Persistent fatigue in post-acute COVID syndrome is associated with altered T1 MRI texture in subcortical structures: a preliminary investigation.

Post-acute COVID syndrome (PACS) is a global health concern and is often associated with debilitating symptoms. Post-COVID fatigue is a particularly frequent and troubling issue, and its underlying mechanisms remain incompletely understood. One potential contributor is micropathological injury of subcortical and brainstem structures, as has been identified in other patient populations. Texture-based analysis (TA) may be used to measure such changes in anatomical MRI data. The present study develops a methodology of voxel-wise TA mapping in subcortical and brainstem regions, which is then applied to T1-weighted MRI data from a cohort of 48 individuals who had PACS (32 with and 16 without ongoing fatigue symptoms) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19. Both groups were assessed an average of 4-5 months post-infection. There were no significant differences between PACS and control groups, but significant differences were observed within the PACS groups, between those with and without fatigue symptoms. This included reduced texture energy and increased entropy, along with reduced texture correlation, cluster shade and profile in the putamen, pallidum, thalamus and brainstem. These findings provide new insights into the neurophysiological mechanisms that underlie PACS, with altered tissue texture as a potential biomarker of this debilitating condition.

Task Activation Results in Regional 13 C-Lactate Signal Increase in the Human Brain.

Hyperpolarized- 13 C magnetic resonance imaging (HP- 13 C MRI) was used to image changes in 13 C-lactate signal during a visual stimulus condition in comparison to an eyes-closed control condition. Whole-brain 13 C-pyruvate, 13 C-lactate and 13 C-bicarbonate production was imaged in healthy volunteers (N=6, ages 24-33) for the two conditions using two separate hyperpolarized 13 C-pyruvate injections. BOLD-fMRI scans were used to delineate regions of functional activation. 13 C-metabolite signal was normalized by 13 C-metabolite signal from the brainstem and the percentage change in 13 C-metabolite signal conditions was calculated. A one-way Wilcoxon signed-rank test showed a significant increase in 13 C-lactate in regions of activation when compared to the remainder of the brain ( p = 0.02, V = 21). No significant increase was observed in 13 C-pyruvate ( p = 0.11, V = 17) or 13 C-bicarbonate ( p = 0.95, V = 3) signal. The results show an increase in 13 C-lactate production in the activated region that is measurable with HP- 13 C MRI.

Evidence of 13 C-lactate oxidation in the human brain from hyperpolarized 13 C-MRI.

PURPOSE: To test the hypothesis that lactate oxidation contributes to the 13 $$ {}^{13} $$ C-bicarbonate signal observed in the awake human brain using hyperpolarized 13 $$ {}^{13} $$ C MRI. METHODS: Healthy human volunteers (N = 6) were scanned twice using hyperpolarized 13 $$ {}^{13} $$ C-MRI, with increased radiofrequency saturation of 13 $$ {}^{13} $$ C-lactate on one set of scans. 13 $$ {}^{13} $$ C-lactate, 13 $$ {}^{13} $$ C-bicarbonate, and 13 $$ {}^{13} $$ C-pyruvate signals for 132 brain regions across each set of scans were compared using a clustered Wilcoxon signed-rank test. RESULTS: Increased 13 $$ {}^{13} $$ C-lactate radiofrequency saturation resulted in a significantly lower 13 $$ {}^{13} $$ C-bicarbonate signal (p = 0.04). These changes were observed across the majority of brain regions. CONCLUSION: Radiofrequency saturation of 13 $$ {}^{13} $$ C-lactate leads to a decrease in 13 $$ {}^{13} $$ C-bicarbonate signal, demonstrating that the 13 $$ {}^{13} $$ C-lactate generated from the injected 13 $$ {}^{13} $$ C-pyruvate is being converted back to 13 $$ {}^{13} $$ C-pyruvate and oxidized throughout the human brain.

Dose-Escalated Radiation Therapy Is Associated With Improved Outcomes for High-Grade Meningioma.

