Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD

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

doi:10.3389/fmed.2022.980950

Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD

Front Med (Lausanne). 2022 Dec 21:9:980950. doi: 10.3389/fmed.2022.980950. eCollection 2022.

Authors

Yingjian Yang^{1

2}, Ziran Chen², Wei Li², Nanrong Zeng^{2

3}, Yingwei Guo^{1

2}, Shicong Wang^{2

3}, Wenxin Duan^{2

3}, Yang Liu^{2

3}, Huai Chen⁴, Xian Li⁴, Rongchang Chen^{5

6

7}, Yan Kang^{1

2

3

8}

Affiliations

¹ College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
² College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China.
³ School of Applied Technology, Shenzhen University, Shenzhen, China.
⁴ Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
⁵ Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, China.
⁶ The Second Clinical Medical College, Jinan University, Guangzhou, China.
⁷ The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China.
⁸ Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, China.

Abstract

Introduction: Because of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier.

Methods: First, lung region images are automatically segmented from chest HRCT images for extracting the original 1,316 lung radiomics (OLR, 1,316) and 13,824 3D CNN features (O3C, 13,824). Second, the local features, including five selected pulmonary function test (PFT) parameters (SLF, 5), 28 selected lung radiomics (SLR, 28), and 22 selected 3D CNN features (S3C, 22), are respectively selected from the original 11 PFT parameters (OLF, 11), 1,316 OLR, and 13,824 O3C by the least absolute shrinkage and selection operator (Lasso) algorithm. Meantime, the global features, including two fused PFT parameters (FLF, 2), six fused lung radiomics (FLR, 6), and 34 fused 3D CNN features (F3C, 34), are respectively fused by 11 OLF, 1,316 OLR, and 13,824 O3C using the principal component analysis (PCA) algorithm. Finally, we combine all the local and global features (SLF + FLF + SLR + FLR + S3C + F3C, 5+ 2 + 28 + 6 + 22 + 34) for dyspnea identification in COPD based on the MLP classifier.

Results: Our proposed method comprehensively improves classification performance. The MLP classifier with all the local and global features achieves the best classification performance at 87.7% of accuracy, 87.7% of precision, 87.7% of recall, 87.7% of F1-scorel, and 89.3% of AUC, respectively.

Discussion: Compared with single-modal data, the proposed strategy effectively improves the classification performance for dyspnea identification in COPD, providing an objective and effective tool for COPD management.

Keywords: 3D CNN features; COPD; PFT parameters; combination strategy; dyspnea identification; lung radiomics features; machine learning; multi-modal data.