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Summary: Background: Perivascular spaces (PVS) on brain MRI are surrogates for small parenchymal blood vessels and their perivascular compartment, and may relate to brain health. However, it is unknown whether PVS can predict dementia risk and brain atrophy trajectories in participants without dementia, as longitudinal studies on PVS are scarce and current methods for PVS assessment lack robustness and inter-scanner reproducibility. Methods: We developed a robust algorithm to automatically assess PVS count and size on clinical MRI, and investigated 1) their relationship with dementia risk and brain atrophy in participants without dementia, 2) their longitudinal evolution, and 3) their potential use as a screening tool in simulated clinical trials. We analysed 46,478 clinical measurements of cognitive functioning and 20,845 brain MRI scans from 10,004 participants (71.1 ± 9.7 years-old, 56.6% women) from three publicly available observational studies on ageing and dementia (the Alzheimer’s Disease Neuroimaging Initiative, the National Alzheimer’s Coordinating Centre database, and the Open Access Series of Imaging Studies). Clinical and MRI data collected between 2004 and 2022 were analysed with consistent methods, controlling for confounding factors, and combined using mixed-effects models. Findings: Our fully-automated method for PVS assessment showed excellent inter-scanner reproducibility (intraclass correlation coefficients >0.8). Fewer PVS and larger PVS diameter at baseline predicted higher dementia risk and accelerated brain atrophy. Longitudinal trajectories of PVS markers differed significantly in participants without dementia who converted to dementia compared with non-converters. In simulated placebo-controlled trials for treatments targeting cognitive decline, screening out participants at low risk of dementia based on our PVS markers enhanced the power of the trial independently of Alzheimer’s disease biomarkers. Interpretation: These robust cerebrovascular markers predict dementia risk and brain atrophy and may improve risk-stratification of patients, potentially reducing cost and increasing throughput of clinical trials to combat dementia. Funding: US National Institutes of Health.

Abstract Cerebrospinal fluid tumor-derived DNA (CSF-tDNA) analysis is a promising approach for monitoring the neoplastic processes of the central nervous system. We applied a lung cancer-specific sequencing panel (CAPP-Seq) to 81 CSF, blood, and tissue samples from 24 lung cancer patients who underwent lumbar puncture (LP) for suspected leptomeningeal disease (LMD). A subset of the cohort (N = 12) participated in a prospective trial of osimertinib for refractory LMD in which serial LPs were performed before and during treatment. CSF-tDNA variant allele fractions (VAFs) were significantly higher than plasma circulating tumor DNA (ctDNA) VAFs (median CSF-tDNA, 32.7%; median plasma ctDNA, 1.8%; P

Abstract Brain metastases occur in 1% of sarcoma cases and are associated with a median overall survival of 6 months. We report a rare case of a brain metastasis with unique radiologic and histopathologic features in a patient with low grade fibromyxoid sarcoma (LGFMS) previously treated with immune checkpoint inhibitor (ICI) therapy. The lone metastasis progressed in the midbrain tegmentum over 15 months as a non-enhancing, T2-hyperintense lesion with peripheral diffusion restriction, mimicking a demyelinating lesion. Histopathology of the lesion at autopsy revealed a rich infiltrate of tumor-associated macrophages (TAMs) with highest density at the leading edge of the metastasis, whereas there was a paucity of lymphocytes, suggestive of an immunologically cold environment. Given the important immunosuppressive and tumor-promoting functions of TAMs in gliomas and carcinoma/melanoma brain metastases, this unusual case provides an interesting example of a dense TAM infiltrate in a much rarer sarcoma brain metastasis.

ObjectiveThis study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2–4) from 3 different centers.MethodsTo quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space.ResultsThe Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively.ConclusionThe proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.

