11 research outputs found
Association of Coronavirus Disease (COVID-19) With Large Vessel Occlusion Strokes: A Case-Control Study
No full textMachine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging.
No full textDual-Layer Spectral CT Virtual-Non-Contrast Images Aid in Parathyroid Adenoma Analysis and Radiation Dose Reduction: Confirmation of Findings From Dual-Energy CT
No full text4D-parathyroid CT scans have become a mainstay in the evaluation and pre-surgical planning for parathyroid adenomas. Most protocols typically rely on non-contrast images, prior to the arterial and delayed phases. Previous reports with dual-energy CT imaging have highlighted the utility of virtual non-contrast images to help reduce radiation dose while maintaining diagnostic accuracy. Herein, we report two cases of surgically proven parathyroid adenomas diagnosed with 4D-parathyroid CT scans performed on dual-layer spectral scanners, and in retrospect highlight the utility of virtual non-contrast images. To our knowledge, this report provides the first description of virtual non-contrast images from dual-layer spectral CT scanners that could aid in the diagnosis of parathyroid adenomas, confirming similar findings described with dual-energy CT scanners
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Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging.
Get PDFBACKGROUND: Differentiating glioblastoma, brain metastasis, and central nervous system lymphoma (CNSL) on conventional magnetic resonance imaging (MRI) can present a diagnostic dilemma due to the potential for overlapping imaging features. We investigate whether machine learning evaluation of multimodal MRI can reliably differentiate these entities. METHODS: Preoperative brain MRI including diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE), and dynamic susceptibility contrast (DSC) perfusion in patients with glioblastoma, lymphoma, or metastasis were retrospectively reviewed. Perfusion maps (rCBV, rCBF), permeability maps (K-trans, Kep, Vp, Ve), ADC, T1C+ and T2/FLAIR images were coregistered and two separate volumes of interest (VOIs) were obtained from the enhancing tumor and non-enhancing T2 hyperintense (NET2) regions. The tumor volumes obtained from these VOIs were utilized for supervised training of support vector classifier (SVC) and multilayer perceptron (MLP) models. Validation of the trained models was performed on unlabeled cases using the leave-one-subject-out method. Head-to-head and multiclass models were created. Accuracies of the multiclass models were compared against two human interpreters reviewing conventional and diffusion-weighted MR images. RESULTS: Twenty-six patients enrolled with histopathologically-proven glioblastoma (n=9), metastasis (n=9), and CNS lymphoma (n=8) were included. The trained multiclass ML models discriminated the three pathologic classes with a maximum accuracy of 69.2% accuracy (18 out of 26; kappa 0.540, P=0.01) using an MLP trained with the VpNET2 tumor volumes. Human readers achieved 65.4% (17 out of 26) and 80.8% (21 out of 26) accuracies, respectively. Using the MLP VpNET2 model as a computer-aided diagnosis (CADx) for cases in which the human reviewers disagreed with each other on the diagnosis resulted in correct diagnoses in 5 (19.2%) additional cases. CONCLUSIONS: Our trained multiclass MLP using VpNET2 can differentiate glioblastoma, brain metastasis, and CNS lymphoma with modest diagnostic accuracy and provides approximately 19% increase in diagnostic yield when added to routine human interpretation
Natural Language–based Machine Learning Models for the Annotation of Clinical Radiology Reports
No full textBrain MRI findings in COVID-19 patients with PRES: A systematic review.
No full textBACKGROUND: Numerous case reports and case series have described brain Magnetic Resonance Imaging (MRI) findings in Coronavirus disease 2019 (COVID-19) patients with concurrent posterior reversible encephalopathy syndrome (PRES).
PURPOSE: We aim to compile and analyze brain MRI findings in patients with COVID-19 disease and PRES.
METHODS: PubMed and Embase were searched on April 5th, 2021 using the terms COVID-19 , PRES , SARS-CoV-2 for peer-reviewed publications describing brain MRI findings in patients 21 years of age or older with evidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and PRES.
RESULTS: Twenty manuscripts were included in the analysis, which included descriptions of 30 patients. The average age was 57 years old. Twenty-four patients (80%) required mechanical ventilation. On brain MRI examinations, 15 (50%) and 7 (23%) of patients exhibited superimposed foci of hemorrhage and restricted diffusion respectively.
CONCLUSIONS: PRES is a potential neurological complication of COVID-19 related disease. COVID-19 patients with PRES may exhibit similar to mildly greater rates of superimposed hemorrhage compared to non-COVID-19 PRES patients
The Song of How the Name Jangar was Discovered: Plot Building in an Epic Narrative Recorded from Oirat Jangarchi Narsa
Get PDFIntroduction. Preservation of Kalmyk and Oirat oral heritage is a primary task of contemporary folklore studies. Scholars affiliated to Kalmyk Scientific Center (RAS) have tackled a project for further research into the Jangar epic (‘The Xinjiang Oirat Version of the Jangar Epic: Taletelling Tradition, Plot Structure, and Motifs’), and in 2023 the former have recorded The Song of How the Name Jangar was Discovered from Oirat jangarchi Narsa, a native of Alashan League (Inner Mongolia Autonomous Region, People’s Republic of China). Goals. The article attempts an insight into the plot structure of the mentioned epic Song, and introduces the latter into scientific circulation. Materials. The study focuses on the text of the Song recorded by the author and transcribed herein, with due account of related texts contained in the Xinjiang Oirat and Kalmyk versions of Jangar. Results. Our analysis of the Song’s plot structure shows the plot is interconnected with that of The Song of How Khan Uzeng Aldar Got Married once recited by the famous Hoboksar jangarchi Javin Juna, and describes some events that follow the invasion of the antagonist Khan Shara Mangas into the mentioned ruler’s lands. The plot of name giving plays an important role in the hero’s epic biography. Biographical cyclization arises from the listener’s interest in the protagonist’s life at large, and implies the most important stages of epic biography — birth, childhood, first feat, heroic deeds (up to heroic descendants who would grow to nobly defend Bumba) — be consistently developed. The miraculously born hero named Jangar matures to assert his heroic name with heroic deeds
Automated deep-neural-network surveillance of cranial images for acute neurologic events
No full textThe Evolution of Pituitary Cysts in Growth Hormone-Treated Children
No full textOBJECTIVES: We have previously shown that pituitary cysts may affect growth hormone secretion. This study sought to determine cyst evolution during growth hormone treatment in children. METHODS: Forty-nine patients with short stature, a pituitary cyst, and at least two brain MRI scans were included. The percent of the pituitary gland occupied by the cyst (POGO) was calculated, and a cyst with a POGO of ≤15% was considered small, while a POGO \u3e15% was considered large. RESULTS: Thirty-five cysts were small, and 14 were large. Five of the 35 small cysts grew into large cysts, while 6 of the 14 large cysts shrunk into small cysts. Of 4 cysts that fluctuated between large and small, 3 presented as large and 1 as small. Small cysts experienced greater change in cyst volume (CV) (mean=61.5%) than large cysts (mean=-0.4%). However, large cysts had a greater net change in CV (mean=44.2 mm) than small cysts (mean=21.0 mm). Older patients had significantly larger mean pituitary volume than younger patients (435.4 mm vs. 317.9 mm) and significantly larger mean CV than younger patients (77.4 mm vs. 45.2 mm), but there was no significant difference in POGO between groups. CONCLUSIONS: Pituitary cyst size can vary greatly over time. Determination of POGO over time is a useful marker for determining the possibility of a pathologic effect on pituitary function since it factors both cyst and gland volume. Large cysts should be monitored closely, given their extreme, erratic behavior
