Husham Sharifi1, Yu Kuang Lai1, Henry Guo2, Mita Hoppenfeld3, Zachary D Guenther2, Laura Johnston4, Theresa Brondstetter1, Laveena Chhatwani1, Mark R Nicolls1, Joe L Hsu5. 1. Department of Medicine, the Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA. 2. Departments of Radiology, Stanford University School of Medicine, Stanford, CA. 3. Departments of Medicine, Stanford University School of Medicine, Stanford, CA. 4. Department of Medicine, Division of Blood and Marrow Transplantation, Stanford University School of Medicine, Stanford, CA. 5. Department of Medicine, the Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA. Electronic address: joehsu@stanford.edu.
Abstract
BACKGROUND: Pulmonary complications, including infections, are highly prevalent in patients after hematopoietic cell transplantation with chronic graft-vs-host disease. These comorbid diseases can make the diagnosis of early lung graft-vs-host disease (bronchiolitis obliterans syndrome) challenging. A quantitative method to differentiate among these pulmonary diseases can address diagnostic challenges and facilitate earlier and more targeted therapy. STUDY DESIGN AND METHODS: We conducted a single-center study of 66 patients with CT chest scans analyzed with a quantitative imaging tool known as parametric response mapping. Parametric response mapping results were correlated with pulmonary function tests and clinical characteristics. Five parametric response mapping metrics were applied to K-means clustering and support vector machine models to distinguish among posttransplantation lung complications solely from quantitative output. RESULTS: Compared with parametric response mapping, spirometry showed a moderate correlation with radiographic air trapping, and total lung capacity and residual volume showed a strong correlation with radiographic lung volumes. K-means clustering analysis distinguished four unique clusters. Clusters 2 and 3 represented obstructive physiology (encompassing 81% of patients with bronchiolitis obliterans syndrome) in increasing severity (percentage air trapping 15.6% and 43.0%, respectively). Cluster 1 was dominated by normal lung, and cluster 4 was characterized by patients with parenchymal opacities. A support vector machine algorithm differentiated bronchiolitis obliterans syndrome with a specificity of 88%, sensitivity of 83%, accuracy of 86%, and an area under the receiver operating characteristic curve of 0.85. INTERPRETATION: Our machine learning models offer a quantitative approach for the identification of bronchiolitis obliterans syndrome vs other lung diseases, including late pulmonary complications after hematopoietic cell transplantation.
BACKGROUND: Pulmonary complications, including infections, are highly prevalent in patients after hematopoietic cell transplantation with chronic graft-vs-host disease. These comorbid diseases can make the diagnosis of early lung graft-vs-host disease (bronchiolitis obliterans syndrome) challenging. A quantitative method to differentiate among these pulmonary diseases can address diagnostic challenges and facilitate earlier and more targeted therapy. STUDY DESIGN AND METHODS: We conducted a single-center study of 66 patients with CT chest scans analyzed with a quantitative imaging tool known as parametric response mapping. Parametric response mapping results were correlated with pulmonary function tests and clinical characteristics. Five parametric response mapping metrics were applied to K-means clustering and support vector machine models to distinguish among posttransplantation lung complications solely from quantitative output. RESULTS: Compared with parametric response mapping, spirometry showed a moderate correlation with radiographic air trapping, and total lung capacity and residual volume showed a strong correlation with radiographic lung volumes. K-means clustering analysis distinguished four unique clusters. Clusters 2 and 3 represented obstructive physiology (encompassing 81% of patients with bronchiolitis obliterans syndrome) in increasing severity (percentage air trapping 15.6% and 43.0%, respectively). Cluster 1 was dominated by normal lung, and cluster 4 was characterized by patients with parenchymal opacities. A support vector machine algorithm differentiated bronchiolitis obliterans syndrome with a specificity of 88%, sensitivity of 83%, accuracy of 86%, and an area under the receiver operating characteristic curve of 0.85. INTERPRETATION: Our machine learning models offer a quantitative approach for the identification of bronchiolitis obliterans syndrome vs other lung diseases, including late pulmonary complications after hematopoietic cell transplantation.
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