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Purpose: At present, there are few examination methods used to evaluate tracheobronchial cartilage damage. In our study, we explored whether endobronchial optical coherence tomography (EB-OCT) can be used to estimate central airway cartilage damage in tracheobronchial tuberculosis (TBTB) patients. Methods: In our study, we used the OCTICS Imaging system to perform EB-OCT scanning for TBTB patients. The thickness of the central airway wall and cartilage was measured by the OCTICS software system workstation. Results: There were 102 TBTB patients included in our study cohort. Their EB-OCT images of the central airway cartilage showed that abnormal cartilage manifests as thinning of the cartilage, cartilage damage, cartilage destruction, and even cartilage deficiency. The cartilage morphology becomes irregular and discontinuous. Some parts of the cartilage become brighter in grayscale. The intima of the cartilage is thickened and discontinuous, and the boundary with submucosa and mucosa is unclear. Conclusion: Our study conducted EB-OCT examination of the central airway cartilage of TBTB patients in vivo for the first time. EB-OCT helps to estimate the cartilage damage of the central airway in TBTB patients to some extent.

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Lung cancer is the leading cause of cancer-related death in China and the world, mainly attributed to delayed diagnosis, given that currently available early screening strategies exhibit limited value. Endobronchial optical coherence tomography (EB-OCT) has the characteristics of non-invasiveness, accuracy, and repeatability. Importantly, the combination of EB-OCT with existing technologies represents a potential approach for early screening and diagnosis. In this review, we introduce the structure and strengths of EB-OCT. Furthermore, we provide a comprehensive overview of the application of EB-OCT on early screening and diagnosis of lung cancer from in vivo experiments to clinical studies, including differential diagnosis of airway lesions, early screening for lung cancer, lung nodules, lymph node biopsy and localization and palliative treatment of lung cancer. Moreover, the bottlenecks and difficulties in developing and popularizing EB-OCT for diagnosis and treatment during clinical practice are analyzed. The characteristics of OCT images of normal and cancerous lung tissues were in good agreement with the results of pathology, which could be used to judge the nature of lung lesions in real time. In addition, EB-OCT can be used as an assistant to biopsy of pulmonary nodules and improve the success rate of biopsy. EB-OCT also plays an auxiliary role in the treatment of lung cancer. In conclusion, EB-OCT is non-invasive, safe and accurate in real-time. It is of great significance in the diagnosis of lung cancer and suitable for clinical application and is expected to become an important diagnostic method for lung cancer in the future.

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BACKGROUND: Manual measurement of endobronchial optical coherence tomography (EB-OCT) images means a heavy workload in the clinical practice, which can also introduce bias if the subjective opinions of doctors are involved. OBJECTIVE: We aim to develop a convolutional neural network (CNN)-based EB-OCT image analysis algorithm to automatically identify and measure EB-OCT parameters of airway morphology. METHODS: The ResUNet, MultiResUNet, and Siamese network were used for analyzing airway inner area (Ai), airway wall area (Aw), airway wall area percentage (Aw%), and airway bifurcate segmentation obtained from EB-OCT imaging, respectively. The accuracy of the automatic segmentations was verified by comparing with manual measurements. RESULTS: Thirty-three patients who were diagnosed with asthma (n = 13), chronic obstructive pulmonary disease (COPD, n = 13), and normal airway (n = 7) were enrolled. EB-OCT was performed in RB9 segment (lateral basal segment of the right lower lobe), and a total of 17,820 OCT images were collected for CNN training, validation, and testing. After training, the Ai, Aw, and airway bifurcate were readily identified in both normal airway and airways of asthma and COPD. The ResUNet and the MultiResUNet resulted in a mean dice similarity coefficient of 0.97 and 0.95 for Ai and Aw segmentation. The accuracy Siamese network in identifying airway bifurcate was 96.6%. Bland-Altman analysis indicated there was a negligible bias between manual and CNN measurements for Ai (bias = -0.02 to 0.01, 95% CI = -0.12 to 0.14) and Aw% (bias = -0.06 to 0.12, 95% CI = -1.98 to 2.14). CONCLUSION: EB-OCT imaging in conjunction with ResUNet, MultiResUNet, and Siamese network could automatically measure normal and diseased airway structure with an accurate performance.

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BACKGROUND: Although FEV1 remains the gold standard for staging COPD, the association between airway remodeling and airflow limitation remains unclear. Endobronchial optical coherence tomography (EB-OCT) was performed to assess the association between disorders of large and medium to small airways and COPD staging. We also evaluated small airway architecture in heavy smokers with normal FEV1 (SNL) and healthy never-smokers. METHODS: We recruited 48 patients with COPD (stage I, n = 14; stage II, n = 15; stage, III-IV, n = 19), 21 SNL, and 17 healthy never-smokers. A smoking history inquiry, as well as spirometry, chest CT, bronchoscopy, and EB-OCT were performed. Mean luminal diameter (Dmean), inner luminal area (Ai), and airway wall area (Aw) of third- to ninth-generation bronchi were measured using EB-OCT. RESULTS: Patients with more advanced COPD demonstrated greater abnormality of airway architecture in both large and medium to small airways, followed by SNL and never-smokers. Abnormality of airway architecture and EB-OCT parameters in SNL were comparable to those in stage I COPD. FEV1% predicted correlated with Dmean and Ai of seventh- to ninth-generation bronchi in COPD; however, neither Dmean nor Ai of third- to sixth-generation bronchi correlated with FEV1% in stage I and stage II COPD and in SNL. CONCLUSIONS: FEV1-based COPD staging partially correlates with small airway disorders in stage II-IV COPD. Small airway abnormalities detected by EB-OCT correlate with FEV1-based staging in COPD and identify early pathologic changes in healthy heavy smokers.

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