Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study

Yang, Jie; Angelini, Elsa D.; Balte, Pallavi; Hoffman, Eric A.; Austin, John; Smith, Benjamin M.; Song, Jingkuan; Barr, R. Graham; Laine, Andrew F.

doi:10.1007/978-3-319-66182-7_14

Cited by 16 publications

(14 citation statements)

References 12 publications

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“…The strong relationship between the model, pulmonary function test results, and MRI VDP also provides support that this model would predict a wide range of disease severity present within our study. Our results are important in the context of previous automated disease quantification methods developed by using texture analysis (28)(29)(30)(31), which were trained by using unsupervised learning or with previously developed disease classification systems. In contrast, our predicted model provided a quantitative measure that was spatially dependent and trained by using HP 3 He MRI ventilation results as the ground truth.…”

Section: Discussionmentioning

confidence: 88%

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation

et al. 2019

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Background: Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose: To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods: In this prospective study (ClinicalTrials.gov: NCT02723474; conducted from January 2010 to February 2017), participants were randomized to optimization (n = 1), training (n = 67), and testing (n = 27) data sets. Hyperpolarized (HP) helium 3 (3 He) MRI ventilation maps were co-registered with thoracic CT to provide ground truth labels, and 87 quantitative imaging features were extracted and normalized to lung averages to generate 174 features. The volume-of-interest dimension and the training data sampling method were optimized to maximize the area under the receiver operating characteristic curve (AUC). Forward feature selection was performed to reduce the number of features; logistic regression, linear support vector machine, and quadratic support vector machine classifiers were trained through fivefold cross validation. The highest-performing classification model was applied to the test data set. Pearson coefficients were used to determine the relationships between the model, MRI, and pulmonary function measurements. Results: The quadratic support vector machine performed best in training and was applied to the test data set. Model-predicted ventilation maps had an accuracy of 88% (95% confidence interval [CI]: 88%, 88%) and an AUC of 0.82 (95% CI: 0.82, 0.83) when the HP 3 He MRI ventilation maps were used as the reference standard. Model-predicted ventilation defect percentage (VDP) was correlated with VDP at HP 3 He MRI (r = 0.90, P , .001). Both model-predicted and HP 3 He MRI VDP were correlated with forced expiratory volume in 1 second (FEV 1

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Section: Discussionmentioning

confidence: 88%

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation

et al. 2019

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“…For example, LAA is insu cient to distinguish emphysema visual subtypes because it merely counts the number of pixels in the lung region of CT images with intensity values lower than a single threshold. Since more sophisticated imaging descriptors (e.g., texture) are shown to be e↵ective for emphysema sub-typing [63][64][65], we hypothesize that such descriptors are also associated with the systemic characterization of the disease such as PBMCs and gene expression. In this paper, we used a previously developed method [11] that uses image texture and intensity value and constructs a multivariate vector for each patient.…”

Section: Discussionmentioning

confidence: 99%

Unpaired Data Empowers Association Tests

Gong

Liu

Sciurba

et al. 2019

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To achieve a holistic view of the underlying mechanisms of human diseases, the biomedical research community is moving toward harvesting retrospective data available in Electronic Healthcare Records (EHRs). The first step for causal understanding is to perform association tests between types of potentially high-dimensional biomedical data, such as genetic, blood biomarkers, and imaging data. To obtain a reasonable power, current methods require a substantial sample size of individuals with both data modalities. This prevents researchers from using much larger EHR samples that include individuals with at least one data type, limits the power of the association test, and may result in higher false discovery rate. We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and, under some conditions, improve the association test power. We study the properties of SAT theoretically and empirically, through simulations and application to real studies in the context of Chronic Obstructive Pulmonary Disease. Our method identifies an association between the high-dimensional characterization of Computed Tomography (CT) chest images and blood biomarkers as well as the expression of dozens of genes involved in the immune system.

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“…In COPD, new subtypes and phenotypes have been discovered through ML approaches [ 72 , 73 ]. These distinct patient subtypes characterized by imaging correlate with physiological parameters.…”

Section: Promise Of Quantitative Chest Ct In Phmentioning

confidence: 99%

Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review

et al. 2021

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Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.

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Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study

Cited by 16 publications

References 12 publications

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation

Unpaired Data Empowers Association Tests

Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review

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