Traqueobroncopatía osteocondroplástica

Juanola, Jordi; Cabezas, Tatiana Rejon; Castillo, María Pilar Ortega

doi:10.1016/j.arbres.2019.03.015

Cited by 4 publications

(1 citation statement)

References 2 publications

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“… 3 Although TO is rare, there have been many reports in which it was occasionally diagnosed. 1 , 4 – 27 To date, only a limited number of studies have utilized extensive data to conduct a comprehensive investigation of this disease. 3 , 28 …”

Section: Introductionmentioning

confidence: 99%

Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images

Chen,

Tang,

Herth

et al. 2024

Ther Adv Respir Dis

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Background: Given the rarity of tracheobronchopathia osteochondroplastica (TO), many young doctors in primary hospitals are unable to identify TO based on bronchoscopy findings. Objectives: To build an artificial intelligence (AI) model for differentiating TO from other multinodular airway diseases by using bronchoscopic images. Design: We designed the study by comparing the imaging data of patients undergoing bronchoscopy from January 2010 to October 2022 by using EfficientNet. Bronchoscopic images of 21 patients with TO at Anhui Chest Hospital from October 2019 to October 2022 were collected for external validation. Methods: Bronchoscopic images of patients with multinodular airway lesions (including TO, amyloidosis, tumors, and inflammation) and without airway lesions in the First Affiliated Hospital of Guangzhou Medical University were collected. The images were randomized (4:1) into training and validation groups based on different diseases and utilized for deep learning by convolutional neural networks (CNNs). Results: We enrolled 201 patients with multinodular airway disease (38, 15, 75, and 73 patients with TO, amyloidosis, tumors, and inflammation, respectively) and 213 without any airway lesions. To find multinodular lesion images for deep learning, we utilized 2183 bronchoscopic images of multinodular lesions (including TO, amyloidosis, tumor, and inflammation) and compared them with images without any airway lesions (1733). The accuracy of multinodular lesion identification was 98.9%. Further, the accuracy of TO detection based on the bronchoscopic images of multinodular lesions was 89.2%. Regarding external validation (using images from 21 patients with TO), all patients could be diagnosed with TO; the accuracy was 89.8%. Conclusion: We built an AI model that could differentiate TO from other multinodular airway diseases (mainly amyloidosis, tumors, and inflammation) by using bronchoscopic images. The model could help young physicians identify this rare airway disease.

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Section: Introductionmentioning

confidence: 99%

Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images

Chen,

Tang,

Herth

et al. 2024

Ther Adv Respir Dis

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Clinicopathological Features and Management Principles of Tracheobronchopathia Osteochondroplastica - A Scoping Review

Devaraja

Surendra

2023

Indian J Otolaryngol Head Neck Surg

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Clinical Characteristics of Tracheobronchopathia Osteochondroplastica: A Retrospective Study of 33 Patients

Liu¹,

H²,

Lei³

et al. 2023

IJGM

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Tracheobronchopathia osteochondroplastica (TO) is a relatively rare benign tracheobronchial disease, which is often misdiagnosed or missed. To date, there is no specific treatment for TO. The aim of this study was to investigate the clinical manifestations, imaging features, bronchoscopy results, pathological findings, and diagnostic points of TO. Patients and methods: A total of 33 patients diagnosed with TO were enrolled. Clinical data were collected using retrospective methods in the period from January 2021 and November 2022. Descriptive analysis was used. Results: Patients included 17 (51.5%) male and 16 (48.5%) female, with a median age of 54 years. The main clinical manifestations included cough in 15 cases, fever in 6 cases, chest tightness in 4 cases, haemoptysis in 3 cases, and chest pain in 4 cases. The time from the onset of symptoms to diagnosis was 1 week to 96 months. Some patients were diagnosed with other lung diseases, including 16 patients with tuberculosis, 2 patients with lung cancer, 3 patients with nontuberculous mycobacteriosis, 3 patients with tuberculous pleurisy, 2 patients with bronchiectasis, and 1 patient with pneumonia. Chest computed tomography (CT) scan demonstrated calcified nodules in 10 (30.3%) patients. In bronchoscopy, entire tracheal involvement was found in 21 (63.6%) patients, 12 (36.4%) patients were found to have involvement of only part of the trachea. The patients were divided into three groups according to the bronchoscopic presentation, the largest proportion was stage II (19/33), followed by stage I (8/33) and stage III (6/33). Histopathological findings showed squamous metaplasia, cartilaginous, and bony tissues. Conclusion: TO is a slowly progressing disease with non-specific clinical symptoms and a low positive rate of imaging diagnosis, making it susceptible to misdiagnosis and missed diagnosis. The disease needs to be diagnosed by combining imaging features, fiberoptic bronchoscopy, and pathological findings.

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Traqueobroncopatía osteocondroplástica

Cited by 4 publications

References 2 publications

Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images

Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images

Clinicopathological Features and Management Principles of Tracheobronchopathia Osteochondroplastica - A Scoping Review

Clinical Characteristics of Tracheobronchopathia Osteochondroplastica: A Retrospective Study of 33 Patients

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