|
|
|
| The comparative study of 18F-FDG PET/CT and mathematical prediction model in diagnosis of solitary pulmonary nodule |
Department of PET/CT, Raiology Imaging Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015
|
|
| Cite this article: |
|
LIN Jie,TANG Kun,YIN Weiwei, et al. The comparative study of 18F-FDG PET/CT and mathematical prediction model in diagnosis of solitary pulmonary nodule[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2015, 45(5): 354-.
|
|
|
Abstract Objective: To compare and analyze the value of 18F-FDG PET/CT and the mathematical prediction model in diagnosis of solitary pulmonary nodule (SPN), using receiver operating characteristic (ROC) curves analysis. Methods: All of 186 patients confirmed with SPN and performed with PET/CT were retrospectively analyzed in this study. For PET/CT integrated images, the morphology and metabolism of lesions were semiquantitatively or visual analysed by observers. A 5-score system was used to classify the diagnostic results of PET/CT integrated images: 0, definitely benign; 1, benign more likely; 2, probably benign; 3, probably malignant; 4, malignant more likely; and 5, definitely malignant. The logistic mathematical model was established with univariate analysis and multivariate analysis. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of PET/CT and the mathematical model in diagnosis of SPN were calculated respectively. The difference of the areas under the ROC curves (AUC) between PET/CT and the mathematical model was compared. Results: The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of PET/CT were 97.6%, 79.4%, 91.4%, 90.2% and 94.3%, respectively. The mathematical model established by logistic regression analysis was: p=ex/(1+ex), x=-8.111+0.091×age+1.351×lobulation+3.565×vascular convergence+2.153×retraction of pleural+0.447×SUVmax. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the model for prediction of SPN were 87.8%, 81.0%, 85.5%, 90.0% and 77.3% respectively. The AUCs of PET/CT and mathematical model were 0.951±0.015 and 0.927±0.019. There was no statistically significant in difference of AUC between PET/CT and mathematical model (P>0.05). Conclusion: The value of mathematical prediction model in diagnosis of SPN is similar to PET/CT and not affected by subjective factors. Therefore, it should be used in clinical practice combined with PET/CT for improving diagnostic accuracy.
|
|
Received: 05 July 2014
|
| |
|
|
|
|
|
|