PURPOSE: The optimal modern radiation therapy (RT) approach after surgery for atypical and malignant meningioma is unclear. We present results of dose escalation in a single-institution cohort spanning 2000 to 2021. METHODS AND MATERIALS: Consecutive patients with histopathologic grade 2 or 3 meningioma treated with RT were reviewed. A dose-escalation cohort (≥66 Gy equivalent dose in 2-Gy fractions using an α/ß = 10) was compared with a standard-dose cohort (<66 Gy). Outcomes were progression-free survival (PFS), cause-specific survival, overall survival (OS), local failure (LF), and radiation necrosis. RESULTS: One hundred eighteen patients (111 grade 2, 94.1%) were identified; 54 (45.8%) received dose escalation and 64 (54.2%) standard dose. Median follow-up was 45.4 months (IQR, 24.0-80.0 months) and median OS was 9.7 years (Q1: 4.6 years, Q3: not reached). All dose-escalated patients had residual disease versus 65.6% in the standard-dose cohort (P < .001). PFS at 3, 4, and 5 years in the dose-escalated versus standard-dose cohort was 78.9%, 72.2%, and 64.6% versus 57.2%, 49.1%, and 40.8%, respectively, (P = .030). On multivariable analysis, dose escalation (hazard ratio [HR], 0.544; P = .042) was associated with improved PFS, whereas ≥2 surgeries (HR, 1.989; P = .035) and older age (HR, 1.035; P < .001) were associated with worse PFS. The cumulative risk of LF was reduced with dose escalation (P = .016). Multivariable analysis confirmed that dose escalation was protective for LF (HR, 0.483; P = .019), whereas ≥2 surgeries before RT predicted for LF (HR, 2.145; P = .008). A trend was observed for improved cause-specific survival and OS in the dose-escalation cohort (P < .1). Seven patients (5.9%) developed symptomatic radiation necrosis with no significant difference between the 2 cohorts. CONCLUSIONS: Dose-escalated RT with ≥66 Gy for high-grade meningioma is associated with improved local control and PFS with an acceptable risk of radiation necrosis.

Chemical Exchange Saturation Transfer MRI: What Neuro-Oncology Clinicians Need To Know.

Chemical exchange saturation transfer (CEST) is a relatively novel magnetic resonance imaging (MRI) technique with an image contrast designed for in vivo measurement of certain endogenous molecules with protons that are exchangeable with water protons, such as amide proton transfer commonly used for neuro-oncology applications. Recent technological advances have made it feasible to implement CEST on clinical grade scanners within practical acquisition times, creating new opportunities to integrate CEST in clinical workflow. In addition, the majority of CEST applications used in neuro-oncology are performed without the use gadolinium-based contrast agents which are another appealing feature of this technique. This review is written for clinicians involved in neuro-oncologic care (nonphysicists) as the target audience explaining what they need to know as CEST makes its way into practice. The purpose of this article is to (1) review the basic physics and technical principles of CEST MRI, and (2) review the practical applications of CEST in neuro-oncology.

Persistent post-COVID headache is associated with suppression of scale-free functional brain dynamics in non-hospitalized individuals.

INTRODUCTION: Post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) is a growing concern, with headache being a particularly debilitating symptom with high prevalence. The long-term effects of COVID-19 and post-COVID headache on brain function remain poorly understood, particularly among non-hospitalized individuals. This study focused on the power-law scaling behavior of functional brain dynamics, indexed by the Hurst exponent (H). This measure is suppressed during physiological and psychological distress and was thus hypothesized to be reduced in individuals with post-COVID syndrome, with greatest reductions among those with persistent headache. METHODS: Resting-state blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging data were collected for 57 individuals who had COVID-19 (32 with no headache, 14 with ongoing headache, 11 recovered) and 17 controls who had cold and flu-like symptoms but  tested negative for COVID-19. Individuals were assessed an average of 4-5 months after COVID testing, in a cross-sectional, observational study design. RESULTS: No significant differences in H values were found between non-headache COVID-19 and control groups., while those with ongoing headache had significantly reduced H values, and those who had recovered from headache had elevated H values, relative to non-headache groups. Effects were greatest in temporal, sensorimotor, and insular brain regions. Reduced H in these regions was also associated with decreased BOLD activity and local functional connectivity. CONCLUSIONS: These findings provide new insights into the neurophysiological mechanisms that underlie persistent post-COVID headache, with reduced BOLD scaling as a potential biomarker that is specific to this debilitating condition.

Segmentation of Brain Metastases Using Background Layer Statistics (BLAST).

BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases is important for treatment planning and evaluating response. The aim of this study was to assess the performance of a semiautomated algorithm for brain metastases segmentation using Background Layer Statistics (BLAST). MATERIALS AND METHODS: Nineteen patients with 48 parenchymal and dural brain metastases were included. Segmentation was performed by 4 neuroradiologists and 1 radiation oncologist. K-means clustering was used to identify normal gray and white matter (background layer) in a 2D parameter space of signal intensities from postcontrast T2 FLAIR and T1 MPRAGE sequences. The background layer was subtracted and operator-defined thresholds were applied in parameter space to segment brain metastases. The remaining voxels were back-projected to visualize segmentations in image space and evaluated by the operators. Segmentation performance was measured by calculating the Dice-Sørensen coefficient and Hausdorff distance using ground truth segmentations made by the investigators. Contours derived from the segmentations were evaluated for clinical acceptance using a 5-point Likert scale. RESULTS: The median Dice-Sørensen coefficient was 0.82 for all brain metastases and 0.9 for brain metastases of ≥10 mm. The median Hausdorff distance was 1.4 mm. Excellent interreader agreement for brain metastases volumes was found with an intraclass correlation coefficient = 0.9978. The median segmentation time was 2.8 minutes/metastasis. Forty-five contours (94%) had a Likert score of 4 or 5, indicating that the contours were acceptable for treatment, requiring no changes or minor edits. CONCLUSIONS: We show accurate and reproducible segmentation of brain metastases using BLAST and demonstrate its potential as a tool for radiation planning and evaluating treatment response.

ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI.

Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts from site to site due to variances in scanners and acquisition protocols. DNNs are famously susceptible to these distribution shifts in computer vision. Currently, there are no benchmarking platforms or frameworks to assess the robustness of new and existing models to specific distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we propose ROOD-MRI: a novel platform for benchmarking the Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and artifacts in MRI. This flexible platform provides modules for generating benchmarking datasets using transforms that model distribution shifts in MRI, implementations of newly derived benchmarking metrics for image segmentation, and examples for using the methodology with new models and tasks. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, providing the hippocampus dataset as a publicly available benchmark. By evaluating modern DNNs on these datasets, we demonstrate that they are highly susceptible to distribution shifts and corruptions in MRI. We show that while data augmentation strategies can substantially improve robustness to OOD data for anatomical segmentation tasks, modern DNNs using augmentation still lack robustness in more challenging lesion-based segmentation tasks. We finally benchmark U-Nets and vision transformers, finding robustness susceptibility to particular classes of transforms across architectures. The presented open-source platform enables generating new benchmarking datasets and comparing across models to study model design that results in improved robustness to OOD data and corruptions in MRI.

Feasibility of diffusion-tensor and correlated diffusion imaging for studying white-matter microstructural abnormalities: Application in COVID-19.

There has been growing attention on the effect of COVID-19 on white-matter microstructure, especially among those that self-isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single-shell diffusion magnetic resonance imaging (MRI) methods for detecting such effects. In this work, the performances of three single-shell-compatible diffusion MRI modeling methods are compared for detecting the effect of COVID-19, including diffusion-tensor imaging, diffusion-tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self-isolated patients at the study initiation and 3-month follow-up, along with age- and sex-matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single-shell methods to demonstrate COVID-19-related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID-19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID-19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID-19 related white-matter microstructural pathology manifests as a change in tissue diffusivity. Interestingly, different b-values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3-month follow-up, likely due to the limited size of the follow-up cohort. To summarize, correlated diffusion imaging is shown to be a viable single-shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID-19 patients, suggesting the two regions react differently to viral infection.

Re-irradiation for recurrent high-grade glioma: an analysis of prognostic factors for survival and predictors of radiation necrosis.

PURPOSE: Recurrent high-grade glioma (rHGG) is a heterogeneous population, and the ideal patient selection for re-irradiation (re-RT) has yet to be established. This study aims to identify prognostic factors for rHGG patients treated with re-RT. METHODS: We retrospectively reviewed consecutive adults with rHGG who underwent re-RT from 2009 to 2020 from our institutional database. The primary objective was overall survival (OS). Secondary endpoints included prognostic factors for early death (< 6 months after re-RT) and predictors of radiation necrosis (RN). RESULTS: For the 79 patients identified, the median OS after re-RT was 9.9 months (95% CI 8.3-11.6). On multivariate analyses, re-resection at progression (HR 0.56, p = 0.027), interval from primary treatment to first progression ≥ 16.3 months (HR 0.61, p = 0.034), interval from primary treatment to re-RT ≥ 23.9 months (HR 0.35, p < 0.001), and re-RT PTV volume < 112 cc (HR 0.27, p < 0.001) were prognostic for improved OS. Patients who had unmethylated-MGMT tumours (OR 12.4, p = 0.034), ≥ 3 prior systemic treatment lines (OR 29.1, p = 0.022), interval to re-RT < 23.9 months (OR 9.0, p = 0.039), and re-RT PTV volume ≥ 112 cc (OR 17.8, p = 0.003) were more likely to die within 6 months of re-RT. The cumulative incidence of RN was 11.4% (95% CI 4.3-18.5) at 12 months. Concurrent bevacizumab use (HR < 0.001, p < 0.001) and cumulative equivalent dose in 2 Gy fractions (EQD2, α/ß = 2) < 99 Gy2 (HR < 0.001, p < 0.001) were independent protective factors against RN. Re-RT allowed for less corticosteroid dependency. Sixty-six percent of failures after re-RT were in-field. CONCLUSION: We observe favorable OS rates following re-RT and identified prognostic factors, including methylation status, that can assist in patient selection and clinical trial design. Concurrent use of bevacizumab mitigated the risk of RN.

