Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index

Sarah Lindgren Belal*, Måns Larsson, Jorun Holm, Karen Middelbo Buch-Olsen, Jens Sörensen, Anders Bjartell, Lars Edenbrandt, Elin Trägårdh

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Purpose: Consistent assessment of bone metastases is crucial for patient management and clinical trials in prostate cancer (PCa). We aimed to develop a fully automated convolutional neural network (CNN)-based model for calculating PET/CT skeletal tumor burden in patients with PCa. Methods: A total of 168 patients from three centers were divided into training, validation, and test groups. Manual annotations of skeletal lesions in [18F]fluoride PET/CT scans were used to train a CNN. The AI model was evaluated in 26 patients and compared to segmentations by physicians and to a SUV 15 threshold. PET index representing the percentage of skeletal volume taken up by lesions was estimated. Results: There was no case in which all readers agreed on prevalence of lesions that the AI model failed to detect. PET index by the AI model correlated moderately strong to physician PET index (mean r = 0.69). Threshold PET index correlated fairly with physician PET index (mean r = 0.49). The sensitivity for lesion detection was 65–76% for AI, 68–91% for physicians, and 44–51% for threshold depending on which physician was considered reference. Conclusion: It was possible to develop an AI-based model for automated assessment of PET/CT skeletal tumor burden. The model’s performance was superior to using a threshold and provides fully automated calculation of whole-body skeletal tumor burden. It could be further developed to apply to different radiotracers. Objective scan evaluation is a first step toward developing a PET/CT imaging biomarker for PCa skeletal metastases.

Original languageEnglish
JournalEuropean Journal of Nuclear Medicine and Molecular Imaging
Issue number5
Pages (from-to)1510-1520
Publication statusPublished - Apr 2023


  • Artificial intelligence
  • Deep learning
  • PET-CT
  • Prostate cancer
  • Tumor burden
  • Humans
  • Artificial Intelligence
  • Male
  • Prostatic Neoplasms/diagnostic imaging
  • Positron-Emission Tomography
  • Tumor Burden
  • Positron Emission Tomography Computed Tomography/methods


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