Mathematical Biosciences and Engineering (Feb 2022)
A novel lung radiomics feature for characterizing resting heart rate and COPD stage evolution based on radiomics feature combination strategy
Abstract
The resting HR is an upward trend with the development of chronic obstructive pulmonary disease (COPD) severity. Chest computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying COPD. Therefore, CT images should provide more information to analyze the lung and heart relationship. The relationship between HR variability and PFT or/and COPD has been fully revealed, but the relationship between resting HR variability and COPD radiomics features remains unclear. 231 sets of chest high-resolution CT (HRCT) images from "COPD patients" (at risk of COPD and stage I to IV) are segmented by the trained lung region segmentation model (ResU-Net). Based on the chest HRCT images and lung segmentation images, 231 sets of the original lung parenchyma images are obtained. 1316 COPD radiomics features of each subject are calculated by the original lung parenchyma images and its derived lung parenchyma images. The 13 selected COPD radiomics features related to the resting HR are generated from the Lasso model. A COPD radiomics features combination strategy is proposed to satisfy the significant change of the lung radiomics feature among the different COPD stages. Results show no significance between COPD stage Ⅰ and COPD stage Ⅱ of the 13 selected COPD radiomics features, and the lung radiomics feature Y1-Y4 (P > 0.05). The lung radiomics feature F2 with the dominant selected COPD radiomics features based on the proposed COPD radiomics features combination significantly increases with the development of COPD stages (P < 0.05). It is concluded that the lung radiomics feature F2 with the dominant selected COPD radiomics features not only can characterize the resting HR but also can characterize the COPD stage evolution.
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