Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features.

Journal: Nuclear medicine communications

Published Date: Jun 1, 2020

Abstract

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]fluoro-2-deoxy-D-glucose (F-FDG) PET in patients with solitary pulmonary nodules (SPNs) at a volume larger than 5 mL.

Authors

Jianping Zhang

Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. zhangjianping@fudan.edu.cn.
Guang Ma

Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai.
Jingyi Cheng

Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai.
Shaoli Song

Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. shaoli-song@163.com.
Yingjian Zhang

Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai.
L Q Shi

Center for Biomedical Imaging, Fudan University.

Keywords

Female Fluorodeoxyglucose F18 Humans Image Processing, Computer-Assisted Male Middle Aged Positron Emission Tomography Computed Tomography Retrospective Studies ROC Curve Solitary Pulmonary Nodule Support Vector Machine Tumor Burden

External Resources

View on PubMed Access via DOI PubMed (32282636)

Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features.

Abstract

Authors

Keywords

External Resources

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