Machine Learning Algorithms to Differentiate Among Pulmonary Complications After Hematopoietic Cell Transplant

Chest. 2020 Sep;158(3):1090-1103. doi: 10.1016/j.chest.2020.02.076. Epub 2020 Apr 25.

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.

Keywords: bone marrow; bronchiolitis obliterans; graft vs host; medical informatics; organizing pneumonia; radiology—thoracic.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Bronchiolitis Obliterans*
  • Hematopoietic Stem Cell Transplantation*
  • Humans
  • Lung
  • Machine Learning