TY - JOUR
T1 - Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions
AU - Zhang, Ruiping
AU - Zhu, Lei
AU - Cai, Zhengting
AU - Jiang, Wei
AU - Li, Jian
AU - Yang, Chengwen
AU - Yu, Chunxu
AU - Jiang, Bo
AU - Wang, Wei
AU - Xu, Wengui
AU - Chai, Xiangfei
AU - Zhang, Xiaodong
AU - Tang, Yong
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/12
Y1 - 2019/12
N2 - Purpose: The study is to explore potential features and develop classification models for distinguishing benign and malignant lung lesions based on CT-radiomics features and PET metabolic parameters extracted from PET/CT images. Materials and methods: A retrospective study was conducted in baseline 18 F-flurodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 135 patients. The dataset was utilized for feature extraction of CT-radiomics features and PET metabolic parameters based on volume of interest, then went through feature selection and model development with strategy of five-fold cross-validation. Specifically, model development used support vector machine, PET metabolic parameters selection used Akaike's information criterion, and CT-radiomics were reduced by the least absolute shrinkage and selection operator method then forward selection approach. The diagnostic performances of CT-radiomics, PET metabolic parameters and combination of both were illustrated by receiver operating characteristic (ROC) curves, and compared by Delong test. Five groups of selected PET metabolic parameters and CT-radiomics were counted, and potential features were found and analyzed with Mann-Whitney U test. Results: The CT-radiomics, PET metabolic parameters, and combination of both among five subsets showed mean area under the curve (AUC) of 0.820 ± 0.053, 0.874 ± 0.081, and 0.887 ± 0.046, respectively. No significant differences in ROC among models were observed through pairwise comparison in each fold (P-value from 0.09 to 0.81, Delong test). The potential features were found to be SurfaceVolumeRatio and SUVpeak (P < 0.001 of both, U test). Conclusion: The classification models developed by CT-radiomics features and PET metabolic parameters based on PET/CT images have substantial diagnostic capacity on lung lesions.
AB - Purpose: The study is to explore potential features and develop classification models for distinguishing benign and malignant lung lesions based on CT-radiomics features and PET metabolic parameters extracted from PET/CT images. Materials and methods: A retrospective study was conducted in baseline 18 F-flurodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 135 patients. The dataset was utilized for feature extraction of CT-radiomics features and PET metabolic parameters based on volume of interest, then went through feature selection and model development with strategy of five-fold cross-validation. Specifically, model development used support vector machine, PET metabolic parameters selection used Akaike's information criterion, and CT-radiomics were reduced by the least absolute shrinkage and selection operator method then forward selection approach. The diagnostic performances of CT-radiomics, PET metabolic parameters and combination of both were illustrated by receiver operating characteristic (ROC) curves, and compared by Delong test. Five groups of selected PET metabolic parameters and CT-radiomics were counted, and potential features were found and analyzed with Mann-Whitney U test. Results: The CT-radiomics, PET metabolic parameters, and combination of both among five subsets showed mean area under the curve (AUC) of 0.820 ± 0.053, 0.874 ± 0.081, and 0.887 ± 0.046, respectively. No significant differences in ROC among models were observed through pairwise comparison in each fold (P-value from 0.09 to 0.81, Delong test). The potential features were found to be SurfaceVolumeRatio and SUVpeak (P < 0.001 of both, U test). Conclusion: The classification models developed by CT-radiomics features and PET metabolic parameters based on PET/CT images have substantial diagnostic capacity on lung lesions.
KW - CT-radiomics features
KW - Lung lesion
KW - PET metabolic parameters
KW - Potential feature
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U2 - 10.1016/j.ejrad.2019.108735
DO - 10.1016/j.ejrad.2019.108735
M3 - Article
C2 - 31733432
AN - SCOPUS:85074726746
SN - 0720-048X
VL - 121
JO - European Journal of Radiology
JF - European Journal of Radiology
M1 - 108735
ER -