title: Parenchymal Airspace Profiling: Sensitive Quantification and Characterization of Lung Structure Evaluating Parenchymal Destruction.
abstract: Lung morphometry was introduced over 50 years ago to provide quantitative evaluation of the lung structure. The existing parameters, such as mean linear intercept and destructive index, suffer from simplistic data interpretation and a subjective data acquisition process. To overcome these existing shortcomings, parenchymal airspace profiling (PAP) was developed to provide a more detailed and unbiased quantitative method. Following the standard protocols of fixation, embedding, and sectioning, lung micrographs were: (1) marked with nonparenchymal area, preprocessed, and binarized under the researcher's supervision; (2) analyzed with a statistical learning method, Gaussian mixture model, to provide an unbiased categorization of parenchymal airspace compartments, corresponding to a single alveolus, alveolar sac, and ductal/destructive airspace; and (3) further quantified into morphometric parameters, including reference volume, alveolar count, and ductal/destructive fraction (DF) based on stereological principles. PAP was performed on hematoxylin and eosin-stained lung sections from mice and rabbits. Unbiased categorization revealed differences in alveolar size among several mouse strains (NZW/LacJ<AKR/J<A/J<C57BL/6J) and across species (mouse<rabbit). Further quantification indicates that parenchymal destruction, modeled in mouse lungs with 1-month smoke exposure, resulted in decreased alveolar count, increased DF, but no significant differences in mean linear intercept. DF also provides a robust measurement that is not biased by processing artifacts, magnification, or reference volume, which are common limitations in human lung biopsies or data obtained from different laboratories. PAP is a novel approach to lung morphometry that offers more detailed characterization of the lung structure, sensitivity, and robustness than presently used methods for evaluating parenchymal destruction.
authors.affiliation: Department of Anesthesiology, College of Physicians and Surgeons, Columbia University, New York, New York.
authors.forename: Rui
authors.identifier: 0000-0001-8679-9551
authors.initials: R
authors.lastname: Xiao
authors.affiliation: Department of Anesthesiology, College of Physicians and Surgeons, Columbia University, New York, New York.
authors.forename: Monica P
authors.initials: MP
authors.lastname: Goldklang
authors.affiliation: Department of Anesthesiology, College of Physicians and Surgeons, Columbia University, New York, New York.
authors.forename: Jeanine M
authors.initials: JM
authors.lastname: D'Armiento
mesh_terms: D000818:Animals; D002452:Cell Count; D008168:Lung / Q000473:pathology*; D008297:Male; D008806:Mice, Inbred AKR; D008810:Mice, Inbred C57BL; D000072356:Parenchymal Tissue / Q000473:pathology*; D011650:Pulmonary Alveoli / Q000473:pathology; D013045:Species Specificity
keywords: computer vision; lung morphometry; parenchymal airspace profiling; statistical learning