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Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.
Hosny, Ahmed; Parmar, Chintan; Coroller, Thibaud P; Grossmann, Patrick; Zeleznik, Roman; Kumar, Avnish; Bussink, Johan; Gillies, Robert J; Mak, Raymond H; Aerts, Hugo J W L.
Afiliación
  • Hosny A; Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Parmar C; Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Coroller TP; Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Grossmann P; Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Zeleznik R; Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Kumar A; Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Bussink J; Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Gillies RJ; Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America.
  • Mak RH; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Aerts HJWL; Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
PLoS Med ; 15(11): e1002711, 2018 11.
Article en En | MEDLINE | ID: mdl-30500819
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Interpretación de Imagen Radiográfica Asistida por Computador / Tomografía Computarizada por Rayos X / Diagnóstico por Computador / Carcinoma de Pulmón de Células no Pequeñas / Aprendizaje Profundo / Neoplasias Pulmonares Tipo de estudio: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: PLoS Med Asunto de la revista: MEDICINA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Interpretación de Imagen Radiográfica Asistida por Computador / Tomografía Computarizada por Rayos X / Diagnóstico por Computador / Carcinoma de Pulmón de Células no Pequeñas / Aprendizaje Profundo / Neoplasias Pulmonares Tipo de estudio: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: PLoS Med Asunto de la revista: MEDICINA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos