Value of 18F-FDG PET-CT in predicting the WHO grade of malignancy in thymic epithelial tumors

目的 探讨术前18F-脱氧葡萄糖(18F-FDG) PET-CT显像对胸腺上皮性肿瘤世界卫生组织(WHO)组织学简化分型的预测价值.方法 45例经病理证实的胸腺上皮性肿瘤患者,根据WHO简化组织学分型法分为低危组及高危组,对其18F-FDG PET-CT影像进行回顾性分析.采用Spearman法分析PET-CT的半定量指标与胸腺肿瘤WHO简化分型之间的相关性,并利用单因素Logistic回归分析联合受试者工作曲线(ROC曲线)评价PET-CT预测WHO简化分型的诊断效能.结果 45例胸腺上皮性肿瘤患者中,低危组10例(22.2％),高危组35例(77.8％).18F-FDG PET-CT显像的最大标准摄取值(SUVmax)、SUVmax与肿瘤最大径比值(SUVmax/T)与胸腺上皮性肿瘤WHO简化分型相关(r值分别为0.577和0.621,均P＜ 0.000 1);SUVmax、SUVmax/T预测胸腺上皮性肿瘤组织学分型的诊断效能均较高(ROC曲线下面积分别为0.888和0.917,均P＜ 0.000 1).结论 18F-FDG PET-CT显像与胸腺上皮性肿瘤组织学WHO简化分型相关,对胸腺上皮性肿瘤的病理恶性度分级有预测价值.Objective To investigate the value of 18F-FDG PET-CT in predicting the simplified WHO grade of malignancy in thymic epithelial tumors.Methods The 18F-FDG PET-CT imaging of forty-five patients with pathologically proven thymic epithelial tumors was analyzed retrospectively.The patients were subdivided into a low-risk group and a high-risk group based on the simplified WHO histological classification.Spearman rank correlation analysis was used to determine the relation between parameters of PET-CT and simplified WHO histological classification.An univariate logistic regression analysis and ROC curve were performed for assessing the ability of 18F-FDG PET-CT as a predictor of the simplified WHO classification.Results There were ten patients classified into low-risk group,and thirty-five patients classified into high-risk group.Both SUVmax and SUVmax / T showed significant relationship with patients subgroup (Spearman correlation coefficients were 0.577 and 0.621,respectively,P ＜ 0.000 1).The SUVmax and SUVmax / T were found to be predictive factors that were useful to distinguish high-risk tumor from lowrisk tumor (area under the ROC curve were 0.888 and 0.917,respectively,P ＜ 0.000 1).Conclusions A significant relationship is observed between 18F-FDG PET-CT findings and histological WHO classification of thymic epithelial tumors.PET-CT may be useful for predicting the grad of malignancy in thymic epithelial tumors.国家自然科学基金中国科技核心期刊(ISTIC)5294-297,3092