Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier

Yang, Yingjian1,2; Li, Wei2; Guo, Yingwei1,2; Zeng, Nanrong2; Wang, Shicong2; Chen, Ziran2; Liu, Yang2; Chen, Huai3; Duan, Wenxin2; Li, Xian3; Zhao, Wei4; Chen, Rongchang5,6,7; Kang, Yan1,2,8

2022

10.3934/mbe.2022366

Mathematical Biosciences and Engineering   影响因子和分区

1547-1063

1551-0018

Computed tomography (CT) has been the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Radiomics features extracted from the region of interest in chest CT images have been widely used for lung diseases, but they have not yet been extensively investigated for COPD. Therefore, it is necessary to understand COPD from the lung radiomics features and apply them for COPD diagnostic applications, such as COPD stage classification. Lung radiomics features are used for characterizing and classifying the COPD stage in this paper. First, 19 lung radiomics features are selected from 1316 lung radiomics features per subject by using Lasso. Second, the best performance classifier (multi-layer perceptron classifier, MLP classifier) is determined. Third, two lung radiomics combination features, Radiomics-FIRST and Radiomics-ALL, are constructed based on 19 selected lung radiomics features by using the proposed lung radiomics combination strategy for characterizing the COPD stage. Lastly, the 19 selected lung radiomics features with Radiomics-FIRST/Radiomics-ALL are used to classify the COPD stage based on the best performance classifier. The results show that the classification ability of lung radiomics features based on machine learning (ML) methods is better than that of the chest high-resolution CT (HRCT) images based on classic convolutional neural networks (CNNs). In addition, the classifier performance of the 19 lung radiomics features selected by Lasso is better than that of the 1316 lung radiomics features. The accuracy, precision, recall, F1-score and AUC of the MLP classifier with the 19 selected lung radiomics features and Radiomics-ALL were 0.83, 0.83, 0.83, 0.82 and 0.95, respectively. It is concluded that, for the chest HRCT images, compared to the classic CNN, the ML methods based on lung radiomics features are more suitable and interpretable for COPD classification. In addition, the proposed lung radiomics combination strategy for characterizing the COPD stage effectively improves the classifier performance by 12% overall (accuracy: 3%, precision: 3%, recall: 3%, F1 score: 2% and AUC: 1%).

radiomics COPD stage (GOLD) classification feature combination Lasso convolutional neural networks (CNN) machine learning (ML) chest HRCT images

SCI ; EI

英语

通讯

National Natural Science Foundation of China[62071311] ; Natural Science Foundation of Guangdong Province, China[2019A1515011382] ; Stable Support Plan for Colleges and Universities in Shenzhen, China[SZWD2021010] ; Scientific Research Fund of Liaoning Province, China[JL201919] ; Special Program for Key Fields of Colleges and Universities in Guangdong Province (biomedicine and health) of China[2021ZDZX2008]

Mathematical & Computational Biology

Mathematical & Computational Biology

WOS:000804003500008

AMER INST MATHEMATICAL SCIENCES-AIMS

20222412222209

Biological organs ; Computerized tomography ; Convolution ; Convolutional neural networks ; Diagnosis ; Image classification ; Image segmentation ; Machine learning ; Multilayer neural networks ; Pulmonary diseases

Biological Materials and Tissue Engineering:461.2 ; Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Computer Applications:723.5 ; Information Sources and Analysis:903.1

Web of Science

1.Northeastern Univ, Coll Med & Biol Informat Engn, Shenyang 110169, Peoples R China
2.Shenzhen Technol Univ, Coll Hlth Sci & Environm Engn, Shenzhen 518118, Peoples R China
3.Guangzhou Med Univ, Dept Radiol, Affiliated Hosp 1, Guangzhou 510120, Peoples R China
4.Chinese Peoples Armed Police Force, Liaoning Prov Corps Hosp, Med Engn, Shenyang 110141, Peoples R China
5.Shenzhen Peoples Hosp, Shenzhen Inst Resp Dis, Shenzhen 518001, Peoples R China
6.Jinan Univ, Clin Med Coll 2, Shenzhen 518001, Peoples R China
7.Southern Univ Sci & Technol, Affiliated Hosp 1, Shenzhen 518001, Peoples R China
8.Minist Educ, Engn Res Ctr Med Imaging & Intelligent Anal, Shenyang 110169, Peoples R China

Yang, Yingjian,Li, Wei,Guo, Yingwei,et al. Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier[J]. Mathematical Biosciences and Engineering,2022,19(8):7826-7855.

Yang, Yingjian.,Li, Wei.,Guo, Yingwei.,Zeng, Nanrong.,Wang, Shicong.,...&Kang, Yan.(2022).Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier.Mathematical Biosciences and Engineering,19(8),7826-7855.

Yang, Yingjian,et al."Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier".Mathematical Biosciences and Engineering 19.8(2022):7826-7855.