Quantitative Lung Morphology: Semi-Automated Measurement of Mean Linear Intercept

Details

• Personal Author:
  Batra P ; Caraher EJ ; Crowley G ; Haider SH ; Kwon S ; Lam R ; Liu M ; Melles D ; Nolan A
• Description:
  Background: Quantifying morphologic changes is critical to our understanding of the pathophysiology of the lung. Mean linear intercept (MLI) measures are important in the assessment of clinically relevant pathology, such as emphysema. However, qualitative measures are prone to error and bias, while quantitative methods such as mean linear intercept (MLI) are manually time consuming. Furthermore, a fully automated, reliable method of assessment is nontrivial and resource-intensive. Methods: We propose a semi-automated method to quantify MLI that does not require specialized computer knowledge and uses a free, open-source image-processor (Fiji). We tested the method with a computer-generated, idealized dataset, derived an MLI usage guide, and successfully applied this method to a murine model of particulate matter (PM) exposure. Fields of randomly placed, uniform-radius circles were analyzed. Optimal numbers of chords to assess based on MLI were found via receiver-operator-characteristic (ROC)-area under the curve (AUC) analysis. Intraclass correlation coefficient (ICC) measured reliability. Results: We demonstrate high accuracy [AUC(ROC) > 0.8 for MLI(actual) > 63.83 pixels] and excellent reliability (ICC = 0.9998, p < 0.0001). We provide a guide to optimize the number of chords to sample based on MLI. Processing time was 0.03 s/image. We showed elevated MLI in PM-exposed mice compared to PBS-exposed controls. We have also provided the macros that were used and have made an ImageJ plugin available free for academic research use at https://med.nyu.edu/nolanlab. Conclusions: Our semi-automated method is reliable, equally fast as fully automated methods, and uses free, open-source software. Additionally, we quantified the optimal number of chords that should be measured per lung field. [Description provided by NIOSH]
• Subjects:
  Automation Laboratories Lung Lung Diseases Occupational Health Particulate Matter Pathology Respiratory System Respiratory Tract Diseases Safety September 11 Terrorist Attacks

  More +
• Keywords:
  Airway Obstruction Analytical Instruments Analytical Methods Author Keywords: Emphysema Exposure Assessment Imaging Techniques Laboratory Animals Laboratory Techniques Lung Architecture Lung Disease Morphology Obstructive Airways Disease Particulates Pathomorphology Performance Capability Performance Tests Physical Properties Pulmonary System Quantitative Analysis World Trade Center WTC

  More +
• ISSN:
  1471-2466
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  California ; New York ; OSHA Region 2 ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  19
• NIOSHTIC Number:
  nn:20057744
• Citation:
  BMC Pulm Med 2019 Nov; 19:206
• Contact Point Address:
  Anna Nolan, Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, NY, USA
• Email:
  Anna.Nolan@med.nyu.edu
• Federal Fiscal Year:
  2020
• Performing Organization:
  New York University School of Medicine
• Peer Reviewed:
  True
• Start Date:
  20170701
• Source Full Name:
  BMC Pulmonary Medicine
• End Date:
  20260630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:f99a1e20074164b03572a640a174e6015da7df5c7494437b32e413310e2aa082cc0cfb9d8bbbcdb32adc5258e600f05ec9bafd98ee8c505f817f0981615c3b3e
• Download URL:
  https://stacks.cdc.gov/view/cdc/214982/cdc_214982_DS1.pdf
• File Type:
  [PDF - 2.07 MB ]