a future direction for improving the performance of the SLAC method. Second, we used a model observer study to evaluate the defect detection performance of the SLAC method. While the observer we used has been shown to mimic human observer performance, ideally this study should be conducted using human observers, such as experienced radiologists. The results from the model observer study motivate the evaluation of the method using human observers. Furthermore, the test dataset only consisted of defects located in the inferior LV wall. A future research direction is to incorporate defects at multiple locations in the test dataset while performing the observer study.There are some limitations to this study. First, the performance of the SLAC method is dependent on the quality of training data, including the segmentation of CT images. We used a Markov random field-based method to segment the CT images. However, there are other methods, including DL-based methods, which may yield segmented low-dose CT images with better segmentation performance.In conclusion, a scatter window projection and deep learning-based transmission-less AC method for SPECT yielded similar performance compared to the standard CTAC method, as evaluated on the clinically relevant task of detecting myocardial perfusion defects with a model observer in a retrospective evaluation study.


COVID-19 can induce neurological sequelae, negatively affecting the quality of life. Unravelling this illness's impact on structural brain connectivity, white-matter microstructure (WMM), and cognitive performance may help elucidate its implications. This cross-sectional study aimed to investigate differences in these factors between former hospitalised COVID-19 patients (COV) and healthy controls. Group differences in structural brain connectivity were explored using Welch-two sample t-tests and two-sample Mann–Whitney U tests. Multivariate linear models were constructed (one per region) to examine fixel-based group differences. Differences in cognitive performance between groups were investigated using Wilcoxon Rank Sum tests. Possible effects of bundle-specific FD measures on cognitive performance were explored using a two-group path model. No differences in whole-brain structural organisation were found. Bundle-specific metrics showed reduced fiber density  and fiber density cross-section  in the motor segment of the corpus callosum in COV compared to healthy controls. Cognitive performance on the motor praxis and digit symbol substitution tests was worse in COV than healthy controls . Associations between the cognitive performance and bundle-specific FD measures differed significantly between groups. WMM and cognitive performance differences were observed between COV and healthy controls.  Depending on the severity of the infection, the disease can cause persisting symptoms that continue to be present beyond the initial ailment, such as respiratory distress, fatigue, cognitive dysfunctions, and neurological sequelae9. The origin of the central nervous system involvement related to COVID-19 can be attributed to the virus operating via perineural invasion in olfactory mucosa10. The virus' spike protein binds to angiotensin-converting enzyme 2 (ACE-2) receptors on the nasal respiratory and olfactory cells10. It then spreads into the olfactory and sensory neurons within the central nervous system11. The ACE-2 receptor is present on vascular endothelial cells and smooth muscle cells in all body organs. SARS-CoV-2 binds to the ACE-2 receptor of the vascular endothelium, suppresses angiotensin (1–7) of the vascular smooth muscle, and causes oxidative stress after crossing the blood–brain barrier11. In turn, this reaction might lead to hypertension and ischemic cerebrovascular lesion overexpression, which reduces the functionality of the central nervous system by causing neuropathological changes and which may negatively impact the patient’s quality of life13.During the last 3 years of the COVID-19 pandemic, over 662 million people have been infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)14. Longitudinal diffusion-weighted magnetic resonance imaging (dMRI) studies have identified brain alterations following COVID-19, including changes in local white matter microstructure and structural whole-brain reorganisation15. Using diffusion tensor imaging (DTI), decreased mean, axial, and radial diffusivity, combined with increased fractional anisotropy (FA) in the corona radiata external capsule and superior frontal-occipital fasciculus has been found in former COVID-19 patients at the 3-month rehabilitation time-point compared to healthy controls16. Furthermore, a DTI tractography analysis showed widespread reduction in fibre bundle volume, length, and mean FA in the association, commissural, projection, and limbic fibre bundles at three months of recovery 17. Using dMRI-based graph theory, Tassignon et al. (2023) identified changes in structural organisation among hospitalised COVID-19 patients, with the characteristic path length decreasing over time following the three months after hospital discharge18. However, no comparison with healthy controls was made18. Further research is therefore needed to investigate whether similar changes in the structural whole-brain organisation can also be detected cross-sectionally compared to healthy controls and whether these alterations align with previous research.Roughly one-fifth of the people with COVID-19 are estimated to develop specific (e.g. headache and fatigue) or non-specific neurological sequelae (e.g. cerebrovascular disorders and other CNS disorders)16. Cognitive dysfunctions entail problems with the executive function, including reasoning, problem-solving, spatial planning and target detection19. The cognitive problems are worrisome, as impaired cognitive functioning can lead to poor work-related and functional outcomes, and an increased risk of cognitive deterioration at a later stage of life22. However, there appear to be signs that impaired cognitive function and brain structure may partially recover over time24. Nevertheless, impaired cognitive functioning resulting from a SARS-CoV-2 infection may affect the perception–action coupling and alter the motor response or motor behaviour25. Alterations to the motor connectome could possibly unravel this diseases’ underlying mechanism and explain the cognitive dysfunctions. To the best of our knowledge, no studies on the motor connectome and its impact on cognitive functioning are available. Therefore, this study focuses on probing the white matter fibre microstructure and, more specifically, the fibers related to motor functioning (i.e. motor-related tracts).Changes in FA and global mean diffusivity of white matter and changes in grey matter volume, reported in both the acute phase of infection and in recovered patients have been correlated with cognitive disorders26. This leads to problems with interpretation, limited biological specificity of associated metrics, and detrimental effects on processing techniques such as tractography 29. The fixel-based statistical analysis paradigm was created to solve these issues31. Constrained spherical deconvolution (CSD) was developed to allow for multiple fiber directions, based on which the fixel-based statistical framework was made to allow for a replacement of the voxel-based statistics32. A "fixel" is an individual fiber population within a voxel that allows fibre-specific measures to assess white matter characteristics and changes in this setting31. These measures include fiber density (FD), fiber cross-section (FC) and fiber density and cross-section (FDC)27. FD refers to the concentration of nerve fibers (i.e. axons) per fixel27. FC refers to the shape and size of a nerve fiber, which provides information about its gross/macroscopic structure. FDC is a modulated measure that constitutes the product of the FD and FC, providing insights into the underlying microscopic and macroscopic structure of white matter fibers27. To the best of our knowledge only one study using fixel-based analysis has been published so far to explore changes in the white matter microstructure following a COVID-19 infection33. This study found a reduction of fibre density in recovered COVID-19 patients compared to healthy controls in the posterior genu and rostral body of the corpus callosum, the arcuate fasciculus, the cingulum, the fornix, the inferior frontal-occipital fasciculus, the inferior and superior longitudinal fasciculus, the uncinate fasciculus, the corona radiata, and corticospinal tracts.To date, white matter brain microstructural alterations following