Development of a Radiomics Model for predicting COPD Exacerbations Based on Complementary Visual Information

Liang, Cho–Chung; Xu, Jianping; Wang, F.; Chen, H.; Tang, J.; Chen, D.; Li, Q.; Jian, Wenhua; Tang, Guosheng; Zheng, Jianbin; Chen, R.; Liang, Zhonglin

doi:10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a2296

Cited by 5 publications

(3 citation statements)

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“…The results indicated the model has good predictive performance for new, unfitted data, as well as its high prediction capability and robustness. These findings are consistent with others utilizing radiomic to predict COPD survival [ 23 , 27 ], spirometry-based evaluation of emphysema and severity [ 28 ], COPD exacerbations [ 29 ], COPD stage classification [ 30 , 31 ], and analysis of COPD and resting heart rate [ 32 ].…”

Section: Discussionsupporting

confidence: 89%

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

Zhou,

et al. 2024

Military Med Res

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Background Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients. Methods This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts. Results Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869–0.906], 0.874 (95%CI 0.844–0.904) and 0.846 (95%CI 0.822–0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model. Conclusions The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.

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Section: Discussionsupporting

confidence: 89%

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

Zhou,

et al. 2024

Military Med Res

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“…However, radiomics in COPD has not been extensively investigated yet. Currently, there are only potential applications of radiomics features in COPD for the diagnosis, treatment, and follow-up of COPD and future directions ( 32 ). In particular, lung radiomics features as an imaging biomarker that reflects the state of lung parenchyma should be applied to COPD risk evaluation for an early COPD risk decision.…”

Section: Introductionmentioning

confidence: 99%

Early COPD Risk Decision for Adults Aged From 40 to 79 Years Based on Lung Radiomics Features

Yang

Guo

et al. 2022

Front. Med.

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BackgroundChronic obstructive pulmonary disease (COPD), a preventable lung disease, has the highest prevalence in the elderly and deserves special consideration regarding earlier warnings in this fragile population. The impact of age on COPD is well known, but the COPD risk of the aging process in the lungs remains unclear. Therefore, it is necessary to understand the COPD risk of the aging process in the lungs, providing an early COPD risk decision for adults.MethodsCOPD risk is evaluated for adults to make an early COPD risk decision from the perspective of lung radiomics features. First, the subjects are divided into four groups according to the COPD stages. Their ages are divided into eight equal age intervals in each group. Second, four survival Cox models are established based on the lung radiomics features to evaluate the risk probability from COPD stage 0 to suffering COPD and COPD stages. Finally, four risk ranks are defined by equally dividing the COPD risk probability from 0 to 1. Subsequently, the COPD risk at different stages is evaluated with varying age intervals to provide an early COPD risk decision.ResultsThe evaluation metrics area under the curve (AUC)/C index of four survival Cox models are 0.87/0.94, 0.84/0.83, 0.94/0.89, and 0.97/0.86, respectively, showing the effectiveness of the models. The risk rank levels up every 5 years for the subjects who had suffered COPD after 60. For the subjects with COPD stage 0, the risk rank of suffering COPD stage I levels up every 5 years after the age of 65 years, and the risk rank of suffering COPD stages II and III & IV levels up every 5 years after the age of 70 years.ConclusionOnce the age is above 60 years, the patients with COPD need to take action to prevent the progress and deterioration of COPD. Once the age is above 65 years, the patients with COPD stage 0 need to take precautions against COPD.

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“…Currently, radiomics features have also been used in COPD for survival prediction [ 21 , 22 ], spirometric assessment of emphysema presence and severity [ 23 ], COPD exacerbations [ 24 ], COPD early decision [ 3 ], COPD stage classification [ 25 , 26 ], COPD prediction [ 27 , 28 ], and analysis of COPD and resting heart rate [ 29 ]. The convolutional neural networks (CNN) and machine learning (ML) models can implement the COPD stage classification task.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Development of a Radiomics Model for predicting COPD Exacerbations Based on Complementary Visual Information

Cited by 5 publications

References 0 publications

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

Early COPD Risk Decision for Adults Aged From 40 to 79 Years Based on Lung Radiomics Features

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

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