Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.

Journal: PLoS medicine
PMID: 30500819

Abstract

BACKGROUND: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.

Authors

  • Ahmed Hosny
    Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Chintan Parmar
    Departments of Radiation Oncology.
  • Thibaud P Coroller
    Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Patrick Grossmann
    Departments of Radiation Oncology.
  • Roman Zeleznik
    Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Avnish Kumar
    Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Johan Bussink
    Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Robert J Gillies
    Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America.
  • Raymond H Mak
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Hugo J W L Aerts
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.

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