a SARS-CoV-2 infection have been investigated using DTI. However, seeing that DTI assumes a single dominant fiber direction per voxel, its application disregards the presence of multiple fiber directions, estimated to be present in up to 90% of voxels at practical imaging resolutions18, we expect a reduced FD and potentially reduced FC and FDC in the COVID patients compared to healthy volunteers. The third aim is to investigate the difference between COVID-19 patients and healthy individuals regarding cognitive performance. We hypothesise that cognitive performance will be worse in COVID-19 patients than in healthy individuals. The fourth and final aim is to investigate whether bundle specific FD measures explain differences in cognitive performance for COVID-19 patients and healthy individuals.Unravelling the neural basis and impact of COVID-19 on the whole-brain structural organisation, white matter microstructure, and cognitive performance may further elucidate this illness's neurocognitive implications. Therefore, the aim of this cross-sectional controlled trial is fourfold. The first aim is to investigate the differences in whole-brain structural connectivity between COVID-19 patients and healthy volunteers who were assumed to have never had a COVID-19 infection, based on graph-theoretical measures derived from diffusion MRI. We hypothesise that structural brain connectivity will be unfavourable in COVID-19 patients compared to healthy individuals. The second aim is to investigate white matter microstructure differences related to motor functioning between COVID-19 patients and healthy individuals using a fixel-based analysis (FBA). Based on findings from DTI studies34, was approved by the Medical Ethics Committee of the University Hospital of the Vrije Universiteit Brussel (B.U.N. 1432020000338) and was registered on ClinicalTrials.gov Protocol Registration and Result System (NCT04726176).A cross-sectional single-centred case–control study was performed at the University Hospital of Brussels  between January and March 2021. The study was executed in compliance with the Declaration of HelsinkiTwenty patients with clinical signs of COVID-19 pneumonia were included in this study. Patients were eligible to participate if they had a positive reverse transcriptase-polymerase chain reaction test (RT-PCR) and were hospitalised at the UZ Brussel. A radiology resident performed patient recruitment in collaboration with the department of infectious diseases. The Intensive Care Unit and Infectiology Department created a list of all hospitalised COVID-19 patients at the UZ Brussel to facilitate the recruitment. An infectious disease specialist contacted the patients on the list by phone or asked about the patient’s willingness to participate during a follow-up consultation. Patients who expressed their willingness to participate were contacted by the radiology resident to further explain the study protocol. In addition to the cohort of patients with COVID-19, a control group of 18 individuals  was included and was age- matched to the patient cohort. Individuals who never had a symptomatic COVID-19 infection were eligible for participation. The recruitment of these individuals was conducted by means of convenience sampling and through the network of the co-authors. All participants provided their written informed consent after being informed both verbally and in writing regarding the study protocol.2). Characteristics of the different brain imaging techniques applied are available in the paper by Tassignon et al. (2023)18.All tests and measurements were conducted at the department of Radiology-Magnetic Resonance (UZ Brussel). Participants underwent a Magnetic Resonance Imaging (MRI) brain scan and cognitive test battery. The patients with COVID-19 underwent the MRI and cognitive test battery upon hospital discharge. The MRI brain scan was acquired in a supine position using a 3 T MRI Ingenia scanner with a 32-channel head coil . The protocol for both cohorts consisted, among others, of an 3D-T1 weighted spin-echo images, dMRI , was used to derive SIFT2-reweighted, anatomically constrained whole-brain probabilistic tractograms with single-shell single-tissue CSD37. Grey matter parcellation was performed with Freesurfer 6.0.0. using the Desikan-Killiany atlas38. Next, as detailed by Tassignon et al. (2023), structural connectomes were generated from the SIFT2-reweighted tractograms and the parcellated T1 images18. Measures of local efficiency, global efficiency, characteristic path length and cluster coefficiency were calculated and normalized against the mean of 100 equivalent random networks.Image pre-processing, structural connectome construction were executed as described by Tassignon et al. (2023)31, and detailed in the MRtrix3 user guide39. Briefly, fiber orientation distribution (FOD) maps were calculated for each subject based on group averaged response functions for anisotropic tissue (white matter) and isotropic tissue . Subject FOD maps were then normalized over the two tissue types and used to generate population averaged (study-specific template) FOD images. Fixels were generated from the template FOD maps and for each individual subject’s FOD maps after warping to template space. Individual subject fixels were assigned to the template fixels, then the CSD-derived metrics FD, FC, and FDC were calculated for each subject. A whole brain tractogram with 20 million streamlines was generated from the template FOD maps and filtered with SIFT to 2 million streamlines. This tractogram was used to define a fixel-to-fixel connectivity matrix, and to define a fixel mask with at least 150 streamlines per fixel. All fixel maps were smoothed based on a sparse fixel-to-fixel connectivity matrix. Finally, statistical analysis compared FD, FC , and FDC measures between COVID-19 patients and healthy controls with fixelcfestats30. Fixelcfestats controls for familywise error rate using permutation testing to control for multiple testing30. Total intracranial volumes calculated by FreeSurfer were used as regressors of noninterest while comparing FC and FDC measures between the two groups.Single-shell two tissue FBA followed the steps described by Raffelt et al. 201740 in native subject space, and resulting tractograms were warped to the fixel template space, to sample the individual subject mean FD, FC, and FDC within the traversed fixels.Bundle specific tractography was carried out using the KUL_FWT pipeline41. The test battery is compiled out of 7 tests, including the motor praxis test, visual object learning test, abstract matching, line orientation test, digit symbol substitution test, balloon analogue risk test, NBACK and psychomotor vigilance test. These tests measure sensorimotor speed, spatial learning and memory, abstraction, spatial orientation, complex scanning and visual tracking, risk decision-making, working memory, and vigilant attention. Participants practised each cognitive test once to mitigate learning effects. For this study the primary outcome of interest was the median reaction time on the motor praxis, digit symbol substitution and psychomotor vigilance tests.The computerized cognitive test battery "Cognition"  was conducted using an iPad. The test battery has an average duration of approximately 18 min, is sensitive to multiple domains at high-level cognitive performance and has been proven to engage specific brain regions, evidenced by functional neuroimaging42. A p-value below 0.05 was considered statistically significant. To investigate the first aim, the difference between COVID-19 patients and healthy controls in structural brain connectivity was assessed by means of the Welch-two sample t-test or, when the assumptions were not met, by the non-parametric Wilcoxon rank sum exact test. In particular, group differences in terms of the clustering coefficient and global and local efficiency were inspected parametrically. The characteristic path length was examined non-parametrically because of non-normality . No corrections for multiple testing were applied.All statistical analyses were performed using R 43 and lmerTest44 packages in R. All assumptions were checked and fulfilled. First, multivariate results were examined, after which univariate parameter estimates were inspected. Hedge’s g were calculated as it is a standardized mean difference measure that adjusts for potential bias in the estimation of the effect size due to small sample sizes. For the third aim, the assumptions for performing a MANOVA were checked but not fulfilled . Therefore, Wilcoxon rank sum exact tests were used to analyse group differences in cognitive performance on the motor praxis, digit symbol substitution and psychomotor vigilance tests. Wilcoxon test effect sizes  were calculated and are provided in the results section. No corrections for multiple testing were applied.For the second aim, whole-brain FBA was conducted to examine differences between COVID-19 patients and healthy controls by means of whole-brain FD, FC and FDC Fig. . Subsequ45. Multicollinearity between the different variables was evaluated. The correlations between the left whole pyramidal tract and left corticospinal tract (r = 0.94) and between the corresponding right measures (r = 0.94), were almost perfect, signifying redundant variables. Consequently, the left and right corticospinal tracts were dropped from the model. The robust maximum likelihood estimator was used. A stepwise model building approach was adopted. In the first model, all hypothesized paths were modelled, and paths with p-values > 0.10 in both groups were removed, resulting in a good fitting model. We manually defined parameters to test whether the parameter estimates differ significantly between both groups using Wald tests. Before interpreting the parameter estimates, we evaluated how well the proposed model fits the data using the following cut-offs. First, if the Chi-square test is non-significant, the model fit is considered acceptable as the observed covariance matric is deemed similar to the model implied covariance matric. It is advised that the Comparative Fit Index exceeds 0.90 or, preferably, 0.9546. For the Tucker Lewis Index, a value between 0.90 and 0.95 is considered as a marginal fit, and values exceeding 0.95 represent a good fit47. Concerning the Root Mean-Square Error of Approximation a value below 0.04 describes a good fit and below 0.08 a moderate fit48. Values of the Standardized Root Mean Square Residual exceeding 0.10 are indicative of a poor fit48. Statistical analyses and graphical representations were made using several R-packages54.For the fourth aim, two-group structural equation modelling was used to unravel the associations between cognitive performance and white matter microstructure and whether these associations differ between the healthy controls and the COVID patients. Specifically, a two-group path model, grouping on participant type , was constructed using the lavaan package (version 0.06-11) in RTable Figure (Free Water Elimination) < 0.05 were considered borderline significant. FD showed minor differences in the corpus callosum and subcortical white matter of the left precentral gyrus , FC showed minimal fixel differences in the left cerebral peduncle, and FDC showed more prominent differences in the left pyramidal tract, corpus callosum  and subcortical white matter .Figure Figure The multivariate ANOVA model showed a significant association between group and the corpus callosum  = 0.771, p = 0.042). Univariate inspection revealed only a significant association between group membership and the motor segment of the corpus callosum with a large effect size . Despite the small sample size, medium to large effect sizes were found on the difference in cognitive performance for the motor praxis test and digit symbol substation test. Performance on the psychomotor vigilance test did not differ between COVID-19 patients and healthy controls .Table New parameters were constructed. Here, a positive estimated difference denotes that the estimate of the COVID-19 patients was lower than that of the healthy controls. A negative estimated difference implies that the estimate of the COVID-19 patients score was higher than that of the healthy controls.Comparing COVID-19 patients and healthy controls, we observed significantly different associations between the motor praxis test and the motor segment of the corpus callosum  and macroscopic cross-sectional axonal size compared to healthy controls, respectivelyThe associations between the microstructural brain structure (i.e. fiber density) and performance on the cognitive tasks were examined using structural equation modelling. Significant differences in associations were found between the performance on the motor praxis test, and the motor segment of the corpus callosum, the left and right whole-pyramidal tracts and the right medial lemniscus in COVID-19 patients compared to the group of healthy controls Table . The ass33. These reductions in fibre density were also correlated with a worse outcome for COVID-19 patients on reaction time and visual memory tests33. These results are consistent with our results. However, caution is needed when comparing our results to those of Bispo et al. Their results might be less specific to the white matter microstructure because their acquisition protocol constituted only 32 directions with a b-value of 800 s/mm233. This is below the advised b-value > 2000s/mm2 for CSD, resulting in lower angular resolution and less specificity for restricted diffusion within the axons55. The population difference between the two studies must also be pointed out. Bispo et al. (2022) mainly included non-hospitalised individuals tested three months after their COVID-19 recovery, while the present study included hospitalised patients tested one month after recovery33. This prompts the question about the influence of hospitalisation length and related disease severity on the effect of COVID-19 on brain structure and cognitive performance56. Consequently, more research is needed to determine whether experiencing a COVID-19 infection affects the brain structure and cognitive performance while accounting for hospitalisation-related factors such as the degree of hospitalisation  and hospitalisation length.Bispo et al. found a reduction of fibre density in recovered COVID-19 patients compared to healthy controls in the posterior genu and rostral body of the corpus callosum, the arcuate fasciculus, the cingulum, the fornix, the inferior frontal-occipital fasciculus, the inferior and superior longitudinal fasciculus, the uncinate fasciculus, the corona radiata, and corticospinal tracts63. Future COVID-19 studies should account for the influence of vaccination status as a possible confounding factor. A study limitation is our small sample size. Therefore, we attached more significance to the visualisations (i.e. data distributions) and effect sizes than to the p-values. In our analyses, we also neutralised this limitation using statistical tests and models designed to control for a small sample size. Additionally, the small sample size limits performing a covariates analysis to correct for possible baseline differences, despite the study groups being matched by age. Future research should implement such analysis to acquire a more comprehensive understanding of the groups. Furthermore, caution is needed when interpreting the results of the white-matter microstructure. Due to the limited sample size, we could not control for brain-size scaling effects. Therefore, we decided not to include the FC and FDC in our last hypothesis (i.e. associations), because of to their intrinsic relation to intracranial volume31. Inclusion of these measures in future studies might shed a broader light on the effects of COVID-19 on white-matter macro-structure. A last limitation comprises our cross-sectional study design. This design limits us from examining the differences between COVID-19 patients and healthy controls at one given point in time. Future research should include the longitudinal follow-up of brain structure and cognitive performance in COVID-19 patients compared to healthy controls to unravel interactions between brain structure and cognitive performance over time64.A strength of this study is that both groups were homogeneous regarding vaccination status, as no vaccination was possible at the time of the data collection, thus limiting the selection and information bias. Hence, vaccination status has no confounding effectThis study found no differences in whole-brain organisation in recovered hospitalised COVID-19 patients compared to healthy controls but did find a difference in fiber density and fiber density cross-section of the motor segment of the corpus callosum. Recovered hospitalised COVID-19 patients also showed a lower cognitive performance in terms of sensory-motor speed, complex tracking and visual scanning. Cognitive performance and white-matter microstructure appeared to be associated and significantly differed between COVID-19 patients and healthy controls. Future research is needed to further investigate this association and its underlying mechanisms and investigate how cognitive performance and microstructural brain changes evaluate over time.


