Uncertainty-aware deep learning for segmentation of primary tumor and pathologic lymph nodes in oropharyngeal cancer: Insights from a multi-center cohort.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published Date: Mar 25, 2025

Abstract

PURPOSE: Information on deep learning (DL) tumor segmentation accuracy on a voxel and a structure level is essential for clinical introduction. In a previous study, a DL model was developed for oropharyngeal cancer (OPC) primary tumor (PT) segmentation in PET/CT images and voxel-level predicted probabilities (TPM) quantifying model certainty were introduced. This study extended the network to simultaneously generate TPMs for PT and pathologic lymph nodes (PL) and explored whether structure-level uncertainty in TPMs predicts segmentation model accuracy in an independent external cohort.

Authors

  • Alessia de Biase
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, GR, The Netherlands.
  • Nanna Maria Sijtsema
    Department of Radiation Oncology, University Medical Center Groningen (UMCG), 9700 RB, 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.
  • Roel Steenbakkers
    Department of Radiation Oncology, University Medical Center Groningen (UMCG), 9700 RB, Groningen, the Netherlands.
  • Johannes A Langendijk
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Peter van Ooijen
    Department of Radiation Oncology, Coordinator Machine Learning Lab, Data Science Center in Health (DASH), University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.

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