Automated prediction of emphysema visual score using homology-based quantification of low-attenuation lung region

Nishio, Motohiro; Nakane, Kazuaki; Kubo, Takeshi; Yakami, Masahiro; Emoto, Yutaka; Nishio, Masaki; Togashi, Kaori

doi:10.1371/journal.pone.0178217

Cited by 11 publications

(19 citation statements)

References 26 publications

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“…LAA% was potently related to pulmonary function variables and RB10 WAR, but also to the numerous laboratory factors, including blood neutrophilia, RBC, serum HDL, IL‐12p70, and IL‐17A. Thus, that simple parameter, calculated automatically by the majority of CT software, may be useful in asthma research.…”

Section: Discussionmentioning

confidence: 99%

“…We compared CT‐derived dimensions of proximal and distal airways, and measured low‐attenuation lung area (LAA%) by the conventional threshold level of −950 Hounsfield units, to show which of those indices correlate best with lung function, histology, and asthma biomarkers. The LAA% determines the percentile quantity of voxels showing a value lower than preset attenuation from all voxels within lung tissue and is usually employed for evaluation of emphysema . In asthma, however, it might be also related to the excessive narrowing of large and small airways with subsequent air trapping and hyperinflation .…”

Section: Introductionmentioning

confidence: 99%

“…The LAA% determines the percentile quantity of voxels showing a value lower than preset attenuation from all voxels within lung tissue and is usually employed for evaluation of emphysema . In asthma, however, it might be also related to the excessive narrowing of large and small airways with subsequent air trapping and hyperinflation . Standard air trapping measurement requires inspiratory and expiratory CT scans with exposure to a double radiation dose.…”

Section: Introductionmentioning

confidence: 99%

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The relationship of airway structural changes to blood and bronchoalveolar lavage biomarkers, and lung function abnormalities in asthma

Koźlik

Zuk

Bartyzel

et al. 2019

Clin Experimental Allergy

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Background: Airway structural changes are important in asthma pathology and require further investigations.Objective: We sought to evaluate which computed tomography (CT) indices, bronchial histological traits, or blood and bronchoalveolar lavage (BAL) biomarkers correlate best with lung function abnormalities in asthma. Methods:In 105 white adult asthmatics (53 with a component of fixed airflow obstruction), we determined airway cross-sectional geometry of two proximal (the right upper lobe apical segmental and the left apicoposterior) and two distal (the right and the left basal posterior) bronchi, quantified the low-attenuation lung area (LAA%), and analysed clusters based on airway CT-metrics. We also performed bronchofiberoscopy with BAL and endobronchial biopsy, assessed blood and BAL biomarkers, including interleukin (IL)-4, IL-5, IL-6, IL-10, IL-12p70, IL-17A, IL-23, interferon (INF)γ and periostin, together with circulating a disintegrin and metalloproteinase domain-containing protein (ADAM)33, and investigated interplays between analysed variables.Results: Patients with fixed airflow limitation were characterized by lower lumen area and increased wall area and wall thickness ratios in distal airways, accompanied by raised LAA%. They had also higher blood neutrophilia, blood and BAL eosinophilia, increased circulating fibrinogen, periostin, and ADAM33. Blood neutrophilia, serum high density lipoproteins, thyroid-stimulating hormone, and shortened activated partial thromboplastin time were determinants of thicker reticular basement membrane (RBM). BAL eosinophilia was the only positive predictor of collagen I accumulation. Surprisingly, we observed a negative correlation between RBM thickening and collagen I deposit. Cluster analysis based on CT-metrics of the right lower lobe basal posterior bronchus revealed three well-separated clusters similar in age, asthma duration, and BMI, but different in RBM thickness, collagen I accumulation, and inflammatory markers.Conclusions and clinical relevance: Airway remodelling traits are mainly related to the Th 2 profile, higher circulating ADAM33, and blood neutrophilia. Lung function

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The relationship of airway structural changes to blood and bronchoalveolar lavage biomarkers, and lung function abnormalities in asthma

Koźlik

Zuk

Bartyzel

et al. 2019

Clin Experimental Allergy

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“…Homology is a mathematical concept that can be used as a basis for measuring the degree of connectivity (a more detailed explanation is provided in Section 2.B) . We previously applied this technique to evaluate the spatial distribution of low‐attenuation lung regions in patients with and without chronic obstructive pulmonary disease and showed that homology‐based emphysema quantification was useful for assessing emphysema severity . We hypothesized that homology‐based features, which can detect detailed information within a tumor, are superior to the radiomic features proposed in a previous work.…”

Section: Introductionmentioning

confidence: 99%

Homology‐based radiomic features for prediction of the prognosis of lung cancer based on CT‐based radiomics

et al. 2020

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Purpose: Radiomics is a new technique that enables noninvasive prognostic prediction by extracting features from medical images. Homology is a concept used in many branches of algebra and topology that can quantify the contact degree. In the present study, we developed homology-based radiomic features to predict the prognosis of non-small-cell lung cancer (NSCLC) patients and then evaluated the accuracy of this prediction method. Methods: Four datasets were used: two to provide training and test data and two for the selection of robust radiomic features. All the datasets were downloaded from The Cancer Imaging Archive (TCIA). In two-dimensional cases, the Betti numbers consist of two values: b 0 (zero-dimensional Betti number), which is the number of isolated components, and b 1 (one-dimensional Betti number), which is the number of one-dimensional or "circular" holes. For homology-based evaluation, computed tomography (CT) images must be converted to binarized images in which each pixel has two possible values: 0 or 1. All CT slices of the gross tumor volume were used for calculating the homology histogram. First, by changing the threshold of the CT value (range: À150 to 300 HU) for all its slices, we developed homology-based histograms for b 0 , b 1 , and b 1 /b 0 using binarized images. All histograms were then summed, and the summed histogram was normalized by the number of slices. 144 homology-based radiomic features were defined from the histogram. To compare the standard radiomic features, 107 radiomic features were calculated using the standard radiomics technique.To clarify the prognostic power, the relationship between the values of the homology-based radiomic features and overall survival was evaluated using LASSO Cox regression model and the Kaplan-Meier method. The retained features with nonzero coefficients calculated by the LASSO Cox regression model were used for fitting the regression model. Moreover, these features were then integrated into a radiomics signature. An individualized rad score was calculated from a linear combination of the selected features, which were weighted by their respective coefficients. Results: When the patients in the training and test datasets were stratified into high-risk and low-risk groups according to the rad scores, the overall survival of the groups was significantly different. The C-index values for the homology-based features (rad score), standard features (rad score), and tumor size were 0.625, 0.603, and 0.607, respectively, for the training datasets and 0.689, 0.668, and 0.667 for the test datasets. This result showed that homology-based radiomic features had slightly higher prediction power than the standard radiomic features. Conclusions: Prediction performance using homology-based radiomic features had a comparable or slightly higher prediction power than standard radiomic features. These findings suggest that homology-based radiomic features may have great potential for improving the prognostic prediction accuracy of CT-based radiomics. In this result, i...

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“…Lung CT scans can reveal emphysema and visual scoring can be used to rate the extent and type of emphysema in the lungs [13]. Visual scores can be used for training classifiers to automatically assess presence and extent of emphysema [14,9]. However, visual scoring of emphysema by experts is both expensive and prone to high rater disagreement [13].…”

Section: Introductionmentioning

confidence: 99%

Crowdsourced Emphysema Assessment

Ørting

Cheplygina

Petersen

et al. 2017

Lecture Notes in Computer Science

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Automated prediction of emphysema visual score using homology-based quantification of low-attenuation lung region

Cited by 11 publications

References 26 publications

The relationship of airway structural changes to blood and bronchoalveolar lavage biomarkers, and lung function abnormalities in asthma

The relationship of airway structural changes to blood and bronchoalveolar lavage biomarkers, and lung function abnormalities in asthma

Homology‐based radiomic features for prediction of the prognosis of lung cancer based on CT‐based radiomics

Crowdsourced Emphysema Assessment

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