With many US states and localities enacting policies that restrict flavored e-cigarette sales, evaluation of these restrictions is critical to inform future efforts. This study analyzed both survey and retail scanner data to assess early-stage impacts of flavored tobacco sales restrictions in Massachusetts and New York State on e-cigarettes sales and product use among young people.This study uses state-level e-cigarette retail sales data and survey data from youth and young adults (aged 13–24 years). Cross-sectional surveys were conducted at two time points in Massachusetts (both post policy implementation) and New York (pre and post policy implementation); retail sales data in both states were analyzed from 2019 through 2020 and compared to sales in control states.E-cigarette unit sales decreased significantly following the implementation of statewide restrictions on flavored e-cigarettes in both Massachusetts and New York State (p<0.001). Survey data showed a decrease in mint flavored e-cigarette use in Massachusetts and an increase in tobacco flavored e-cigarette use in New York State over time (p=0.001). In both states, a greater proportion of respondents reported using disposable e-cigarettes at Time 2 compared to Time 1 (p=0.001). Among those who reported using fruit-flavored e-cigarettes in New York State, a significantly greater proportion reported disposable device use at Time 2 compared to Time 1 (p=0.004).Findings from these case studies from two US states suggest that statewide policies reduce the availability of e-cigarettes and have the potential to reduce use of many youth-appealing flavors. The increase in use of disposable e-cigarettes likely reflects existing loopholes in federal policy, which may be attenuating the potential impact of strong state-level policies. Most young people report that the first tobacco product they used was flavored2 and our most recent national studies continue to show that most youth reporting use of e-cigarettes report using flavored products4.There is strong evidence that flavors attract young people to tobacco and nicotine products. For example, two-thirds of youth who use tobacco products reported flavors5. E-cigarettes quickly became the most commonly used tobacco product among young people with 14.1% of high school students and 3.3% of middle school students reporting past 30-day e-cigarette use in 2022. Intensity of use is a concern, 46.0% of high school and 20.8% of middle school students who vaped reporting using e-cigarettes 20 or more times in the past thirty days. Further, 85.5% of high school students and 81.5% of middle school students who use e-cigarettes had used a flavored e-cigarette product3, with the most common products flavored like fruit, mint, and candy, highlighting the important role flavors have in youth e-cigarette use.E-cigarettes first entered the US marketplace in 2007, and by 2017, e-cigarettes were available in more than 15500 unique flavors7. The initial growth in youth e-cigarette use has largely been attributed to the product JUUL, a device with flavors such as mango and mint which are popular among high school students and the first e-cigarette known to deliver nicotine salts, which allow for high levels of nicotine to be consumed with less harshness than freebase nicotine8. In response to a growing outcry for the Food and Drug Administration (FDA) efforts to limit flavors in e-cigarettes, JUUL voluntarily removed all fruit and dessert flavored products from online and retail markets by November of 2018, followed by the removal of all mint flavored products in October of 20199. However, the market quickly evolved to meet the consumer demand for flavored products, and sales of menthol JUUL pods grew10, along with sales of fruit flavors from other e-cigarette brands11.Given nicotine’s unique effects on the adolescent brain including propensity for future drug abuse, dysfunction of emotional regulation, and cognitive deficits, as well as increased likelihood of continued and future tobacco use, use of e-cigarettes among young people is a serious public health concernFederal action, restricting flavored tobacco products to prevent youth use, contains exemptions that threaten public health. The FDA issued national guidance that prohibits the sale of flavored refillable e-cigarette cartridges other than tobacco or menthol in 2020. However, this policy excluded disposable e-cigarettes or e-liquids, which has led to a significant increase in sales of flavored disposable e-cigarettes.13. As of 31 March 2023, 388 local and state policies restrict the sale of flavored tobacco products13.In the absence of comprehensive federal oversight of flavored tobacco products and of flavored e-cigarettes in particular, state and local jurisdictions began to restrict the sales of all flavored tobacco products to address the growing e-cigarette epidemic15. The law went into effect immediately for flavored e-cigarettes, and on 1 June 2020 for all other flavored tobacco products15. Subsequently, New York State passed a statewide ban on flavored e-cigarettes that went into effect on 18 May 202016. While the Massachusetts and New York State policies are similar, they differ in the scope of their product bans; Massachusetts bans all flavored tobacco products whereas New York State prohibits the sale of flavored e-cigarettes, but not other flavored products like little cigars, cigarillos and menthol cigarettes, and exempts flavored products that have an FDA pre-market approval17.In November 2019, Massachusetts became the first state to pass a law prohibiting the sale of all flavored tobacco products, including flavored e-cigarettes, menthol cigarettes and flavored cigars, except for on-site consumption at smoking bars, a class of licensed establishments with enclosed indoor spaces such as cigar bars or hookah bars12. Several recent studies have assessed the impact of the state and local flavor policies across the US22. Compliance with such policies appears to be high with fewer flavored products available in stores22 assessed with retail sales data, environmental scans and research with retailers23. Survey data findings are mixed; even with strong policies and compliance, youth who use e-cigarettes may still be able to access flavored products in California18 and ever and current use has not changed24. However, work coming out of Minnesota indicates that youth use of flavored products may be stalled in areas with policies, compared to increasing in areas without policies20. Despite recent research, additional evidence is needed to fully understand the short- and long-term impacts of state and local flavored e-cigarette policies. Using both retail sales data and online survey data to assess the impact of flavor policies, we examined product sales and tobacco use behavior including use of flavors in two states. We also examined device type over time. While neither statewide policy explicitly regulated device type, the 2020 FDA guidance included differential regulations for device types, and prior studies have found certain device types are more likely to be sold in flavors other than tobacco25.Although the evidence is still emerging on the impact of these policies, a recent systematic review of the published studies found that flavored and menthol tobacco product sales restrictions implemented in US jurisdictions have achieved some of their intended outcomes. The evidence was strongest for proximal impacts of reduced access and reduced consumption  of flavored tobacco productsRetail sales dataData were purchased from Information Resources, Inc. (IRI) and were aggregated by states in 4-week periods from 27 January 2019 to 27 December 2020. E-cigarette unit sales and product characteristics are captured at the point of sale from the Universal Product Code (UPC), a bar code designation for consumer products. Data provide state-representative sales from brick-and-mortar convenience stores, gas stations, grocery stores, drug stores, club stores, discount/dollar stores, mass merchandisers, and military commissaries. Data do not include sales from tobacco stores, vape shops and online retailers.Web survey data26. Invitations were sent through survey vendors to participants enrolled in Ipsos Public Affairs online survey panels and verified by home zip code. The survey study protocol was approved by Advarra Institutional Review Board (Protocol 00041490) on 23 January 2020. Parents of youth participants (aged 13–17 years) provided informed consent and the youth provided assent to participate. Young adults (aged 18–24 years) provided informed consent to participate. Participants received an incentive for completing the survey, generally between $10 and $15 with participants living in a house without internet access and participants from underrepresented backgrounds receiving higher