Objective To investigate the diagnostic efficiency of 18F-FDG PET/CT imaging for indeterminate thyroid nodules.Methods Sixty-eight patients (24 males,44 females,age:(52.8±10.58) years) with indeterminate thyroid nodules who underwent 18F-FDG PET/CT imaging from January 2006 to December 2012 were analyzed retrospectively.The imaging characteristic including clearity of boundary,uniform density,calcification,capsule,mean CT value,nodular size and SUVmax of thyroid nodules were evaluated.The gold standard was postoperative pathological results.Mann-Whitney u test,x2 test and ROC curve analysis were performed to investigate the diagnostic efficiency.Results Among 68 patients with indeterminate thyroid nodules,18 were malignant and 50 were benign according to pathological results.Uniform density,calcification,capsule (x2 =0.21,0.01,0.43,all P＞0.05) and mean CT value,nodular size (all AUCs＜0.5) could not differentiate benign from malignant thyroid nodules.However,whether the nodules had clear boundary was significant to differentiate benign from malignant nodules (x2=8.06,P＜0.05),and the sensitivity,specificity and accuracy were 55.6％ (10/ 18),80.0％ (40/50) and 73.5％ (50/68),respectively.The mean SUVmax of benign and malignant thyroid nodules was 3.16±1.84 and 8.53±7.09,respectively (u=-4.281,P＜0.01).AUC of SUVmax was 0.841 (95％ CI:0.726-0.955).According to the maximal Youden index(0.562),4.25 was chosen as the SUVmax threshold,and its sensitivity,specificity and accuracy were72.2％(13/18),84.0％(42/50) and 80.9％(55/68),respectively.Conclusion Among different characteristics of 18F-FDG PET/CT imaging,SUVmax of thyroid nodules plays an important role in the differential diagnosis of undetermined thyroid nodules,but CT image features have limited value.

Objective To examine the distribution,volume and glucose-uptake activity of brown adipose tissue (BAT) in adults and investigate their correlations with metabolic indicators.Methods 18F-flurodeoxyglucose (FDG) PET/CT was used to analyze the distribution,volume and glucose-uptake activity of BAT.The clinical and metabolic differences between BAT positive group (n =121) and BAT negative group (n=257) were compared.The influences of metabolic indicators (fast blood glucose (FBG),triglyceride (TG),total cholesterol (TC),high density lipoprotein cholesterol (HDL-C),low density lipoprotein cholesterol (LDL-C),uric acid (UA)) on the distribution,volume and activity of BAT were investigated.Logistic regression analysis,two-sample t test,x2 test and multiple linear regression were used to analyze the data.Results The distribution,volume and glucose-uptake activity of BAT were found to be significantly higher in subjects being tested in colder seasons than those who were tested in warmer seasons:2.91％ (87/2 991) vs 1.68％(34/2018),(433±402) vs (329±298) ml,(212±183) vs (169±145) g (x2=7.66,t values:3.36 and 2.98,all P＜0.05).The female proportion was significantly higher in BAT positive group than that in BAT negative group:68.60％ (83/121) vs 31.91％ (82/257) (x2 =16.10,P＜0.01).The average levels of age,body mass index (BMI),FBG,TG,TC,LDL-C and UA in BAT positive group were significantly low-er than those in BAT negative group:(41.30±10.90) vs (48.70±9.60) years,(21.30±2.40) vs (24.50± 3.10) kg/m2,(4.56±0.74) vs (5.34±1.33) mmol/L,(0.94±0.36) vs (2.06±1.64) mmol/L,(4.42± 0.79) vs (4.88±0.87) mmol/L,(1.99±0.58) vs (3.10±0.77) mmol/L,(285.11±70.00) vs (347.70± 101.10) μmol/L (t values:from-6.25 to-2.94,all P＜0.01).Logistic regression analysis revealed that season,gender,age,BMI,FBG,TG and LDL-C levels were all independent influencing factors of BAT distribution in adults (odds ratios:5.36,2.06,0.95,0.79,0.49,0.23,0.02;P＜0.01 or P＜0.05).Among BAT positive adults,gender and FBG levels were found to be strongly affected by the volume and glucose-uptake activity of BAT (β values:0.28,-0.21,both P＜0.05).Conclusions The distribution,volume and glucose-uptake activity of BAT in adults are associated with multiple metabolic indicators including BMI,levels of glucose,lipid and UA.The distribution of BAT is affected by gender,age,season,BMI,blood glucose,and blood lipids.