Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Nov 19, 2021

Abstract

BACKGROUND AND PURPOSE: The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performed manually which exposes two major issues: cost and reproducibility. Delineation is a time-consuming process that is subject to inter- and intra-observer variability. While methods have been proposed to predict GTV contours, typical approaches ignore variability and therefore fail to utilize the valuable confidence information offered by multiple contours.

Authors

  • Thibault Marin
    Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States; Harvard Medical School, Boston, United States.
  • Yue Zhuo
    Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States; Harvard Medical School, Boston, United States.
  • Rita Maria Lahoud
    Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States; Harvard Medical School, Boston, United States.
  • Fei Tian
    Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
  • Xiaoyue Ma
    Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA.
  • Fangxu Xing
    Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA.
  • Maryam Moteabbed
    Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States; Department of Radiation Oncology, Massachusetts General Hospital, Boston, United States; Harvard Medical School, Boston, United States.
  • Xiaofeng Liu
    Changzhou Key Laboratory of Robots & Intelligent Technology, Hohai University, China.
  • Kira Grogg
    Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States; Harvard Medical School, Boston, United States.
  • Nadya Shusharina
    Department of Radiation Oncology, Massachusetts General Hospital, Boston, United States; Harvard Medical School, Boston, United States.
  • Jonghye Woo
    Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA.
  • Ruth Lim
    Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
  • Chao Ma
  • Yen-Lin E Chen
    Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States; Department of Radiation Oncology, Massachusetts General Hospital, Boston, United States; Harvard Medical School, Boston, United States.
  • Georges El Fakhri

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