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

Yingjian Yang; Wei Li; Yingwei Guo; Nanrong Zeng; Shicong Wang; Ziran Chen; Yang Liu; Huai Chen; Wenxin Duan; Xian Li; Wei Zhao; Rongchang Chen; Yan Kang; Yingjian Yang; Wei Li; Yingwei Guo; Nanrong Zeng; Shicong Wang; Ziran Chen; Yang Liu; Huai Chen; Wenxin Duan; Xian Li; Wei Zhao; Rongchang Chen; Yan Kang

doi:10.3934/mbe.2022366

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 8: 7826-7855. doi: 10.3934/mbe.2022366

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

1.
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
2.
College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
3.
Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
4.
Medical Engineering, Liaoning Provincial Corps Hospital of the Chinese People's Armed Police Force, Shenyang 110141, China
5.
Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen 518001, China
6.
The Second Clinical Medical College, Jinan University, Shenzhen 518001, China
7.
The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518001, China
8.
Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China

Academic Editor: Soheila Borhani

Received: 05 April 2022 Revised: 02 May 2022 Accepted: 08 May 2022 Published: 25 May 2022

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
Citation: Yingjian Yang, Wei Li, Yingwei Guo, Nanrong Zeng, Shicong Wang, Ziran Chen, Yang Liu, Huai Chen, Wenxin Duan, Xian Li, Wei Zhao, Rongchang Chen, Yan Kang. 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. doi: 10.3934/mbe.2022366

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Abstract

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%).

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