期刊
NUCLEAR MEDICINE COMMUNICATIONS
卷 41, 期 6, 页码 560-566出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MNM.0000000000001193
关键词
18F-fluorodeoxyglucose; PET computed tomography; solitary pulmonary nodule; support vector machine
资金
- Foundation of Shanghai Municipal Commission of Health and Family Planning [201740185]
- Shanghai Engineering Research Center of Molecular Imaging Probes, China [19DZ2282200]
Purpose This study aimed to evaluate the diagnostic value of a support vector machine (SVM) model built with texture features based on standard 2-[F-18]fluoro-2-deoxy-D-glucose (F-18-FDG) PET in patients with solitary pulmonary nodules (SPNs) at a volume larger than 5 mL. Patients and methods The PET results of 82 patients diagnosed with SPNs between 2014 and 2018 were retrospectively analysed. The volumes of interest (VOIs) of the SPNs were automatically segmented using threshold techniques from the standard PET imaging. Then, a large number of texture features were extracted from the VOIs using texture-analysis software. Next, an optimized SVM machine-learning model that was trained on standard PET images using texture features was employed to identify the optimal discrimination between malignant and benign nodules. Diagnostic models based on the maximum standardized uptake value (SUVmax) and the metabolic tumour volume (MTV) were compared with the SVM model with regard to the SPN diagnostic power. Results Compared with the SUVmax and MTV models, the texture-based SVM model provided an improvement of approximately 20% in diagnostic accuracy, positive predictive value, negative predictive value and the area under the operating characteristic curve. The receiver operating characteristic curve of the SVM model showed a significant improvement compared with the MTV model (P = 0.0345 < 0.05) and the SUVmax model (P = 0.01 < 0.05). Conclusions Standard F-18-FDG PET imaging can increase the differentiation of benign and malignant SPNs with volumes larger than 5 mL using an SVM model based on texture features.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据