Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting.

Journal: JAMA oncology
PMID: 30998808

Abstract

IMPORTANCE: Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial training and is subject to significant interobserver variation.

Authors

  • Raymond H Mak
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Michael G Endres
    Laboratory for Innovation Science at Harvard, Harvard University, Boston, Massachusetts.
  • Jin H Paik
    Laboratory for Innovation Science at Harvard, Harvard University, Boston, Massachusetts.
  • Rinat A Sergeev
    Laboratory for Innovation Science at Harvard, Harvard University, Boston, Massachusetts.
  • Hugo Aerts
    Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute/Harvard Medical School, Boston, Massachusetts.
  • Christopher L Williams
    Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute/Harvard Medical School, Boston, Massachusetts.
  • Karim R Lakhani
    Laboratory for Innovation Science at Harvard, Harvard University, Boston, Massachusetts.
  • Eva C Guinan
    Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute/Harvard Medical School, Boston, Massachusetts.

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