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

*Kontaktforfatter

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Abstract

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.

OriginalsprogEngelsk
TidsskriftEuropean Journal of Nuclear Medicine and Molecular Imaging
Vol/bind50
Udgave nummer5
Sider (fra-til)1510-1520
ISSN1619-7070
DOI
StatusUdgivet - apr. 2023

Bibliografisk note

Funding Information:
Open access funding provided by Lund University. This work was supported by the Swedish government and Region Skåne under the ALF agreement (the Medical Training and Research Agreement), and Knut and Alice Wallenberg Foundation, Sweden.

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