IntroductionManagement of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences.MethodsWe adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives.ResultsThe 2.5D and 3D models achieved similar results, albeit the 2.5D model had better detection rate, whereas the 3D model had fewer false positive predictions, and nnU-Net had fewest false positives, but with the lowest detection rate. On MRI data from center 1, the 2.5D, 3D, and nnU-Net detected 79%, 71%, and 65% of all metastases; had an average per patient sensitivity of 0.88, 0.84, and 0.76; and had on average 6.2, 3.2, and 1.7 false positive predictions per patient, respectively. For center 2, the 2.5D, 3D, and nnU-Net detected 88%, 86%, and 78% of all metastases; had an average per patient sensitivity of 0.92, 0.91, and 0.85; and had on average 1.0, 0.4, and 0.1 false positive predictions per patient, respectively.Discussion/ConclusionOur results show that deep learning can yield highly accurate segmentations of brain metastases with few false positives in multinational data, but the accuracy degrades for metastases with an area smaller than 0.4 cm2.

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Purpose: To report a case of branch retinal artery occlusion (BRAO) followed by branch retinal vein occlusion (BRVO) and paracentral acute middle maculopathy (PAMM) in a patient with confirmed calciphylaxis. Observations: A 52-year-old female with a history of BRAO in the right eye one-year prior presented with decreased vision and a new inferotemporal scotoma. Computed tomography angiography of the head and neck demonstrated vascular calcifications at the origin of both ophthalmic arteries, which were otherwise poorly visualized. Ophthalmic examination demonstrated retinal whitening superiorly with intraretinal hemorrhages inferiorly. Optical coherence tomography (OCT) demonstrated middle retinal hyperreflectivity and a mild epiretinal membrane. Fluorescein angiography (FFA) demonstrated delayed perfusion of superior retinal arcade. On further questioning, patient was found to have a history of IgA nephropathy with end-stage renal disease, secondary hyperparathyroidism and calciphylaxis. Calciphylaxis is a systemic disease, characterized by high levels of calcium and progressive calcification of the vascular medial layer leading to ischemia. Anterior ischemic optic neuropathy (AION) and crystalline retinopathy have been reported as ocular manifestations of calciphylaxis, however, there are very few reports on ophthalmic manifestations of calciphylaxis. Conclusion and importance: Clinical manifestations of calciphylaxis are variable and a detailed clinical history is important to suspect calciphylaxis. Calciphylaxis should be considered in the differential diagnosis of BRAO, BRVO, PAMM or any ophthalmic vascular manifestation in patients with end-stage renal disease.

Abstract The purpose of this study was to assess the clinical value of a deep learning (DL) model for automatic detection and segmentation of brain metastases, in which a neural network is trained on four distinct MRI sequences using an input-level dropout layer, thus simulating the scenario of missing MRI sequences by training on the full set and all possible subsets of the input data. This retrospective, multicenter study, evaluated 165 patients with brain metastases. The proposed input-level dropout (ILD) model was trained on multisequence MRI from 100 patients and validated/tested on 10/55 patients, in which the test set was missing one of the four MRI sequences used for training. The segmentation results were compared with the performance of a state-of-the-art DeepLab V3 model. The MR sequences in the training set included pre-gadolinium and post-gadolinium (Gd) T1-weighted 3D fast spin echo, post-Gd T1-weighted inversion recovery (IR) prepped fast spoiled gradient echo, and 3D fluid attenuated inversion recovery (FLAIR), whereas the test set did not include the IR prepped image-series. The ground truth segmentations were established by experienced neuroradiologists. The results were evaluated using precision, recall, Intersection over union (IoU)-score and Dice score, and receiver operating characteristics (ROC) curve statistics, while the Wilcoxon rank sum test was used to compare the performance of the two neural networks. The area under the ROC curve (AUC), averaged across all test cases, was 0.989 ± 0.029 for the ILD-model and 0.989 ± 0.023 for the DeepLab V3 model (p = 0.62). The ILD-model showed a significantly higher Dice score (0.795 ± 0.104 vs. 0.774 ± 0.104, p = 0.017), and IoU-score (0.561 ± 0.225 vs. 0.492 ± 0.186, p

