Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network

Yang，Yingjian1,2; Wang，Shicong2,3; Zeng，Nanrong2,3; Duan，Wenxin2,3; Chen，Ziran2; Liu，Yang2; Li，Wei2; Guo，Yingwei1,2; Chen，Huai4; Li，Xian4; Chen，Rongchang5,6,7; Kang，Yan1,2,3,8

2022-10-01

10.3390/diagnostics12102274

Diagnostics   Impact Factor & Quartile

2075-4418

Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification.

auto-metric graph neural network (AMGNN) chest HRCT image COPD stage (GOLD) generalized linear model (GLM) Lasso algorithm lung radiomics features multi-classification

SCI

English

Corresponding

Natural Science Foundation of Guangdong Province[2019A1515011382];National Natural Science Foundation of China[62071311];Scientific Research Fund of Liaoning Provincial Education Department[JL201919];

General & Internal Medicine

Medicine, General & Internal

WOS:000872676900001

MDPI

2-s2.0-85140767309

Scopus

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.School of Applied Technology,Shenzhen University,Shenzhen,518060,China
4.Department of Radiology,the First Affiliated Hospital of Guangzhou Medical University,Guangzhou,510120,China
5.Shenzhen Institute of Respiratory Diseases,Shenzhen People’s Hospital,Shenzhen,518001,China
6.The Second Clinical Medical College,Jinan University,Guangzhou,518001,China
7.The First Affiliated Hospital,Southern University of Science and Technology,Shenzhen,518001,China
8.Engineering Research Centre of Medical Imaging and Intelligent Analysis,Ministry of Education,Shenyang,110169,China

Yang，Yingjian,Wang，Shicong,Zeng，Nanrong,et al. Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network[J]. Diagnostics,2022,12(10).

Yang，Yingjian.,Wang，Shicong.,Zeng，Nanrong.,Duan，Wenxin.,Chen，Ziran.,...&Kang，Yan.(2022).Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network.Diagnostics,12(10).

Yang，Yingjian,et al."Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network".Diagnostics 12.10(2022).