18F-FDG PET/CT Quantitative Parameters and Texture Analysis Effectively Differentiate Endometrial Precancerous Lesion and Early-Stage Carcinoma. (12th June 2019)

• Record Type: Journal Article
• Authors: Wang, Tong; Sun, Hongzan; Guo, Yan; Zou, Lue
• Is Part Of: Molecular imaging. Volume 18(2019)
• Publication Date: 2019-06-12
• Subjects: PET/CT -- endometrial atypical hyperplasia (EAH) -- endometrial carcinoma (EC 1a) -- SUVpeak -- SUVmax -- textural analysis; Molecular diagnosis -- Periodicals; Diagnostic imaging -- Periodicals; Molecular biology -- Periodicals; Molecular diagnosis; Diagnostic imaging; Molecular biology; Periodicals; 616.075
• DOI: 10.1177/1536012119856965
• Languages: English
• ISSNs: 1535-3508

Abstract

Objective: This study evaluated the metabolic parameters and texture features of fluorodeoxyglucose positron emission tomography–computed tomography (PET/CT) for the diagnosis and differentiation of endometrial atypical hyperplasia (EAH), EAH with field cancerization (FC), and stage 1A endometrial carcinoma (EC 1a). Materials and Methods: We retrospectively analyzed the metabolic parameters of PET/CT in 170 patients with diagnoses confirmed by pathology, including 57 cases of EAH (57/170, 33.53%), 45 cases of FC (45/170, 26.47%), and 68 cases of EC 1a (68/170, 40.0%). Then, the texture features of each tumor were extracted and compared with the metabolic parameters and pathological results using nonparametric tests and linear regression analysis. The diagnostic performance was assessed by the area under the curve (AUC) values obtained from receiver operating characteristic analysis. Results: There were moderate positive correlations between the PET standardized uptake values (SUVpeak, SUVmax, and SUVmean) and postoperative pathological features with correlation coefficients ( r s ) of 0.663, 0.651, and 0.651, respectively ( P < .001). Total lesion glycolysis showed relatively low correlation with pathological characteristics ( r s = 0.476), whereas metabolic tumor volume and age showed the weakest correlations ( r s = 0.186 and 0.232, respectively). To differentiate between the diagnosis of EAH and FC, SUVmax displayed the largest AUC of 0.857 (sensitivity, 82.2%; specificity, 84.2%). Five texture features were screened out as Percentile 40, Percentile 45, InverseDifferenceMoment_AllDirection_offset 1, InverseDifferenceMoment_angle 45_offset 4, and ClusterProminence_angle 135_offset 7 ( P < .001) by linear model of texture analysis (AUC = 0.851; specificity = 0.692; sensitivity = 0.871). To differentiate between the diagnoses of FC and EC 1a, SUVpeak displayed the largest AUC of 0.715 (sensitivity, 67.6%; specificity, 77.8%), and 2 texture features were identified as Percentile 10 and CP_angle 135_offset 7 (AUC = 0.819; specificity = 0.871; sensitivity = 0.766; P < .001). Conclusions: SUVmax and SUVpeak had the highest diagnostic values for EAH, FC, and EC 1a compared with the other tested parameters. SUVmax, Percentile 40, Percentile 45, InverseDifferenceMoment_AllDirection_offset 1, InverseDifferenceMoment_angle 45_offset 4, and ClusterProminence_angle 135_offset 7 distinguished EAH from FC. SUVpeak, Percentile 10, and ClusterProminence_angle 135_offset 7 distinguished FC from EC 1a. This study showed that the addition of texture features provides valuable information for differentiating EAH, FC, and EC 1a diagnoses.