Multicenter PET image harmonization using generative adversarial networks.

Abstract

To improve reproducibility and predictive performance of PET radiomic features in multicentric studies by cycle-consistent generative adversarial network (GAN) harmonization approaches. 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 ¹⁸F-FDG PET before and after harmonization. Image quality remained high (structural similarity: left kidney <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 0.800, right kidney <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 0.806, liver <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 0.780, lung <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 0.838, spleen <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 0.793, whole-body <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 0.832) after image harmonization across all utilized datasets. Using GAN-harmonization, inter-site reproducibility of radiomic features in healthy liver tissue increased at least by <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 5 ± 14% (first-order), <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 16 ± 7% (GLCM), <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 19 ± 5% (GLRLM), <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 16 ± 8% (GLSZM), <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 17 ± 6% (GLDM), and <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 23 ± 14% (NGTDM). In the head and neck cancer cohort, the outcome prediction improved from AUC 0.68 (95% CI 0.66-0.71) to AUC 0.73 (0.71-0.75) by application of GAN-harmonization. GANs are capable of performing image harmonization and increase reproducibility and predictive performance of radiomic features derived from different centers and scanners.