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http://dx.doi.org/10.1055/s-2003-820558 | DOI Listing |
Oper Neurosurg (Hagerstown)
January 2025
Department of ENT, Postgraduate Institute of Medical Education & Research (PGIMER), Chandigarh, India.
Sci Rep
January 2025
Department of Pediatrics and Pediatric Hematology/Oncology, University Children's Hospital, Carl Von Ossietzky Universität, Klinikum Oldenburg AöR, Rahel-Straus-Straße 10, 26133, Oldenburg, Germany.
Survivors of sellar/suprasellar tumors involving hypothalamic structures face a risk of impaired quality of life, including tumor- and/or treatment-related hypothalamic obesity (TTR-HO) defined as abnormal weight gain resulting in severe persistent obesity due to physical, tumor- and/or treatment related damage of the hypothalamus. We analyze German claims data to better understand treatment pathways for patients living TTR-HO during the two years following the index surgical treatment. A database algorithm identified patients with TTR-HO in a representative German payer claims database between 2010 and 2021 (n = 5.
View Article and Find Full Text PDFJ Neurosurg Case Lessons
January 2025
Department of Neurology, Mayo Clinic, Rochester, Minnesota.
Background: Adamantinomatous craniopharyngiomas (ACPs) are slow-growing, cystic, highly morbid central nervous system tumors located adjacent to vital structures including the pituitary, hypothalamus, and optic chiasm. Tumor recurrence is common. Treatment relies on resection with or without adjuvant radiation and is highly individualized.
View Article and Find Full Text PDFCureus
December 2024
Department of Technology and Clinical Trials, Advanced Research, Deerfield Beach, USA.
This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models.
View Article and Find Full Text PDFAnn Pediatr Endocrinol Metab
December 2024
Department of Pediatrics, Seoul National University Children's Hospital, Seoul, Korea.
Rare endocrine diseases are complex conditions that require lifelong specialized care due to their chronic nature and associated long-term complications. In Korea, a lack of nationwide data on clinical practice and outcomes has limited progress in patient care. Therefore, the Multicenter Networks for Ideal Outcomes of Pediatric Rare Endocrine and Metabolic Disease (OUTSPREAD) study was initiated.
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