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A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.
Saha, Ashirbani; Harowicz, Michael R; Grimm, Lars J; Kim, Connie E; Ghate, Sujata V; Walsh, Ruth; Mazurowski, Maciej A.
Afiliação
  • Saha A; Department of Radiology, Duke University School of Medicine, Durham, NC, 22705, USA. ashirbani.saha@duke.edu.
  • Harowicz MR; Department of Radiology, Duke University School of Medicine, Durham, NC, 22705, USA.
  • Grimm LJ; Department of Radiology, Duke University School of Medicine, Durham, NC, 22705, USA.
  • Kim CE; Department of Radiology, Duke University School of Medicine, Durham, NC, 22705, USA.
  • Ghate SV; Department of Radiology, Duke University School of Medicine, Durham, NC, 22705, USA.
  • Walsh R; Department of Radiology, Duke University School of Medicine, Durham, NC, 22705, USA.
  • Mazurowski MA; Department of Radiology, Duke University School of Medicine, Durham, NC, 22705, USA.
Br J Cancer ; 119(4): 508-516, 2018 08.
Article em En | MEDLINE | ID: mdl-30033447
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Neoplasias da Mama / Imageamento por Ressonância Magnética / Biomarcadores Tumorais Tipo de estudo: Prognostic_studies Limite: Adult / Aged / Aged80 / Female / Humans / Middle aged Idioma: En Revista: Br J Cancer Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Neoplasias da Mama / Imageamento por Ressonância Magnética / Biomarcadores Tumorais Tipo de estudo: Prognostic_studies Limite: Adult / Aged / Aged80 / Female / Humans / Middle aged Idioma: En Revista: Br J Cancer Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos