title: Microscopy-based quantitative analysis of lung structure: application in diagnosis.
abstract: BACKGROUND
  Stereology provides a set of methods that are appropriate for a microscopy-based quantitative assessment of lung structure. In general, the aim of stereology is to obtain information on three-dimensional structures from two-dimensional sections of these structures. The inherent impartiality of stereological principles is critical in order to meet the requirements of 'good laboratory practice'.
  
  
  METHODS
  This article is a systematic review of the applications of stereology to characterize pathological alterations of emphysema, fibrosis, acute lung injury and tumor grading. The reader is provided with a general overview of unbiased or design-based stereology and is provided with some examples of how these methods could be integrated into a diagnostic work-up of lung diseases in humans and animal models. The article also reviews the implications of a published statement, which defines standards for quantitative assessment of lung structure based on stereology, by the American Thoracic Society and the European Respiratory Society.
  
  
  CONCLUSIONS
  In view of the recently published standards for quantitative assessment of lung structure, unbiased stereological methods are strongly recommended, particularly as they provide valuable information in diagnosing lung diseases and allow a statistically valid quantitative comparison between different groups. Future developments will make the application of stereology in lung biology and pathology even more efficient. Moreover, there is also the potential for combing the principles of stereology with other imaging modalities (e.g., radiological), which will allow for non-invasive lung stereology.
authors.affiliation: Institute of Functional and Applied Anatomy , Hannover Medical School, Carl-Neuberg Str. 1, 30625 Hannover , Germany knudsen.lars@mh-hannover.de.
authors.forename: Lars
authors.initials: L
authors.lastname: Knudsen
authors.forename: Matthias
authors.initials: M
authors.lastname: Ochs