Introduction: Lymphatic abnormalities have been documented on magnetic resonance lymphangiography in patients with functionally univentricular circulation.
Objective: The aim of this study is to evaluate associations between findings on magnetic resonance lymphangiography, cardiac catheterisation and functional phenotype in patients with Fontan-Kreutzer circulation.
Materials And Methods: Between January 2017 and October 2019, seven patients with Fontan-Kreutzer circulation (16.57 ± 7.10 years) were enrolled following ethical committee approval and attainment of informed consent. Clinical data, consecutive magnetic resonance imaging and cardiac catheterisation findings were reviewed. Qualitative lymphatic abnormality types were defined as: 1 - Little or none; 2 - Localised to the supraclavicular region; 3 - Extending into the mediastinum; and 4 - Extending into the lung. Pulmonary hypertension was defined as a transpulmonary gradient > 6 mmHg. Quantitative data were assessed using Mann-Whitney tests, and < 0.05 was considered statistically significant.
Results: Patients with lymphatic abnormalities categorised type 1 or 2 and transpulmonary gradient ≤ 6 mmHg had a normal functional phenotype, while those with type 3 or 4 and transpulmonary gradient > 6 mmHg had a failing functional phenotype.
Conclusion: Magnetic resonance lymphangiography in patients with Fontan-Kreutzer circulation and pulmonary hypertension revealed lymphatic abnormalities types 3-4, indicative of a failing functional phenotype compared to patients without pulmonary hypertension. If validated in larger studies, these results suggest the potential utility of this extracardiac biomarker for improving risk stratification. Integration of lymphangiography and transpulmonary gradient with functional class, atrioventricular valve function, and ejection fraction allowed for the categorisation of three distinct functional phenotypes, which may be valuable for future analyses.
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http://dx.doi.org/10.1017/S1047951124026362 | DOI Listing |
Sci Rep
December 2024
Department of Diagnostic Radiology, Dalhousie University, Halifax, Canada.
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December 2024
Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Republic of Korea.
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December 2024
Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea.
Vertebral collapse (VC) following osteoporotic vertebral compression fracture (OVCF) often requires aggressive treatment, necessitating an accurate prediction for early intervention. This study aimed to develop a predictive model leveraging deep neural networks to predict VC progression after OVCF using magnetic resonance imaging (MRI) and clinical data. Among 245 enrolled patients with acute OVCF, data from 200 patients were used for the development dataset, and data from 45 patients were used for the test dataset.
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December 2024
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.
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December 2024
BAOBAB Unit, NeuroSpin center, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest.
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