The value of radiomic features in chronic obstructive pulmonary disease assessment: a prospective study

Yang, Kai; Yang, Yuqiong; Kang, Yan; Liang, Zhenyu; Wang, F.; Li, Q.; Xu, Jiawen; Tang, Guoyan; Chen, R.

doi:10.1016/j.crad.2022.02.015

Cited by 9 publications

(11 citation statements)

References 31 publications

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“…Therefore, it is reasonable for dyspnea identification based on lung region HRCT images and effectively avoids the limitations of challenging segmentation tasks of small airways and vessels, which is conducive to clinical application. Besides, the value of lung radiomics features extracted from lung region HRCT images in COPD assessment has also been confirmed ( 16 ).…”

Section: Introductionmentioning

confidence: 81%

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

Yang

Chen

et al. 2022

Front. Med.

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IntroductionBecause 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.MethodsFirst, 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.ResultsOur 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.DiscussionCompared 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.

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Section: Introductionmentioning

confidence: 81%

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

Yang

Chen

et al. 2022

Front. Med.

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“…Moreover, wavelet transform can eliminate noise or sharpen the image, and this process does not alter its radiological features. 27 Therefore, compared to the non acute stage of asthma, the increase in these characteristic values in the acute stage group indicates a rougher texture and increased heterogeneity.…”

Section: Discussionmentioning

confidence: 98%

Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics

Chen,

Wang,

Huang

et al. 2024

JAA

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Objective To explore the value of a new model based on CT radiomics in predicting the staging of patients with bronchial asthma (BA). Methods Patients with BA from 2018 to 2021 were retrospectively analyzed and underwent plain chest CT before treatment. According to the guidelines for the prevention and treatment of BA (2016 edition), they were divided into two groups: acute attack and non-acute attack. The images were processed as follows: using Lung Kit software for image standardization and segmentation, using AK software for image feature extraction, and using R language for data analysis and model construction (training set: test set = 7: 3). The efficacy and clinical effects of the constructed model were evaluated with ROC curve, sensitivity, specificity, calibration curve and decision curve. Results A total of 112 patients with BA were enrolled, including 80 patients with acute attack (range: 2–86 years old, mean: 53.89±17.306 years old, males of 33) and 32 patients with non-acute attack (range: 4–79 years old, mean: 57.38±19.223 years old, males of 18). A total of 10 imaging features are finally retained and used to construct model using multi-factor logical regression method. In the training group, the AUC, sensitivity and specificity of the model was 0.881 (95% CI:0.808–0.955), 0.804 and 0.818, separately; while in the test group, it was 0.792 (95% CI:0.608–0.976), 0.792 and 0.80, respectively. Conclusion The model constructed based on radiomics has a good effect on predicting the staging of patients with BA, which provides a new method for clinical diagnosis of staging in BA patients.

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“…Since its introduction, radiomics has been extensively used in CT images with hundreds of millions of pixels to extract features [ 30 ]. Specifically, in COPD research, radiomics has shown promising potential in diagnosis and treatment, with its recognized usefulness [ 31 , 32 ]. A recent study shows that radiomics achieves near-to-standard-dose CT in predicting COPD disease on low-dose CT [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images

Wang,

Li,

Zeng

et al. 2024

Heliyon

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The value of radiomic features in chronic obstructive pulmonary disease assessment: a prospective study

Cited by 9 publications

References 31 publications

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

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

Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images

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