Objective . Emphysema is characterized by the destruction and permanent enlargement of the alveoli in the lung. According to visual CT appearance, emphysema can be divided into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE), and paraseptal emphysema (PSE). Automating emphysema classification can help precisely determine the patterns of lung destruction and provide a quantitative evaluation. Approach . We propose a vision transformer (ViT) model to classify the emphysema subtypes via CT images. First, large patches (61 × 61) are cropped from CT images which contain the area of normal lung parenchyma, CLE, PLE, and PSE. After resizing, the large patch is divided into small patches and these small patches are converted to a sequence of patch embeddings by flattening and linear embedding. A class embedding is concatenated to the patch embedding, and the positional embedding is added to the resulting embeddings described above. Then, the obtained embedding is fed into the transformer encoder blocks to generate the final representation. Finally, the learnable class embedding is fed to a softmax layer to classify the emphysema. Main results . To overcome the lack of massive data, the transformer encoder blocks (pre-trained on ImageNet) are transferred and fine-tuned in our ViT model. The average accuracy of the pre-trained ViT model achieves 95.95% in our lab’s own dataset, which is higher than that of AlexNet, Inception-V3, MobileNet-V2, ResNet34, and ResNet50. Meanwhile, the pre-trained ViT model outperforms the ViT model without the pre-training. The accuracy of our pre-trained ViT model is higher than or comparable to that by available methods for the public dataset. Significance . The results demonstrated that the proposed ViT model can accurately classify the subtypes of emphysema using CT images. The ViT model can help make an effective computer-aided diagnosis of emphysema, and the ViT method can be extended to other medical applications.

Chronic obstructive pulmonary disease (COPD) is associated with morphologic abnormalities of airways with various patterns and severities. However, the way of effectively representing these abnormalities is lacking and whether these abnormalities enable to distinguish COPD from healthy controls is unknown. We propose to use deep convolutional neural network (CNN) to assess 3D lung airway tree from the perspective of computer vision, thereby constructing models of identifying COPD. After extracting airway trees from CT images, snapshots of their 3D visualizations are obtained from ventral, dorsal and isometric views. Using snapshots of each view, one deep CNN model is constructed and further optimized by Bayesian optimization algorithm to indentify COPD. The majority voting of three views presents the final prediction. Finally, the class-discriminative localization maps have been drawn to visually explain the CNNs' decisions. The models trained with single view (ventral, dorsal and isometric) of colorful snapshots present the similar accuracy (ACC) (86.8%, 87.5% and 86.7%) and the model after voting achieves the ACC of 88.2%. The ACC of the final voting model using gray and binary snapshots achieves 88.6% and 86.4%, respectively. Our specially designed CNNs outperform the typical off-the-shelf CNNs and the pre-trained CNNs with fine tuning. The class-discriminative regions of COPD are mainly located at central airways; however, regions in HC are scattering and located at peripheral airways. It is feasible to identify COPD using snapshots of 3D lung airway tree extracted from CT images via deep CNN. The CNNs can represent the abnormalities of airway tree in COPD and make accurate CT-based diagnosis of COPD.

Purpose: Although cigarette smoke exposure is the major risk factor for chronic obstructive pulmonary disease (COPD), the mechanism is not completely understood. The aim of the present study was to investigate whether ACSL4-mediated ferroptosis in lung epithelial cells plays a part in the COPD development process and its association. Patients and Methods: In this study, animal and cell models of COPD were modelled using cigarette smoke extracts (CSEs), and cell viability, lipid ROS, iron ion deposition, and ferroptosis-related markers were measured in lung tissue and lung epithelial cells following CSE exposure. Morphological changes in mitochondria were observed in lung tissue and epithelial cells of the lung by transmission electron microscope. The expression levels of ACSL4 mRNA and protein in lung tissue and epithelial cells were measured by real-time PCR and Western blotting. In addition, animal-interfering lentivirus and cell-interfering RNA against ACSL4 were constructed in this study, ferroptosis in lung tissue and lung epithelial cells after ACSL4 interference was detected, and ACSL4 mRNA and protein expression levels were detected. Results: CSE induced ferroptosis in lung tissues and lung epithelial cells, and the expression levels of ACSL4 were elevated in CSE-treated lung tissues and lung epithelial cells. After ACSL4 interference, the expression of ACSL4 decreased, mitochondrial morphology was restored, and ferroptosis in lung tissues and lung epithelial cells was alleviated. Both respiratory frequency and enhanced pause of COPD mice models decreased after ACSL4 interference. Conclusion: ACSL4-mediated ferroptosis in lung epithelial cells is associated with COPD and positively correlated with ferroptosis in epithelial cells.

This study aims to explore the effect of thymoquinone (TQ) on particulate matter 2.5 (PM2.5)-induced lung injury. The PM2.5 sample was provided by Shenyang Environment Monitor Central Station. Lung...