OBJECTIVE: To evaluate the role of (18)F-FDG PET/CT in characterizating solitary pulmonary nodule (SPN) and bone lesions. METHODS: 105 patients with a SPN smaller than 30 mm in axial diameter were recruited for this study. PET/CT images were obtained 60 min after intravenous injection of (18)F-FDG. Logistic regression analysis was performed to identify clinical predictors of SPN malignancy including age, sex, smoking history, malignant history, family history, symptoms, size, location, CT appearances, (18)F-FDG uptake, and to develop a clinical prediction model to estimate the probability of malignancy in the patients with SPN. The model fit was evaluated and the area under curve (AUC) of receiver operating characteristic (ROC) was used to evaluate the power of the model. RESULTS: The logistic regression analysis indicated that male, a positive smoking history, older age, larger nodule diameter, nodule with specula and nodule with high (18)F-FDG uptake were more likely to have malignant SPN. The clinical prediction model is described by the following equation: Logit(P) = -8.722 + 2.448 (gender) + 2.023(smoking) + 0. 851(age) + 1.057 (diameter) + 2.432 (spiculation) + 1.502 (FDG uptake). The AUC of the model was 0.892 (95% confidence interval 0.817 - 0.941). The prediction model had high accuracy in predicting malignant SPN, with 90.2%, 84.1 % and 87.6% sensitivity, specificity and accuracy respectively when the cut off value was set at 0.67. CONCLUSION: The prediction model is valid in predicting the probability of malignant SPN.
OBJECTIVE: To evaluate the role of (18)F-FDG PET/CT in characterizating solitary pulmonary nodule (SPN) and bone lesions. METHODS: 105 patients with a SPN smaller than 30 mm in axial diameter were recruited for this study. PET/CT images were obtained 60 min after intravenous injection of (18)F-FDG. Logistic regression analysis was performed to identify clinical predictors of SPN malignancy including age, sex, smoking history, malignant history, family history, symptoms, size, location, CT appearances, (18)F-FDG uptake, and to develop a clinical prediction model to estimate the probability of malignancy in the patients with SPN. The model fit was evaluated and the area under curve (AUC) of receiver operating characteristic (ROC) was used to evaluate the power of the model. RESULTS: The logistic regression analysis indicated that male, a positive smoking history, older age, larger nodule diameter, nodule with specula and nodule with high (18)F-FDG uptake were more likely to have malignant SPN. The clinical prediction model is described by the following equation: Logit(P) = -8.722 + 2.448 (gender) + 2.023(smoking) + 0. 851(age) + 1.057 (diameter) + 2.432 (spiculation) + 1.502 (FDG uptake). The AUC of the model was 0.892 (95% confidence interval 0.817 - 0.941). The prediction model had high accuracy in predicting malignant SPN, with 90.2%, 84.1 % and 87.6% sensitivity, specificity and accuracy respectively when the cut off value was set at 0.67. CONCLUSION: The prediction model is valid in predicting the probability of malignant SPN.