incentives.Repeated, cross-sectional online surveys were conducted at two time points. The survey used convenience sampling of respondents aged 13–24 years living in Massachusetts and New York State. Sampling quotas for age and gender were employed and post-stratification weights using Census demographic benchmarks were applied to resemble the population characteristics in each geographical area. This approach is commonly used for convenience sample internet panel surveysSurvey fielding dates relative to policy implementation can be found in Retail sales data27. E-cigarette products were classified as tobacco-flavored if tobacco or a descriptor  was explicitly mentioned in the name of the product’s flavor. Flavors with a cooling sensation, such as ‘frost menthol’, were classified as menthol. Mint-flavored e-cigarettes, such as ‘wintergreen’, were classified as mint. The all-other flavor category included flavors such as fruit, clove/spice, candy/desserts/other sweets, chocolate, alcoholic drinks, and non-alcoholic drinks. Flavors that could not be readily identified (e.g. ‘Fusion’) were searched online using brands and product characteristics, then assigned to tobacco, menthol, mint, or all other flavors, based on their descriptions if possible.E-cigarette flavors were categorized as tobacco, menthol, mint, all other flavors, and not available/applicable based on explicit flavor namesFurthermore, all e-cigarette products were categorized as prefilled cartridges, disposable devices, or e-liquids. Tanks, cartridges, and pods used in rechargeable and reusable e-cigarette devices were classified as prefilled cartridges. Devices that are not intended to be reused or refilled were classified as disposable devices. E-liquids reflect those products which are containers of the liquid used in e-cigarette devices. Devices and accessories sold without e-liquids were excluded.Web survey dataCharacteristicsDemographic characteristics including age , race and ethnicity , gender  and perceived financial situation  were assessed.Flavor useRespondents who had used an e-cigarette in the past 30-days (current) were asked to select all flavors they used in the past 30-days. Current flavors used were categorized into categories of tobacco, menthol, mint, fruit, and other . Since these flavor categories were ‘select all that apply’, respondents could use multiple flavor categories.Device typeRespondents who had used an e-cigarette in the past 30-days (current) reported the device type of e-cigarette used most often in the past 30-days: a disposable e-cigarette, an e-cigarette that uses pre-filled or refillable pods or cartridges (pod mods), or an e-cigarette with a tank that can be refilled with liquids (tanks/mods).Retail sales data28. Additionally, some localities within states have restricted flavored e-cigarette sales which may contaminate the control group and bias the results. Timing of policy implementation, however, varied across localities within states which complicates the ability to exclude data at the state-level, based on a common date. As a sensitivity analysis, sales data in states with local flavor restrictions were excluded during 2020, given that most of the restrictions on flavored e-cigarette sales have begun in 2020. These states include California, Colorado, Georgia, Illinois, Maine, Ohio, and Oregon.Trends in e-cigarette sales in Massachusetts and New York State from January 2019 to December 2020 were assessed overall, by flavor, and product type relative to sales in control states before and after policy implementation  and 3. S28. Due to low unit sales, e-liquid sales were not stratified and compared pre and post policy. However, these e-liquids were included in the calculation of total unit sales. Percentage changes along with 95% Confidence Intervals (CIs) were calculated. Regression analyses were used to assess the significance of changes in sales within Massachusetts and New York State as well as differences between Massachusetts and New York State and control states. A simple regression analysis with a pre-post indictor was used to assess the significance of changes in sales over time within each intervention state. A p-value of the indicator variable was reported for each state. To compare each intervention state with control states, a simple regression analysis with a pre-post indicator, a control-intervention indicator, and an interaction term between these two indicator variables was conducted. The p-value of the interaction term was reported. Sales data analyses were conducted in Stata 17 with findings significant at p<0.05.The main outcome was standardized unit sales, where one unit reflects 5 prefilled cartridges or 1 disposable device or 1 e-liquid bottleRespondent dataData were weighted using US Census estimates to reflect the demographic profile of each state’s population. Analyses were conducted in Stata SE 15.1 . Pearson chi-squared tests were used to assess differences in e-cigarette use, flavors used, and device type between Time 1 and Time 2, in each state. All variables were reported and analyzed as frequencies. Differences in device type by flavor category were also examined from Time 1 to Time 2.Sales dataE-cigarette unit sales in Massachusetts decreased significantly following the implementation of statewide restrictions on flavored e-cigarettes in October 2019, a change that was immediate and sustained over time . In partIn contrast to Massachusetts, trends in control states varied by flavor and device type during this period when national policy allowed for the sale of flavored disposable cigarettes. Menthol flavored e-cigarette sales in control states increased significantly , while sales of mint and all other flavored e-cigarettes decreased significantly . There was no significant change in tobacco-flavored sales. By product type, prefilled cartridge sales in control states decreased significantly , while sales of disposal e-cigarettes  increased significantly during this period . This compares to over an 80% decrease in both disposable and prefilled cartridge sales in Massachusetts, where all flavored products were restricted.Survey findingsSurvey respondent characteristics across Time 1 and Time 2 are presented in Characteristics of e-cigarette product use are also presented in Sales dataE-cigarette unit sales in New York State decreased significantly following the implementation of a statewide ban on flavored nicotine e-cigarettes, excluding tobacco-flavored e-cigarettes. Specifically, the average 4-week period total e-cigarette unit sales decreased by 33.9% (95% CI: -42.1 – -24.5) from May to December 2020 compared to the period before implementation of flavored e-cigarette restrictions (January 2019 to May 2020) . E-cigarNew York State experienced a decline in total e-cigarette sales across all device types and flavors, with the exception of tobacco-flavored e-cigarettes. Notably, tobacco-flavored e-cigarette sales increased by 83.4% (95% CI: 67.4–101.0). Sales of tobacco flavored e-cigarettes decreased in control states during this period . The significant decline in menthol e-cigarette sales in New York State  stands in stark contrast to the increase in sales of menthol e-cigarettes in control states during the same period . Furthermore, while sales of disposal e-cigarettes increased significantly in control states during this period , both prefilled cartridge and disposal e-cigarette sales decreased in New York State.Sensitivity analysesExcluding states with local flavored e-cigarette restrictions from the control group did not meaningfully change the results. The direction and the significance of the trends were similar with some changes in magnitude. The results are reported in Supplementary file Table 1.Survey findingsNew York State survey respondent characteristics across Time 1 and Time 2 are presented in Approximately one in five youth and young adults in our sample currently used e-cigarettes at Time 1. A similar proportion was observed at Time 2. Use of tobacco flavored e-cigarettes (26.2%) was significantly higher at Time 2 compared to Time 1 . Respondents reported significantly higher use of disposable devices and lower use of tanks/mods at Time 2  compared to Time 1 . Among young people who reported use of tobacco-flavored e-cigarettes in the past 30-days, significantly more reported using disposables (32.6%) and pod mods (53.7%) at Time 2 compared to Time 1 . Additionally, among those who reported using fruit-flavored e-cigarettes, a significantly greater proportion reported disposable device use and lower use of pod mods at Time 2 (45.5%) than Time 1 (30.2%),  (p=0.004). Sample size, unweighted percentages and weighted percentages are included for both states in Supplementary file Table 1.29 where e-cigarette sales remained high during the study period, with increases in sales in menthol e-cigarettes