Published in:
01-12-2017
Texture analysis of FDG PET/CT for differentiating between FDG-avid benign and metastatic adrenal tumors: efficacy of combining SUV and texture parameters
Authors:
Masatoyo Nakajo, Megumi Jinguji, Masayuki Nakajo, Tetsuya Shinaji, Yoshiaki Nakabeppu, Yoshihiko Fukukura, Takashi Yoshiura
Published in:
Abdominal Radiology
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Issue 12/2017
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Abstract
Purpose
To retrospectively investigate the SUV-related and texture parameters individually and in combination for differentiating between F-18-fluorodeoxyglucose (FDG)-avid benign and metastatic adrenal tumors with PET/CT.
Methods
Thirteen benign adrenal tumors (BATs) and 22 metastatic adrenal tumors (MATs) with a metabolic tumor volume (MTV) > 10.0 cm3 and SUV ≥ 2.5 were included. SUVmax, MTV, total lesion glycolysis, and four textural parameters [entropy, homogeneity, intensity variability (IV), and size-zone variability] were obtained. These parameters were compared between BATs and MATs using Mann–Whitney U test, and the diagnostic performance was evaluated by the area under the curve (AUC) values derived from the receiver operating characteristic analysis. The diagnostic value of combining SUV and texture parameters was examined using a scoring system.
Results
MATs showed significantly higher SUVmax (p = 0.004), entropy (p = 0.013), IV (p = 0.006), and lower homogeneity (p = 0.019) than BATs. The accuracies for diagnosing MATs were 82.9, 82.9, 85.7, and 71.4% for SUVmax, entropy, IV, and homogeneity, respectively. No significant differences in AUC were found among these parameters (p > 0.05 each). When each parameter was scored as 0 (negative for malignancy) and 1 (positive for malignancy) according to each threshold criterion and the four parameter summed scores 0, 1, and 2 were defined as benignity and 3 and 4 as malignancy, the sensitivity and specificity and accuracy to predict MATs were 100% (22/22), 84.6% (11/13), and 94.3% (33/35), respectively, with 0.97 of the AUC.
Conclusion
The combined use of SUVmax and texture parameters has a potential to significantly increase the diagnostic performance to differentiate between large FDG-avid BATs and MATs.