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A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.

NPJ Digit Med

January 2025

Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.

Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.

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Rupture of the thyrocervical trunk aneurysm into the thoracic cavity does not occur very often. It is an urgent condition due to hemorrhagic shock by massive hemothorax with potentially fatal consequences. Pregnancy and puerperium are additional risk factors for a rupture of the thyrocervical trunk aneurysm in patients with neurofibromatosis and aneurysms.

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Background: Oral therapeutic options for plexiform neurofibromas (PNs) in individuals with neurofibromatosis type 1 (NF1) are receiving attention in clinical research. The MEK inhibitor (MEKi) Selumetinib is FDA-approved in children ages 2+ years with inoperable PNs, and shows activity in adults. Prolonged therapy with selumetinib is necessary to maintain tumor reduction.

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Neurofibromatosis type 1 (NF1) is a complex neurocutaneous disorder caused by pathogenic variants in the gene. Although genotype-phenotype correlation studies are increasing, robust clinically relevant correlations have remained limited. We conducted a retrospective analysis of data obtained from a cohort of 204 Hungarian individuals, with a mean age of 16 years (age range: 1-33 years).

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