as well as disposable devices which are still available nationally in a wide range of flavors.In the months following the implementation of state policies to prohibit the sale of flavored e-cigarettes, both Massachusetts and New York State experienced significant declines in total e-cigarette unit sales in tracked channels. In Massachusetts, this decline was immediate, with a low level of total sales primarily consisting of tobacco flavored e-cigarettes. In New York State, tobacco flavored e-cigarettes sales increased, suggesting a consumer shift to those e-cigarettes that were still available in the market. These states stand in contrast to the national trendsSurvey data from Massachusetts indicate a significant reduction in the use of mint flavored e-cigarettes among youth and young adults. In New York State, youth and young people reported greater use of tobacco flavored e-cigarettes, the only type of e-cigarettes still legally available in New York. This finding is consistent with the sales data for that state. In Massachusetts, we see a slight decrease in use of menthol flavored e-cigarettes during a period in which national sales of menthol e-cigarettes dramatically increased.30. Similarly, a study of the statewide flavor policy in New York found that while there were decreases in past 30-day e-cigarette use, cigarette, and dual use of e-cigarettes and cigarettes, were observed over the 2-year period in New York State post implementation; 95% of vapers still reported using a non-tobacco-flavored e-cigarette following the restriction31.These study findings are consistent with a recent study demonstrating high retailer compliance and large decreases in the sales of all flavored tobacco following policy implementation in Massachusetts33. In this study, we see significantly higher use of disposables use among young people who currently use e-cigarettes in New York State over time; disposables use also increased in Massachusetts. These findings suggest that as long as the larger national market includes flavored e-cigarettes regardless of device type, youth remain at risk of using these products even in states with restrictions in place. It is possible that purchasing behavior also shifted to online or vape shops, but these retailer types are not provided in the retail sales data used for this study. Collectively, these behaviors support the need for comprehensive policies to further limit access to all nicotine and tobacco products that present harms to youth and young adults.Findings provide insight into the immediacy of shifts in consumer behavior when flavor accessibility varies by device type. Both state policies were implemented during a period in which disposable e-cigarettes sales increased when they were exempted from the 2020 FDA Guidance which restricted flavored e-cigarettes. Data from the 2021 National Youth Tobacco Survey found that 53.7% of youth who currently use e-cigarettes reported the use of a disposable e-cigarette34; the majority of those who use e-cigarettes continue to comprise youth and young adults33. Second, retail sales data only include in-store purchasing for a set of store types; these data cannot provide information about trends in online purchasing or vape/tobacco store purchasing. The potential access through these sources has implications for policy change; comprehensive efforts that include all types of retailers are needed. Third, survey samples were drawn using convenience sampling methods which, despite the use of sampling quotas and weights to approximate the population characteristics in each geographical area, limit the representativeness of the sample and thus the generalizability of the findings. Additionally, sales, behavior, and policy implementation in Massachusetts and New York State may not be generalizable to other states. Massachusetts had significant support at the local level prior to the implementation of a state-wide policy with 170 local flavor restrictions before a statewide ban was implemented. The statewide policies passed in New York State and Massachusetts also included other tobacco policy provisions that may have influenced sales and self-reported use patterns. Finally, the observational and cross-sectional nature of the survey cannot rule out that findings could be attributable to factors other than the policy changes, and additional research with stronger longitudinal designs will be needed to more fully attribute policy impacts.This study has several limitations. First, the retail sales data do not include demographic information of purchasers’ age. Thus, sales data cannot explain purchase patterns by age categories. However, prior research shows that current use of e-cigarettes among adults remains relatively low 35. Prior studies have used retail sales indicators22 and survey data24 for early-stage policy evaluation. The present study builds on prior work by leveraging multiple data sources to create a more complete picture of flavored tobacco policy impacts.Nonetheless, these results signal some early and important sales and behavior shifts in the period following policy implementation in two states. Findings are consistent with other published studies that show that state and local tobacco restrictions produce a dramatic and immediate impact on sales of e-cigarettes36, this will be an important aspect of flavored tobacco policies to continue to monitor. Study findings reinforce the continued need to better understand access and patterns of use, and to address loopholes in the current regulatory environment at the federal level in order to maximize the impact of flavored tobacco sales restrictions.Future research is needed to examine the longer term behavioral impacts of these flavored tobacco policies and can leverage both ongoing surveys and health department surveillance efforts that measure state and local policy impacts. Research efforts should consider whether and to what extent state and local polices are implemented and enforced to more fully explain the impact of policy restrictions. Studies should also investigate unintended consequences of policy passage. While studies to date demonstrate that the Massachusetts policy was effective in reducing access to flavors without increasing sales in neighboring statesFindings from these case studies from two US states suggest that statewide policies reduce the availability of e-cigarettes and have the potential to reduce use of many youth-appealing flavors. The increase in use of disposable e-cigarettes likely reflects existing loopholes in federal policy which may be attenuating the potential impact of strong state-level policies.Click here for additional data file.


Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic. Although systemic steroids play an important role in treating patients with severe COVID-19, the role of inhaled corticosteroids in non–critically ill, hospitalized patients with COVID-19 remains unclear.We analyzed findings in non–critically ill, hospitalized patients with COVID-19 who were >18 years old and were admitted to 64 Japanese hospitals between January and September 2020. We performed propensity score matching analysis to evaluate 28-day and in-hospital mortality rates with or without inhaled ciclesonide within 2 days of admission. Sensitivity analyses using inverse probability weighting analysis, and generalized estimating equation method were also performed.Eligible patients (n = 3638) were divided into ciclesonide (n = 290) and control  groups. The 1-to-4 propensity score matching analysis included 271 ciclesonide users and 1084 nonusers. There were no significant differences between the 2 groups for 28-day  or in-hospital  mortality rates. The sensitivity analysis showed similar outcomes.From this multicenter observational study in Japan, inhaled ciclesonide did not decrease 28-day or in-hospital mortality rates in non–critically ill, hospitalized patients with COVID-19. Future large, multinational, randomized trials are required to confirm our results. At the end of 2019, the coronavirus disease 2019 (COVID-19) was identified as the cause of an outbreak of pneumonia in Wuhan, China . StrategThe Randomized Evaluation of COVID-19 Therapy (RECOVERY) trial showed that intravenous dexamethasone was associated with lower mortality in patients hospitalized for COVID-19 , and theCiclesonide is an inhaled corticosteroid delivered via a nonchlorofluorocarbon hydrofluoroalkane metered-dose inhaler. Ciclesonide is converted into an active metabolite and has beneficial effects in treating asthma . A previThe Japanese Multicenter Research on Coronavirus disease 2019 (COVID-19) by Assembling Real-world Data (J-RECOVER study) was a multicenter observational study conducted across 64 Japanese hospitals. The study protocol was reviewed and approved by the ethics committee or investigational review