Evaluating Focal 18F-FDG Uptake in Thyroid Gland with Radiomics
10.1007/s13139-020-00659-2
- Author:
Ayşegül AKSU
1
;
Nazlı Pınar Karahan ŞEN
;
Emine ACAR
;
Gamze Çapa KAYA
Author Information
1. Department of Nuclear Medicine, School of Medicine, Dokuz Eylul University, İzmir, Turkey
- Publication Type:ORIGINAL ARTICLE
- From:Nuclear Medicine and Molecular Imaging
2020;54(5):241-248
- CountryRepublic of Korea
- Language:English
-
Abstract:
Purpose:The aim of this study was to evaluate the ability of 18F-FDG PET/CT texture analysis to predict the exact pathological outcome of thyroid incidentalomas.
Methods:18F-FDG PET/CT images between March 2010 and September 2018 were retrospectively reviewed in patients with focal 18F-FDG uptake in the thyroid gland and who underwent fine needle aspiration biopsy from this area. The focal uptake in the thyroid gland was drawn in 3D with 40% SUVmax threshold. Features were extracted from volume of interest (VOI) using the LIFEx package. The features obtained were compared in benign and malignant groups, and statistically significant variables were evaluated by receiver operating curve (ROC) analysis. The correlation between the variables with area under curve (AUC) value over 0.7 was examined; variables with correlation coefficient less than 0.6 were evaluated with machine learning algorithms.
Results:Sixty patients (70% train set, 30% test set) were included in the study. In univariate analysis, a statistically significant difference was observed in 6 conventional parameters, 5 first-, and 16 second-order features between benign and malignant groups in train set (p < 0.05). The feature with the highest benign-malignant discriminating power was GLRLMRLNU (AUC:0.827). AUC value of SUVmax was calculated as 0.758. GLRLMRLNU and SUVmax were evaluated to build a model to predict the exact pathology outcome. Random forest algorithm showed the best accuracy and AUC (78.6% and 0.849, respectively).
Conclusion:In the differentiation of benign-malignant thyroid incidentalomas, GLRLMRLNU and SUVmax combination may be more useful than SUVmax to predict the outcome.
