Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 9, 2022

Abstract

OBJECTIVES: Recent studies have shown that deep learning based on pre-treatment positron emission tomography (PET) or computed tomography (CT) is promising for distant metastasis (DM) and overall survival (OS) prognosis in head and neck cancer (HNC). However, lesion segmentation is typically required, resulting in a predictive power susceptible to variations in primary and lymph node gross tumor volume (GTV) segmentation. This study aimed at achieving prognosis without GTV segmentation, and extending single modality prognosis to joint PET/CT to allow investigating the predictive performance of combined- compared to single-modality inputs.

Authors

  • Yiling Wang
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany; Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China.
  • Elia Lombardo
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany.
  • Michele Avanzo
    Division of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, PN, Italy.
  • Sebastian Zschaek
    Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Radiation Oncology, Berlin, Germany.
  • Julian Weingärtner
    Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Radiation Oncology, Berlin, Germany.
  • Adrien Holzgreve
    University Hospital, LMU Munich, Nuclear Medicine, Munich, Germany.
  • Nathalie L Albert
    German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Nuclear Medicine, Ludwig-Maximilians-University Munich, Munich, Germany.
  • Sebastian Marschner
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany.
  • Giuseppe Fanetti
    Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, Italy.
  • Giovanni Franchin
    Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, Italy.
  • Joseph Stancanello
    Division of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, PN, Italy.
  • Franziska Walter
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.
  • Stefanie Corradini
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Maximilian Niyazi
    Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
  • Jinyi Lang
    Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China.
  • Claus Belka
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Marco Riboldi
    Department of Medical Physics, Ludwig-Maximilians-Universität München, Germany.
  • Christopher Kurz
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.
  • Guillaume Landry
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.

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