board of each participating center. The need for written informed consent was waived by the ethics committees of all the centers because the data were collected retrospectively. A research outline of the study protocol, including data collection, has been published . This reThe current study included patients with SARS-CoV-2 infection diagnosed by means of laboratory testing, who were discharged from each participating hospital between 1 January and 30 September 2020. SARS-CoV-2 was mainly the pre-alpha variant confirmed in Japan during this period . We obtaThe DPC data are administrative claims data obtained from approximately 8 million inpatients discharged per year from >1000 acute care hospitals in Japan. Because the DPC data are linked to a health insurance payment system, the attending physicians accurately recorded the diagnoses, including the main diagnosis and comorbid conditions. The DPC data included the following information on demographics and selected clinical information: admission and discharge, discharge status (live or dead), diagnoses at admission, comorbid conditions at admission, diagnoses after admission, surgical and other procedures performed, dates of surgical or other procedures, medications, dates when medications were administered, and special reimbursements for specific conditions during hospitalization .We identified the non–critically ill patients with COVID-19 during the study periods. Non–critically ill hospitalized patients were defined as hospitalized patients who did not receive mechanical ventilation support or enter the ICU within 2 days after admission, according to the definition of severity in COVID-19 according to the Ministry of Health, Labour and Welfare of Japan . We exclThe exposure of interest was whether patients received inhaled ciclesonide. The ciclesonide group included patients who received inhaled ciclesonide within 2 days of admission, whereas the control group included patients who did not receive the same. In Japan, physicians prescribe the drugs according to the prescription dose defined by the government. In ciclesonide, the usual dosage and administration for adults is 100–400 μg of ciclesonide once daily by inhalation. Although the ciclesonide dosage can be adjusted according to the patient's symptoms, the maximum daily dose is 800 µg.The variables assessed included age, age distribution, sex, race, body mass index , hospital type (academic or nonacademic), total number of beds in each hospital, dyspnea, median duration (in days) from symptom onset to admission, pneumonia at admission, and comorbid conditions at admission. Hospital bed information was divided into 3 groups according to interquartile range: <600 beds , ≥600 and ≦815 beds (≥25% and ≦75%), and 815 beds (75%). Pneumonia at admission was evaluated using chest radiography or computed tomography.Comorbid conditions at the time of admission were defined according to the Charlson comorbidity index and included myocardial infarction, congestive heart failure, cerebrovascular disease, chronic pulmonary disease, dementia, mild liver disease, malignancy, diabetes mellitus with or without chronic complications, rheumatic diseases, renal disease, and human immunodeficiency virus (HIV) infection. Several elements of the patient medical histories  were not included in this analysis because they were relevant to only a few or no patients. We investigated the following severity score and laboratory data; sequential organ failure assessment (SOFA) score, white blood cell count (in cells per microliter), C-reactive protein level (in micrograms per deciliter), and D-dimer level (in micrograms per milliliter). The SOFA score was used to evaluate the severity of COVID-19 at admission. This scoring system is established from 6 organ system categories  and is a good predictor of sepsis-related severity [We also investigated the following procedures performed within 2 days of admission: systemic steroids, antiviral therapy, antibiotics, anticoagulation, and oxygen support. Systemic steroid administration included oral and intravenous routes. Antiviral therapy included oral favipiravir and intravenous remdesivir. Antibiotics administered orally and/or intravenously were included. Anticoagulants included heparin, direct oral anticoagulants, and warfarin. Oxygen support included oxygen that was started within 2 days of admission.The main outcomes of this study were 28-day and in-hospital mortality rates. We also evaluated the following outcomes: proportion of patients transferred to other hospitals, proportion of patients requiring mechanical ventilation from 3 or more days after admission through discharge, and proportion of ICU admissions from 3 or more days after admission through discharge. The proportion of patients transferred to another hospital refers to transfer because the patient's symptoms worsened. In addition, we determined the proportions of deaths due to respiratory events such as worsening pneumonia or the proportions of deaths due to non-respiratory events in in-hospital mortality for each hospital.We performed 1-to-4 propensity score matching to adjust for differences in baseline characteristics between the 2 groups. To estimate the propensity score, the probability of a patient receiving ciclesonide was adjusted for potential confounders using the following characteristics: age, age distribution, sex, race, body mass index, hospital information, dyspnea, median duration from symptom onset to admission, pneumonia at admission, comorbid conditions at admission, systemic steroids, antiviral therapy, antibiotics, anticoagulants, and oxygen support within 2 days after admission. The C statistic was calculated to evaluate the goodness of fit. A 1-to-4 matched analysis using nearest-neighbor matching was performed based on the propensity scores. A match occurred when a patient in the ciclesonide group had an estimated score within 0.2 standard deviations of a score for a patient in the control group. We used absolute standardized mean differences to assess the balance between patient characteristics. Absolute standardized mean differences of <10% were considered negligible imbalances in baseline characteristics between the groups . DescripWe also used risk differences to compare outcomes between the groups. In addition, we performed sensitivity analyses using inverse probability of treatment weighting (IPTW) and the generalized estimating equation (GEE) method. We used logistic regression analysis fitted with a GEE-adjusted propensity score to investigate the association between ciclesonide and the outcomes by estimating odds ratios and associated confidence intervals (CIs) using methods appropriate for all included patients , 23. In A total of 4291 patients with COVID-19 were identified during the study period. Eligible patients were divided into a ciclesonide group (n = 290) and a control group (n = 3393). The 1-to-4 propensity score matching analysis included the ciclesonide group (n = 271) and the control group (n = 1084). The C statistic indicated that the goodness of fit was 0.709  in the propensity score model.After propensity score matching, there were no significant differences between the 2 groups for 28-day mortality rate , in-hospital mortality rate , proportion of transfers to other hospitals , the proportion requiring mechanical ventilation after 3 or more days of admission , or the proportion of ICU admissions after 3 or more days of admission . The proportions of in-hospital deaths due to respiratory events were 92% in the ciclesonide group versus 96% in the control group.2 = 0.16; P = .68).The risk differences in IPTW analysis were similar after propensity score matching . The KapIn this multicenter observational study involving 4291 hospitalized, non–critically ill patients with COVID-19, inhaled ciclesonide did not significantly decrease the 28-day mortality rate, the in-hospital mortality rate, the proportion of transfers to another hospital, the proportion of patients requiring mechanical ventilation after 3 or more days of admission, or the proportion of ICU admissions after 3 or more days of admission.There are multiple possible reasons why inhaled ciclesonide failed to improve mortality rates. First, it may be associated with a high risk of bacterial pneumonia exacerbation in patients with COVID-19. In several studies, inhaled steroid use has been shown to increase the risk of bacterial colonization and bacterial pneumonia , 25. A pSecond, inhaled ciclesonide may have low systemic bioavailability and may not have adequate antiviral or anti-inflammatory effects to improve the prognosis in patients with COVID-19. Although the antiviral mechanism of ciclesonide against SARS-CoV-2 remains unknown, some previous studies have shown that ciclesonide inhibits viral endoribonuclease, activated kinase, or the viral