| Literature DB >> 30075545 |
Shasha Hou1, Xiaoyun Lin, Shen Wang, Yiming Shen, Zhaowei Meng, Qiang Jia, Jian Tan.
Abstract
This study aimed to analyze the imaging findings of F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) and chest thin-layer high-resolution computed tomography (HRCT), correlate the maximum standardized uptake value (SUVmax), and the pathological type of benign or malignant pulmonary nodules (PNs), and assess the diagnostic accuracy in differentiating malignant from benign PNs.A retrospective review of F-FDG PET/CT scans from 88 patients with PNs confirmed by pathology or clinical follow-up were included. They both accepted PET/CT and HRCT scan conventional. The final results were determined by a combination of PET/CT and HRCT. Independent samples t test was used for statistical analysis. Receiver operating curves (ROC) were generated and the optimal threshold of SUVmax was determined.The sensitivity, specificity, and accuracy of HRCT, PET/CT, and PET/CT combined with HRCT in the diagnosis of PNs were 83.3%, 70%, 77.3%; 91.7%, 62.5%, 78.4%; and 95.8%, 75%, 86.4%, respectively. The SUVmax of malignant nodules was significantly higher than that of benign nodules, and the difference was statistically significant (t = -5.668, P < .001). In the subgroup analysis, the SUVmax of squamous cell carcinoma was higher than that of the denocarcinoma (t = -5.442, P < .001), and that of bronchioloalveolar carcinoma (t = 4.678, P < .001), the difference were both statistically significant. There were both no significant difference between adenocarcinoma and bronchioloalveolar carcinoma (t = 0.36, P = .722), tuberculosis and inflammatory nodules (t = -0.18, P = .858). Higher the value of SUVmax, greater the risk of malignancy. However, when the SUVmax ranges between 2.5 and 8.0, the lesion may be benign or malignant, and a comprehensive evaluation using combination methods with HRCT are required. When SUVmax <2.5, there is still a 9.5% chance of PN malignancy. ROC curve shows SUVmax >3.635 as the best threshold, and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of PET/CT in diagnosis of PNs were 83.3%, 62.5%, 79.2%, 71.7%, and 71.4%, respectively.PET/CT combined with HRCT should be advocated to improve the sensitivity, specificity, and accuracy of PET/CT in diagnosis of PNs.Entities:
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Year: 2018 PMID: 30075545 PMCID: PMC6081071 DOI: 10.1097/MD.0000000000011640
Source DB: PubMed Journal: Medicine (Baltimore) ISSN: 0025-7974 Impact factor: 1.817
The CT value and SUVmax of benign and malignant PNs in different pathologic types (range, mean ± standard deviation).
The correlation analysis of SUVmax and pathology of nodules.
Figure 1A 69-year-old female patient presented with a subsolid nodule outside the basal segment of the lower lobe of the right lung. The diameter of the nodule measured about 29 mm. The SUVmax and delayed SUVmax were 2.43 and 3.12, respectively. Histopathology revealed a highly differentiated adenocarcinoma. A shows PET MIP. B shows a PET image in transverse section. C shows a chest thin layer CT image. D shows a fusion image of PET/CT. PET/CT = positron emission tomography/computed tomography, SUVmax = maximum standardized uptake value. MIP = maximum intensity projection.
Figure 2A 66-year-old female patient presented with an empty nodule with sharp edges at the posterior segment of the upper lobe of the left lung, whose diameter was about 13 mm and the SUVmax, 5.47. Histopathology revealed TB. A shows PET MIP. B shows PET image in transverse section. C shows chest thin layer CT image. D shows a fusion image of PET/CT. PET/CT = positron emission tomography/computed tomography, SUVmax = maximum standardized uptake value.
The efficiency of the diagnosis of PNs by HRCT, PET/CT, and PET/CT in combination with HRCT.
Figure 3The ROC curve of the diagnosis of PN in a PET/CT mathematical prediction model (AUC = 0.835 ± 0.042). PET/CT = positron emission tomography/computed tomography, PN = pulmonary nodules, ROC = receiver operating curves.