Article ; Online: Multicenter PET image harmonization using generative adversarial networks.
European journal of nuclear medicine and molecular imaging
2024
Abstract: Purpose: To improve reproducibility and predictive performance of PET radiomic features in multicentric studies by cycle-consistent generative adversarial network (GAN) harmonization approaches.: Methods: GAN-harmonization was developed to harmonize ... ...
Abstract | Purpose: To improve reproducibility and predictive performance of PET radiomic features in multicentric studies by cycle-consistent generative adversarial network (GAN) harmonization approaches. Methods: GAN-harmonization was developed to harmonize whole-body PET scans to perform image style and texture translation between different centers and scanners. GAN-harmonization was evaluated by application to two retrospectively collected open datasets and different tasks. First, GAN-harmonization was performed on a dual-center lung cancer cohort (127 female, 138 male) where the reproducibility of radiomic features in healthy liver tissue was evaluated. Second, GAN-harmonization was applied to a head and neck cancer cohort (43 female, 154 male) acquired from three centers. Here, the clinical impact of GAN-harmonization was analyzed by predicting the development of distant metastases using a logistic regression model incorporating first-order statistics and texture features from baseline Results: Image quality remained high (structural similarity: left kidney Conclusions: GANs are capable of performing image harmonization and increase reproducibility and predictive performance of radiomic features derived from different centers and scanners. |
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Language | English |
Publishing date | 2024-05-02 |
Publishing country | Germany |
Document type | Journal Article |
ZDB-ID | 8236-3 |
ISSN | 1619-7089 ; 0340-6997 ; 1619-7070 |
ISSN (online) | 1619-7089 |
ISSN | 0340-6997 ; 1619-7070 |
DOI | 10.1007/s00259-024-06708-8 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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