RNA replication-transcription complex, which might suppress viral replication . In addiThird, different inhaled corticosteroids may differ in their effectiveness. Two RCTs have investigated the effects of budesonide as an inhaled glucocorticoid in patients with COVID-19. The PRINCIPLE trial showed that budesonide was associated with improved time to recovery, lower rates of hospital admission, and fewer deaths . The STOFourth, the timing of ciclesonide initiation may be associated with these results. A previous study showed that initiation of ciclesonide within 3 days after diagnosis was associated with prevention of mechanical ventilation , and inhOur study has several strengths. First, our results are reflective of a real-world clinical setting and treatment strategies for COVID-19 in Japan. Second, many participating hospitals were tertiary emergency centers with an average number of beds >750 , and theOur study also has several limitations. First, it was a retrospective multicenter study, and confounding indications may have influenced the results. Second, our database did not include any information on long-term symptoms associated with COVID-19, and we did not know the effect of ciclesonide against long COVID syndrome . Third, In conclusion, in this multicenter observational study in Japan, inhaled ciclesonide did not reduce 28-day or in-hospital mortality rates in non–critically ill hospitalized patients with COVID-19. Future large, multinational, randomized trials are required to confirm our results.Open Forum Infectious Diseases online. 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This scientific research characterizes the recurrence pattern of patients with glioblastoma after trimodality treatment, neurosurgery and radiation therapy, either intensity modulated radiation therapy (IMRT) or volumetric intensity modulated arc therapy (VMAT), as well as concurrent chemotherapy in a clinical setting based on daily image-guided radiation therapy (IGRT) at the West German Cancer Center. This study underlines the importance of reconsideration of clinical target volume margins in the clinical routine. Larger radiation therapy margins may decrease the proportion of out-field recurrences, but an effect on overall survival is highly questionable.p < 0.0001). Overall survival of patients with in-and out-field recurrence was similar (p = 0.7053). The only prognostic factor significantly associated with out-field recurrence was multifocality of recurrence (p = 0.0037). Cumulative incidences of in-field recurrences at 24 months were 60%, 22% and 11% for recurrences located within a 10 mm margin, outside a 10 mm margin but within the 95% isodose, or outside the 95% isodose (p < 0.0001). Survival from recurrence was improved after complete resection (p = 0.0069). Integrating these data into a concurrent-risk model shows that extending margins beyond 10 mm has only small effects on survival hardly detectable by clinical trials. Conclusions: Two-thirds of recurrences were observed within a 10 mm margin around the GTV. Smaller margins reduce normal brain radiation exposure allowing for more extensive salvage radiation therapy options in case of recurrence. Prospective trials using margins smaller than 20 mm around the GTV are warranted.Background: While prognosis of glioblastoma after trimodality treatment is well examined, recurrence pattern with respect to the delivered dose distribution is less well described. Therefore, here we examine the gain of additional margins around the resection cavity and gross-residual-tumor. Methods: All recurrent glioblastomas initially treated with radiochemotherapy after neurosurgery were included. The percentage overlap of the recurrence with the gross tumor volume (GTV) expanded by varying margins (10 mm to 20 mm) and with the 95% and 90% isodose was measured. Competing-risks analysis was performed in dependence on recurrence pattern. Results: Expanding the margins from 10 mm to 15 mm, to 20 mm, to the 95%- and 90% isodose of the delivered dose distribution with a median margin of 27 mm did moderately increase the proportion of relative in-field recurrence volume from 64% to 68%, 70%, 88% and 88% ( The optimal target volume for radiotherapy of glioblastoma is unknown. Moreover, European and US target delineation concepts differ significantly ,2. AccorBased on a systematic institutional database search, all consecutive patients with newly diagnosed and histopathologically confirmed glioblastoma recurrence treated at the department of radiation therapy of the West German Cancer Center with conventional or hypofractionated radiation therapy after neurosurgical resection at age > 18 years were included in the time period from 1 January 2007 to 31 December 2021. The recurrence was confirmed histopathologically, by high-resolution MRI or/and PET/MRI and follow-up of at least 3 months. If IDH-mutation status was available, IDH-mutant WHO grade 4 astrocytoma were excluded. IDH-mutation analysis was performed immunohistochemically. A supratentorial component was obligatory. Cases with history of chemotherapy prior to GBM diagnosis were excluded. The study was conducted after the local ethics committee approval (20-9719-BO).2 ≥ 3 directions) and ADC-Maps (apparent diffusion coefficient) at a 1.5 or 3T MRI-scanner. Following the first cycles of adjuvant therapy, patients received regularly scheduled follow-up MRI scans. To differentiate tumor recurrence from post-therapeutic changes and to exclude possible confusion with pseudo-progression, we included only patients with histopathologically confirmed recurrence, or confirmed tumor progression on further follow-up MRI and/or PET/MRI. Radiation techniques changed over time. From 2000 to 2012, 3D conformal radiotherapy was used, from 2012 to 2018, static field IMRT was the preferred technique, and since 2018, non-coplanar VMAT has been the most widely applied technique. Three-dimensional planning was performed with the treatment planning system Eclipse . Reproducible positioning was achieved with a thermoplastic mask system. The EORTC 26981/22981 quality assurance statements were adopted FET/PET-uptake correlated with neovascularization and cellularity of glioblastoma . HoweverIn line with others, this study demonstrates that central in-field recurrences are the dominant cause of failure after standard postoperative radiochemotherapy of glioblastomas. The relapse pattern of glioblastomas can be moderately changed by dose escalation of radiotherapy, by temozolomide for MGMT promoter hypermethylated glioblastomas, or addition of TT-Fields towards more marginal or out-field recurrences ,41,42. HAssuming competing risks of recurrences at the different distances from the resection cavities observed in this study and using the time-dependent probabilities for the different events reveals that an effect on survival by expanding the radiotherapy target volume beyond 10 mm will unlikely be detectable by large randomised trials. Here, a moderate recurrence-delaying effect of radiotherapy in the expanded volumes of median 5 months is anticipated. Even assuming a 12-month delay, a long-term survival benefit was barely demonstrable from this study because of the high risk of in-field recurrence with a 10 mm margin.After neurosurgery, radiochemotherapy and chemotherapy tumor progression or recurrence may be difficult to differentiate from pseudo-progression. Therefore, a major strength of the present study was that only patients with histopathologically confirmed tumor recurrence or tumor progression confirmed on follow-up imaging, either by MRI and/or PET/MRI, were included.In the following, a range of limitations of the present study results shall be discussed. The Kaplan–Meier probabilities of freedom from relapse at the considered localizations reflect the average for the group of patients studied. However, there may be larger inter-individual differences. Patients suffering from out-field recurrences might have a lower underlying risk of in-field recurrences with an increased gain of a larger CTV margin. This study does not allow for subgroup analysis and subgroups behave differently. However, no subgroups were excluded from this cohort, and therefore this group of patients reflects our experience in real life with patients who have close imaging follow-up studies.The present results indicate that the classic GTV-CTV delineation strategy for postoperative RT of GBM should be reconsidered in favor of smaller CTV margins. While not significantly hampering tumor control, smaller CTV margins may allow for better protection of healthy tissue, thus ensuring patients’ quality of life and leaving therapeutic options for salvage RT in the yet unpreventable event of GBM recurrence. Prospective randomised clinical trials using smaller CTV margins are warranted to confirm these findings.