Age-associated change in pyruvate metabolism investigated with hyperpolarized 13 C-MRI of the human brain.

In this study, hyperpolarized 13 C MRI (HP-13 C MRI) was used to investigate changes in the uptake and metabolism of pyruvate with age. Hyperpolarized 13 C-pyruvate was administered to healthy aging individuals (N = 35, ages 21-77) and whole-brain spatial distributions of 13 C-lactate and 13 C-bicarbonate production were measured. Linear mixed-effects regressions were performed to compute the regional percentage change per decade, showing a significant reduction in both normalized 13 C-lactate and normalized 13 C-bicarbonate production with age: - 7 % ± 2 % per decade for 13 C-lactate and - 9 % ± 4 % per decade for 13 C-bicarbonate. Certain regions, such as the right medial precentral gyrus, showed greater rates of change while the left caudate nucleus had a flat 13 C-lactate versus age and a slightly increasing 13 C-bicarbonate versus age. The results show that both the production of lactate (visible as 13 C-lactate signal) as well as the consumption of monocarboxylates to make acetyl-CoA (visible as 13 C-bicarbonate signal) decrease with age and that the rate of change varies by brain region.

Effects of post-acute COVID-19 syndrome on the functional brain networks of non-hospitalized individuals.

Introduction: The long-term impact of COVID-19 on brain function remains poorly understood, despite growing concern surrounding post-acute COVID-19 syndrome (PACS). The goal of this cross-sectional, observational study was to determine whether there are significant alterations in resting brain function among non-hospitalized individuals with PACS, compared to symptomatic individuals with non-COVID infection. Methods: Data were collected for 51 individuals who tested positive for COVID-19 (mean age 41±12 yrs., 34 female) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19 (mean age 41±14 yrs., 9 female), with both groups assessed an average of 4-5 months after COVID testing. None of the participants had prior neurologic, psychiatric, or cardiovascular illness. Resting brain function was assessed via functional magnetic resonance imaging (fMRI), and self-reported symptoms were recorded. Results: Individuals with COVID-19 had lower temporal and subcortical functional connectivity relative to controls. A greater number of ongoing post-COVID symptoms was also associated with altered functional connectivity between temporal, parietal, occipital and subcortical regions. Discussion: These results provide preliminary evidence that patterns of functional connectivity distinguish PACS from non-COVID infection and correlate with the severity of clinical outcome, providing novel insights into this highly prevalent disorder.

Hypoperfusion Precedes Tau Deposition in the Entorhinal Cortex: A Retrospective Evaluation of ADNI-2 Data.

BACKGROUND AND PURPOSE: Tau deposition in the entorhinal cortex is the earliest pathological feature of Alzheimer's disease (AD). However, this feature has also been observed in cognitively normal (CN) individuals and those with mild cognitive impairment (MCI). The precise pathophysiology for the development of tau deposition remains unclear. We hypothesized that reduced cerebral perfusion is associated with the development of tau deposition. METHODS: A subset of the Alzheimer's Disease Neuroimaging Initiative data set was utilized. Included patients had undergone arterial spin labeling perfusion MRI along with [18F]flortaucipir tau PET at baseline, within 1 year of the MRI, and a follow-up at 6 years. The association between baseline cerebral blood flow (CBF) and the baseline and 6-year tau PET was assessed. Univariate and multivariate linear modeling was performed, with p<0.05 indicating significance. RESULTS: Significant differences were found in the CBF between patients with AD and MCI, and CN individuals in the left entorhinal cortex (p=0.013), but not in the right entorhinal cortex (p=0.076). The difference in maximum standardized uptake value ratio between 6 years and baseline was significantly and inversely associated with the baseline mean CBF (p=0.042, R²=0.54) in the left entorhinal cortex but not the right entorhinal cortex. Linear modeling demonstrated that CBF predicted 6-year tau deposition (p=0.015, R²=0.11). CONCLUSIONS: The results of this study suggest that a reduction in CBF at the entorhinal cortex precedes tau deposition. Further work is needed to understand the mechanism underlying tau deposition in aging and disease.

