Nucl Med Mol Imaging.  2020 Oct;54(5):241-248. 10.1007/s13139-020-00659-2.

Evaluating Focal ¹⁸F-FDG Uptake in Thyroid Gland with Radiomics

• Aksu A
• Şen NPK
• Acar E
• Kaya G

• KMID: 2507741
• DOI: http://doi.org/10.1007/s13139-020-00659-2

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 GLRLM_RLNU (AUC:0.827). AUC value of SUVmax was calculated as 0.758. GLRLM_RLNU 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, GLRLM_RLNU and SUVmax combination may be more useful than SUVmax to predict the outcome.

Keyword

Thyroid incidentaloma; FDG; PET; Radiomics; Texture