Artificial Intelligence-based CT Assessment of Bronchiectasis: The COPDGene Study

Advances in COPD imaging using CT and MRI: linkage with lung physiology and clinical outcomes

Recent years have witnessed major advances in lung imaging in patients with COPD. These include significant refinements in images obtained by computed tomography (CT) scans together with the introduction of new techniques and software that aim for obtaining the best image whilst using the lowest possible radiation dose. Magnetic resonance imaging (MRI) has also emerged as a useful radiation-free tool in assessing structural and more importantly functional derangements in patients with well-established COPD and smokers without COPD, even before the existence of overt changes in resting physiological lung function tests. Together, CT and MRI now allow objective quantification and assessment of structural changes within the airways, lung parenchyma and pulmonary vessels. Furthermore, CT and MRI can now provide objective assessments of regional lung ventilation and perfusion, and multinuclear MRI provides further insight into gas exchange; this can help in structured decisions regarding treatment plans. These advances in chest imaging techniques have brought new insights into our understanding of disease pathophysiology and characterising different disease phenotypes. The present review discusses, in detail, the advances in lung imaging in patients with COPD and how structural and functional imaging are linked with common resting physiological tests and important clinical outcomes.

[The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]

The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.

Clinical outcomes of long-term inhaled combination therapies in patients with bronchiectasis and airflow obstruction

BACKGROUND AND OBJECTIVES

Few studies have reported which inhaled combination therapy, either bronchodilators and/or inhaled corticosteroids (ICSs), is beneficial in patients with bronchiectasis and airflow obstruction. Our study compared the efficacy and safety among different inhaled combination therapies in patients with bronchiectasis and airflow obstruction.

METHODS

Our retrospective study analyzed the patients with forced expiratory volume in 1 s (FEV1)/forced vital capacity < 0.7 and radiologically confirmed bronchiectasis in chest computed tomography between January 2005 and December 2021. The eligible patients underwent baseline and follow-up spirometric assessments. The primary endpoint was the development of a moderate-to-severe exacerbation. The secondary endpoints were the change in the annual FEV1 and the adverse events. Subgroup analyses were performed according to the blood eosinophil count (BEC).

RESULTS

Among 179 patients, the ICS/long-acting beta-agonist (LABA)/long-acting muscarinic antagonist (LAMA), ICS/LABA, and LABA/LAMA groups were comprised of 58 (32.4%), 52 (29.1%), and 69 (38.5%) patients, respectively. ICS/LABA/LAMA group had a higher severity of bronchiectasis and airflow obstruction, than other groups. In the subgroup with BEC ≥ 300/uL, the risk of moderate-to-severe exacerbation was lower in the ICS/LABA/LAMA group (adjusted HR = 0.137 [95% CI = 0.034-0.553]) and the ICS/LABA group (adjusted HR = 0.196 [95% CI = 0.045-0.861]) compared with the LABA/LAMA group. The annual FEV1 decline rate was significantly worsened in the ICS/LABA group compared to the LABA/LAMA group (adjusted β-coefficient=-197 [95% CI=-307--87]) in the subgroup with BEC < 200/uL.

CONCLUSION

In patients with bronchiectasis and airflow obstruction, the use of ICS/LABA/LAMA and ICS/LABA demonstrated a reduced risk of exacerbation compared to LABA/LAMA therapy in those with BEC ≥ 300/uL. Conversely, for those with BEC < 200/uL, the use of ICS/LABA was associated with an accelerated decline in FEV1 in comparison to LABA/LAMA therapy. Further assessment of BEC is necessary as a potential biomarker for the use of ICS in patients with bronchiectasis and airflow obstruction.

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

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.

Automatic analysis of bronchus-artery dimensions to diagnose and monitor airways disease in cystic fibrosis

BACKGROUND

Cystic fibrosis (CF) lung disease is characterised by progressive airway wall thickening and widening. We aimed to validate an artificial intelligence-based algorithm to assess dimensions of all visible bronchus-artery (BA) pairs on chest CT scans from patients with CF.

