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Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors

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Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors

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Veiga Canuto, D.; Fernandez Patón, M.; Cerdá-Alberich, L.; Jimenez-Pastor, AM.; Gomis Maya, A.; Carot Sierra, JM.; Sanguesa Nebot, C.... (2024). Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors. RADIOLOGY-ARTIFICIAL INTELLIGENCE. 6(4). https://doi.org/10.1148/ryai.230208

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Título: Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors
Autor: Veiga Canuto, Diana Fernandez Patón, Matías Cerdá-Alberich, Leonor Jimenez-Pastor, Ana Maria Gomis Maya, Armando Carot Sierra, José Miguel Sanguesa Nebot, Cinta Martínez de las Heras, Blanca Pötschger, Ulrike Taschner-Mandl, Sabine Neri, Emanuele Cañete, Adela Ladenstein, Ruth Hero, Barbara Alberich-Bayarri, Ángel Martí-Bonmatí, Luis
Entidad UPV: Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials
Fecha difusión:
Resumen:
[EN] Purpose: To evaluate the reproducibility of radiomics features extracted from T2-weighted MR images in patients with neuroblastoma. Materials and Methods: A retrospective study included 419 patients (mean age, 29 ...[+]
Palabras clave: Pediatrics , MR Imaging , Oncology , Radiomics , Reproducibility , Repeatability , Neuroblastic tumors
Derechos de uso: Cerrado
Fuente:
RADIOLOGY-ARTIFICIAL INTELLIGENCE. (eissn: 2638-6100 )
DOI: 10.1148/ryai.230208
Editorial:
Radiological Society of North America
Versión del editor: https://doi.org/10.1148/ryai.230208
Código del Proyecto:
info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/
Agradecimientos:
This project has received funding from the European Union Horizon 2020 research and innovation program under grant agreement number 826494. This study was funded by PRIMAGE (PRedictive In-silico Multiscale Analytics to ...[+]
Tipo: Artículo

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