PET and CT based DenseNet outperforms advanced deep learning models for outcome prediction of oropharyngeal cancer.

Abstract

Authors

• Baoqiang Ma
  School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China.
• Jiapan Guo
  University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
• Lisanne V van Dijk
  Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA.
• Johannes A Langendijk
  Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
• Peter M A van Ooijen
  Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Machine Learning Lab, Data Science Center in Health (DASH), Groningen, Netherlands.
• Stefan Both
  Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
• Nanna M Sijtsema
  Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.

Keywords

External Resources