Abstract Background Mild Cognitive Impairment (MCI) carries a high risk of progression to Alzheimer’s disease (AD) dementia. Previous clinical trials testing whether cholinesterase inhibitors can slow the rate of progression from MCI to AD dementia have yielded disappointing results. However, recent studies of the effects of repetitive transcranial magnetic stimulation (rTMS) in AD have demonstrated improvements in cognitive function. Because few rTMS trials have been conducted in MCI, we designed a trial to test the short-term efficacy of rTMS in MCI. Yet, in both MCI and AD, we know little about what site of stimulation would be ideal for improving cognitive function. Therefore, two cortical sites will be investigated in this trial: (1) the dorsolateral prefrontal cortex (DLPFC), which has been well studied for treatment of major depressive disorder; and (2) the lateral parietal cortex (LPC), a novel site with connectivity to AD-relevant limbic regions. Methods/design In this single-site trial, we plan to enroll 99 participants with single or multi-domain amnestic MCI. We will randomize participants to one of three groups: (1) Active DLPFC rTMS; (2) Active LPC rTMS; and (3) Sham rTMS (evenly split between DLPFC and LPC locations). After completing 20 bilateral rTMS treatment sessions, participants will be followed for 6 months to test short-term efficacy and track durability of effects. The primary efficacy measure is the California Verbal Learning Test-II (CVLT-II), assessed 1 week after intervention. Secondary analyses will examine effects of rTMS on other cognitive measures, symptoms of depression, and brain function with respect to the site of stimulation. Finally, selected biomarkers will be analyzed to explore predictors of response and mechanisms of action. Discussion The primary aim of this trial is to test the short-term efficacy of rTMS in MCI. Additionally, the project will provide information on the durability of cognitive effects and potentially distinct effects of stimulating DLPFC versus LPC regions. Future efforts would be directed toward better understanding therapeutic mechanisms and optimizing rTMS for treatment of MCI. Ultimately, if rTMS can be utilized to slow the rate of progression to AD dementia, this will be a significant advancement in the field. Trial registration Clinical Trials NCT03331796. Registered 6 November 2017, https://clinicaltrials.gov/ct2/show/NCT03331796. All items from the World Health Organization Trial Registration Data Set are listed in Appendix A. Protocol version This report is based on version 1, approved by the DSMB on 30 November, 2017 and amended on 14 August, 2018 and 19 September, 2019.

Malignant optic glioma presents a clinical and diagnostic challenge, as early imaging findings overlap with other more common causes of optic nerve enhancement and enlargement, potentially leading to delay in diagnosis. This rare diagnosis carries an extremely poor prognosis, with death usually occurring within 1 year. We present a case of malignant optic glioma that was initially diagnosed as optic neuritis and central retinal vein occlusion, and we emphasize the importance of serial imaging and definitive biopsy to promote early diagnosis and treatment of this entity. Keywords: Head and neck radiology, Neuro-ophthalmology, Optic glioma, Glioblastoma multiforme (GBM), Optic nerve, Central retinal vein occlusion (CRVO)

Early detection of brain metastases and differentiation from other neuropathologies is crucial. Although biopsy is often required for definitive diagnosis, imaging can provide useful information. After treatment commences, imaging is also performed to assess the efficacy of treatment. Contrast-enhanced magnetic resonance imaging (MRI) is the traditional imaging method for the evaluation of brain metastases, as it provides information about lesion size, morphology, and macroscopic properties. Newer MRI sequences have been developed to increase the conspicuity of detecting enhancing metastases. Other advanced MRI techniques, that have the capability to probe beyond the anatomic structure, are available to characterize micro-structures, cellularity, physiology, perfusion, and metabolism. Artificial intelligence provides powerful computational tools for detection, segmentation, classification, prediction, and prognosis. We highlight and review a few advanced MRI techniques for the assessment of brain metastases–specifically for (1) diagnosis, including differentiating between malignancy types and (2) evaluation of treatment response, including the differentiation between radiation necrosis and disease progression.

Intracranial hemorrhage is common and is caused by diverse pathology, including trauma, hypertension, cerebral amyloid angiopathy, hemorrhagic conversion of ischemic infarction, cerebral aneurysms, cerebral arteriovenous malformations, dural arteriovenous fistula, vasculitis, and venous sinus thrombosis, among other causes. Neuroimaging is essential for the treating physician to identify the cause of hemorrhage and to understand the location and severity of hemorrhage, the risk of impending cerebral injury, and to guide often emergent patient treatment. We review CT and MRI evaluation of intracranial hemorrhage with the goal of providing a broad overview of the diverse causes and varied appearances of intracranial hemorrhage.

