Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma.

Liu, Gengbo; Poon, Mini; Zapala, Matthew A; Temple, William C; Vo, Kieuhoa T; Matthay, Kathrine K; Mitra, Debasis; Seo, Youngho

Liu, Gengbo; Poon, Mini; Zapala, Matthew A; Temple, William C; Vo, Kieuhoa T; Matthay, Kathrine K; Mitra, Debasis; Seo, Youngho.

Afiliação

Liu G; Department of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA.
Poon M; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
Zapala MA; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
Temple WC; Department of Pediatrics, University of California, San Francisco, CA, USA.
Vo KT; Department of Pediatrics, University of California, San Francisco, CA, USA.
Matthay KK; Department of Pediatrics, University of California, San Francisco, CA, USA.
Mitra D; Department of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA. dmitra@cs.fit.edu.
Seo Y; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA. dmitra@cs.fit.edu.

J Digit Imaging ; 35(3): 605-612, 2022 06.

Article em En | MEDLINE | ID: mdl-35237892

Assuntos

Aprendizado de Máquina; Neuroblastoma; Algoritmos; Criança; Humanos; Neuroblastoma/diagnóstico por imagem; Neuroblastoma/genética; Curva ROC; Estudos Retrospectivos

Palavras-chave

CT; Machine learning; Neural network; Neuroblastoma; Radiomics

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado de Máquina / Neuroblastoma Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Child / Humans Idioma: En Revista: J Digit Imaging Assunto da revista: DIAGNOSTICO POR IMAGEM / INFORMATICA MEDICA / RADIOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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