MRI Assessment of Cerebral Blood Flow in Nonhospitalized Adults Who Self-Isolated Due to COVID-19.

BACKGROUND: Neurological symptoms associated with coronavirus disease 2019 (COVID-19), such as fatigue and smell/taste changes, persist beyond infection. However, little is known of brain physiology in the post-COVID-19 timeframe. PURPOSE: To determine whether adults who experienced flu-like symptoms due to COVID-19 would exhibit cerebral blood flow (CBF) alterations in the weeks/months beyond infection, relative to controls who experienced flu-like symptoms but tested negative for COVID-19. STUDY TYPE: Prospective observational. POPULATION: A total of 39 adults who previously self-isolated at home due to COVID-19 (41.9 ± 12.6 years of age, 59% female, 116.5 ± 62.2 days since positive diagnosis) and 11 controls who experienced flu-like symptoms but had a negative COVID-19 diagnosis (41.5 ± 13.4 years of age, 55% female, 112.1 ± 59.5 since negative diagnosis). FIELD STRENGTH AND SEQUENCES: A 3.0 T; T1-weighted magnetization-prepared rapid gradient and echo-planar turbo gradient-spin echo arterial spin labeling sequences. ASSESSMENT: Arterial spin labeling was used to estimate CBF. A self-reported questionnaire assessed symptoms, including ongoing fatigue. CBF was compared between COVID-19 and control groups and between those with (n = 11) and without self-reported ongoing fatigue (n = 28) within the COVID-19 group. STATISTICAL TESTS: Between-group and within-group comparisons of CBF were performed in a voxel-wise manner, controlling for age and sex, at a family-wise error rate of 0.05. RESULTS: Relative to controls, the COVID-19 group exhibited significantly decreased CBF in subcortical regions including the thalamus, orbitofrontal cortex, and basal ganglia (maximum cluster size = 6012 voxels and maximum t-statistic = 5.21). Within the COVID-19 group, significant CBF differences in occipital and parietal regions were observed between those with and without self-reported on-going fatigue. DATA CONCLUSION: These cross-sectional data revealed regional CBF decreases in the COVID-19 group, suggesting the relevance of brain physiology in the post-COVID-19 timeframe. This research may help elucidate the heterogeneous symptoms of the post-COVID-19 condition. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.

Arachnoid granulations may be protective against the development of shunt dependent chronic hydrocephalus after aneurysm subarachnoid hemorrhage*.

BACKGROUND AND PURPOSE: Chronic hydrocephalus may develop as a sequela of aneurysmal subarachnoid hemorrhage, requiring long-term cerebrospinal fluid shunting. Several clinical predictors of chronic hydrocephalus and shunt dependence have been proposed. However, no anatomical predictors have been identified. MATERIALS AND METHODS: A retrospective cohort study was performed including 61 patients with aneurysmal subarachnoid hemorrhage. Clinical characteristics were noted for each patient including presentation World Federation of Neurosurgical Societies grade, modified Fischer grade, aneurysm characteristics, requirement for acute and chronic cerebrospinal fluid diversion, and 3-month modified Rankin scale. CT images were evaluated to determine the Evans' index and to enumerate the number of arachnoid granulations. Association between the clinical characteristics with ventriculoperitoneal shunt insertion and the 3-month modified Rankin scale were assessed. RESULTS: The initial Evans' index was positively associated with mFisher grade and age, but not the number of arachnoid granulations. 16.4% patients required insertion of a ventriculoperitoneal shunt. The number of arachnoid granulations were a significant negative predictor of ventriculoperitoneal shunt insertion [OR: 0.251 (95% CI:0.073-0.862; p = 0.028)]. There was significant difference in the number of arachnoid granulations between those with and without ventriculoperitoneal shunt (p = 0.002). No patient with greater than 4 arachnoid granulations required a ventriculoperitoneal shunt, irrespective of severity of initial grade. CONCLUSION: Arachnoid granulations may be protective against the development of shunt dependent chronic hydrocephalus after aneurysmal subarachnoid hemorrhage. This is irrespective of presenting hemorrhage severity. This is a potentially novel radiologic biomarker and anatomic predictor of shunt dependence.

