COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images

Deng, Xingguang1,2; Li, Wei2; Yang, Yingjian3; Wang, Shicong2,4; Zeng, Nanrong2,4; Xu, Jiaxuan5; Hassan, Haseeb2; Chen, Ziran1,2; Liu, Yang2; Miao, Xiaoqiang1,2; Guo, Yingwei6; Chen, Rongchang7; Kang, Yan1,2,3,8

2024-06-01

10.1007/s11517-024-03016-z

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING   影响因子和分区

0140-0118

1741-0444

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1, 781 x 2 lung radiomics and 13, 824 x 2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7% of accuracy, 90.9% of precision, 89.5% of F1-score, and 95.8% of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.

Biphasic CT Images COPD stage detection Auto-metric graph neural network Machine learning Lasso algorithm Radiomics

SCI ; EI

英语

通讯

Computer Science ; Engineering ; Mathematical & Computational Biology ; Medical Informatics

Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology ; Medical Informatics

WOS:001163538100001

SPRINGER HEIDELBERG

ENGINEERING

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.Shenzhen Lanmage Med Technol Co Ltd, Dept Radiol, 103 Baguang Serv Ctr, Shenzhen 518119, Guangdong, Peoples R China
4.Shenzhen Univ, Sch Appl Technol, Shenzhen 518060, Peoples R China
5.Guangzhou Med Univ, Affiliated Hosp 1, Natl Ctr Resp Med, Guangzhou Inst Resp Hlth,State Key Lab Resp Dis,Na, Guangzhou 510120, Peoples R China
6.Northeast Petr Univ, Sch Elect & Informat Engn, Daqing 163318, Peoples R China
7.Jinan Univ, Southern Univ Sci & Technol, Affiliated Hosp 1, Shenzhen Peoples Hosp,Clin Med Coll,Shenzhen Inst, Shenzhen 518001, Peoples R China
8.Minist Educ, Engn Res Ctr Med Imaging & Intelligent Anal, Shenyang 110169, Peoples R China

Deng, Xingguang,Li, Wei,Yang, Yingjian,et al. COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images[J]. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING,2024,62(6).

Deng, Xingguang.,Li, Wei.,Yang, Yingjian.,Wang, Shicong.,Zeng, Nanrong.,...&Kang, Yan.(2024).COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING,62(6).

Deng, Xingguang,et al."COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images".MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 62.6(2024).