Here we investigate the utility of a dual-echo Echo-Planar Imaging (DE-EPI) Diffusion Weighted Imaging (DWI) approach to improve lesion conspicuity in pediatric imaging. This method delivers two 'echo images' for one diffusion-preparation period. We also demonstrate how the echoes can be utilized to remove transmit/receive coil-induced and static magnetic field intensity modulations on both echo images, which often mimic pathology and thereby pose diagnostic challenges. DE-EPI DWI data were acquired in 18 pediatric patients with abnormal diffusion lesions, and 46 pediatric patient controls at 3T. Echo1 [TE = 45ms] and Echo2 [TE = 86ms] were corrected for signal intensity variation across the images by exploiting the images equivalent coil-sensitivity and susceptibility-induced modulations. Two neuroradiologists independently reviewed Echo1 and Echo2 and their intensity-corrected variants (cEcho1 and cEcho2) on a 7-point Likert scale, with grading on lesion conspicuity diagnostic confidence. The apparent diffusion coefficient (ADC) map from Echo1 was used to validate presence of true pathology. Echo2 was unanimously favored over Echo1 for its sensitivity for detecting acute brain injury, with a mean respective lesion conspicuity of 5.7/4.4 (p < 0.005) and diagnostic confidence of 5.1/4.3 (p = 0.025). cEcho2 was rated higher than cEcho1, with a mean respective lesion conspicuity of 5.5/4.3 (p < 0.005) and diagnostic confidence of 5.4/4.4 (p < 0.005). cEcho2 was favored over all echoes for its diagnostic reliability, particularly in regions close to the head coil. This work concludes that DE-EPI DWI is a useful alternative to conventional single-echo EPI DWI, whereby Echo2 and cEcho2 allows for improved lesion detection and overall higher diagnostic confidence.

In this paper, based on the developed statistical-thermodynamic model, which is based on data on the local structure of the compound and taking into account the striction interaction caused by the large sizes of the Ba and K cations, the formation of ferroelectric phases in BaTiO3 and KNbO3 perovskites has been studied. Based on the modified eight-minimum model, it has been possible to qualitatively identify the factors that determine the features of the thermodynamic behavior of these crystals and to reproduce the process of formation of the whole set of phase states observed in BaTiO3 and KNbO3.

Topicality. Accelerated metabolism in Graves' disease causes the development of oxidative stress, which significantly affects the clinical course and treatment results. Purpose. This study was conducted to evaluate the effect of treatment with thyrosol and thyrosol in combination with antioxidants, in particular vitamins C and E, on indicators of thyroid hormone function (thyroid-stimulating hormone and thyroxine) and oxidative stress (malondialdehyde and total antioxidant capacity). Material and method. The clinical study was performed on 41 patients with newly diagnosed Graves' disease of moderate severity, aged from 21 to 43 years, who were divided into two groups. The control group consisted of 19 patients who received thyrosol for treatment. The main group included 22 patients who were treated with thyrosol in combination with vitamins C and E. Examinations were carried out upon inclusion in the program and one and two months after the start of treatment. Results. The conducted studies showed that in the course of treatment, the indicators of malic dialdehyde gradually decreased from 21.3±0.4 (17.3–29.2) to 10.31±0.1 (9.72–10.82) and thyroxine from 37.7±0.5 (31.2–42.7) to 15.31±0.2 (13.20–20.52) and the total antioxidant capacity increased from 0.69±0.04 (0.52–0.79) to 1.58±0.1 (1.48–1.61), thyroid-stimulating hormone from 0.01±0.004 (0.07–0.001) to 2.31±0.1 (0.90–3.52). These changes were more pronounced in the main group compared to the control group during the study one month and two months after the start of treatment. Conclusions. Thus, antioxidant supplements can be used to improve thyroid function in hyperthyroid patients by enhancing antioxidants and restoring the oxidant-antioxidant balance.