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation.

White matter hyperintensities (WMHs) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D convolutional neural network with a U-Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control, and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. Four hundred thirty-two subjects were recruited to train the CNNs from four multisite imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multisite study. We compared our model to two established state-of-the-art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U-Net achieved the highest Dice similarity coefficient of 0.89 ± 0.08 and the lowest modified Hausdorff distance of 2.98 ± 4.40 mm. We further validated our models highlighting their robustness on "clinical adversarial cases" simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io.

Hypofractionated Stereotactic Radiation Therapy for Intact Brain Metastases in 5 Daily Fractions: Effect of Dose on Treatment Response.

PURPOSE: Multileaf collimator (MLC) linear accelerator (Linac)-based hypofractionated stereotactic radiation therapy (HSRT) is increasingly used not only for large brain metastases or those adjacent to critical structures but also for those metastases that would otherwise be considered for single-fraction radiosurgery (SRS). However, data on outcomes in general are limited, and there is a lack of understanding regarding optimal dosing. Our aim was to report mature image-based outcomes for MLC-Linac HSRT with a focus on clinical and dosimetric factors associated with local failure (LF). METHODS AND MATERIALS: A total of 220 patients with 334 brain metastases treated with HSRT were identified. All patients were treated using a 5-fraction daily regimen and were followed with clinical evaluation and volumetric magnetic resonance imaging every 2 to 3 months. Overall survival and progression-free survival were calculated using the Kaplan-Meier method, with LF determined using Fine and Gray's competing risk method. Predictive factors were identified using Cox regression multivariate analysis. RESULTS: Median follow-up was 10.8 months. Median size of treated metastasis was 1.9 cm; 60% of metastases were <2 cm in size. The median total dose was 30 Gy in 5 fractions; 36% of the cohort received <30 Gy. The median time to LF and 12-month cumulative incidence of LF was 8.5 months and 23.8%, respectively. Median time to death and 12-month overall survival rates were 11.8 months and 48.2%, respectively. Fifty-two metastases (15.6%) had an adverse radiation effect, of which 32 (9.5%) were symptomatic necrosis. Multivariable analysis identified worse LF in patients who received a total dose of <30 Gy (hazard ratio, 1.62; P = .03), with LF at 6 and 12 months of 13% and 33% for patients treated with <30 Gy versus 5% and 19% for patients treated with >30 Gy. Exploratory analysis demonstrated a dose-response effect observed in all histologic types, including among breast cancer subtypes. CONCLUSION: Optimal local control is achieved with HSRT of ≥30 Gy in 5 daily fractions, independent of tumor volume and histology, with an acceptable risk of radiation necrosis.

Brain structure and function in people recovering from COVID-19 after hospital discharge or self-isolation: a longitudinal observational study protocol.

BACKGROUND: The detailed extent of neuroinvasion or deleterious brain changes resulting from COVID-19 and their time courses remain to be determined in relation to "long-haul" COVID-19 symptoms. Our objective is to determine whether there are alterations in functional brain imaging measures among people with COVID-19 after hospital discharge or self-isolation. METHODS: This paper describes a protocol for NeuroCOVID-19, a longitudinal observational study of adults aged 20-75 years at Sunnybrook Health Sciences Centre in Toronto, Ontario, that began in April 2020. We aim to recruit 240 adults, 60 per group: people who contracted COVID-19 and were admitted to hospital (group 1), people who contracted COVID-19 and self-isolated (group 2), people who experienced influenza-like symptoms at acute presentation but tested negative for COVID-19 and self-isolated (group 3, control) and healthy people (group 4, control). Participants are excluded based on premorbid neurologic or severe psychiatric illness, unstable cardiovascular disease, and magnetic resonance imaging (MRI) contraindications. Initial and 3-month follow-up assessments include multiparametric brain MRI and electroencephalography. Sensation and cognition are assessed alongside neuropsychiatric assessments and symptom self-reports. We will test the data from the initial and follow-up assessments for group differences based on 3 outcome measures: MRI cerebral blood flow, MRI resting state fractional amplitude of low-frequency fluctuation and electroencephalography spectral power. INTERPRETATION: If neurophysiologic alterations are detected in the COVID-19 groups in our NeuroCOVID-19 study, this information could inform future research regarding interventions for long-haul COVID-19. The study results will be disseminated to scientists, clinicians and COVID-19 survivors, as well as the public and private sectors to provide context on how brain measures relate to lingering symptoms.