METHODS

The algorithm fully automatically segments the bronchial tree; identifies bronchial generations; matches bronchi with the adjacent arteries; measures for each BA-pair bronchial outer diameter (Bout), bronchial lumen diameter (Bin), bronchial wall thickness (Bwt) and adjacent artery diameter (A); and computes Bout/A, Bin/A and Bwt/A for each BA pair from the segmental bronchi to the last visible generation. Three datasets were used to validate the automatic BA analysis. First BA analysis was executed on 23 manually annotated CT scans (11 CF, 12 control subjects) to compare automatic with manual BA-analysis outcomes. Furthermore, the BA analysis was executed on two longitudinal datasets (Copenhagen 111 CTs, ataluren 347 CTs) to assess longitudinal BA changes and compare them with manual scoring results.

RESULTS

The automatic and manual BA analysis showed no significant differences in quantifying bronchi. For the longitudinal datasets the automatic BA analysis detected 247 and 347 BA pairs/CT in the Copenhagen and ataluren dataset, respectively. A significant increase of 0.02 of Bout/A and Bin/A was detected for Copenhagen dataset over an interval of 2 years, and 0.03 of Bout/A and 0.02 of Bin/A for ataluren dataset over an interval of 48 weeks (all p<0.001). The progression of 0.01 of Bwt/A was detected only in the ataluren dataset (p<0.001). BA-analysis outcomes showed weak to strong correlations (correlation coefficient from 0.29 to 0.84) with manual scoring results for airway disease.

CONCLUSION

The BA analysis can fully automatically analyse a large number of BA pairs on chest CTs to detect and monitor progression of bronchial wall thickening and bronchial widening in patients with CF.

Suspected Bronchiectasis and Mortality in Adults With a History of Smoking Who Have Normal and Impaired Lung Function : A Cohort Study

BACKGROUND

Bronchiectasis in adults with chronic obstructive pulmonary disease (COPD) is associated with greater mortality. However, whether suspected bronchiectasis-defined as incidental bronchiectasis on computed tomography (CT) images plus clinical manifestation-is associated with increased mortality in adults with a history of smoking with normal spirometry and preserved ratio impaired spirometry (PRISm) is unknown.

OBJECTIVE

To determine the association between suspected bronchiectasis and mortality in adults with normal spirometry, PRISm, and obstructive spirometry.

DESIGN

Prospective, observational cohort.

SETTING

The COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease) study.

PARTICIPANTS

7662 non-Hispanic Black or White adults, aged 45 to 80 years, with 10 or more pack-years of smoking history. Participants who were former and current smokers were stratified into normal spirometry (n = 3277), PRISm (n = 986), and obstructive spirometry (n = 3399).

MEASUREMENTS

Bronchiectasis identified by CT was ascertained using artificial intelligence-based measurements of an airway-to-artery ratio (AAR) greater than 1 (AAR >1), a measure of bronchial dilatation. The primary outcome of "suspected bronchiectasis" was defined as an AAR >1 of greater than 1% plus 2 of the following: cough, phlegm, dyspnea, and history of 2 or more exacerbations.

RESULTS

Among the 7662 participants (mean age, 60 years; 52% women), 1352 (17.6%) had suspected bronchiectasis. During a median follow-up of 11 years, 2095 (27.3%) died. Ten-year mortality risk was higher in participants with suspected bronchiectasis, compared with those without suspected bronchiectasis (normal spirometry: difference in mortality probability [Pr], 0.15 [95% CI, 0.09 to 0.21]; PRISm: Pr, 0.07 [CI, -0.003 to 0.15]; obstructive spirometry: Pr, 0.06 [CI, 0.03 to 0.09]). When only CT was used to identify bronchiectasis, the differences were attenuated in the normal spirometry (Pr, 0.04 [CI, -0.001 to 0.08]).

LIMITATIONS

Only 2 racial groups were studied. Only 1 measurement was used to define bronchiectasis on CT. Symptoms of suspected bronchiectasis were nonspecific.

CONCLUSION

Suspected bronchiectasis was associated with a heightened risk for mortality in adults with normal and obstructive spirometry.

PRIMARY FUNDING SOURCE

National Heart, Lung, and Blood Institute.