Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.

Journal: Computer methods and programs in biomedicine
Published Date: Nov 22, 2023

Abstract

BACKGROUND AND OBJECTIVE: Recently, deep learning (DL) algorithms showed to be promising in predicting outcomes such as distant metastasis-free survival (DMFS) and overall survival (OS) using pre-treatment imaging in head and neck cancer. Gross Tumor Volume of the primary tumor (GTVp) segmentation is used as an additional channel in the input to DL algorithms to improve model performance. However, the binary segmentation mask of the GTVp directs the focus of the network to the defined tumor region only and uniformly. DL models trained for tumor segmentation have also been used to generate predicted tumor probability maps (TPM) where each pixel value corresponds to the degree of certainty of that pixel to be classified as tumor. The aim of this study was to explore the effect of using TPM as an extra input channel of CT- and PET-based DL prediction models for oropharyngeal cancer (OPC) patients in terms of local control (LC), regional control (RC), DMFS and OS.

Authors

  • Alessia de Biase
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, GR, The Netherlands.
  • 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, The Netherlands. Electronic address: l.v.van.dijk@umcg.nl.
  • Johannes A Langendijk
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Stefan Both
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Peter M A van Ooijen
    University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
  • Nanna M Sijtsema
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.

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