title: A role of three-dimensional (3D)-reconstruction in the classification of lung adenocarcinoma.
abstract: BACKGROUND
  Three-dimensional (3D)-reconstruction from paraffin embedded sections has been considered laborious and time-consuming. However, the high-resolution images of large object areas and different fields of view obtained by 3D-reconstruction make one wonder whether it can add a new insight into lung adenocarcinoma, the most frequent histology type of lung cancer characterized by its morphological heterogeneity.
  
  
  OBJECTIVE
  In this work, we tested whether an automated tissue sectioning machine and slide scanning system could generate precise 3D-reconstruction of microanatomy of the lung and help us better understand and define histologic subtypes of lung adenocarcinoma.
  
  
  METHODS
  Four formalin-fixed human lung adenocarcinoma resections were studied. Paraffin embedded tissues were sectioned with Kurabo-Automated tissue sectioning machine and serial sections were automatically stained and scanned with a Whole Slide Imaging system. The resulting stacks of images were 3D reconstructed by Pannoramic Viewer software.
  
  
  RESULTS
  Two of the four specimens contained islands of tumor cells detached in alveolar spaces that had not been described in any of the existing adenocarcinoma classifications. 3D-reconstruction revealed the details of spatial distribution and structural interaction of the tumor that could hardly be observed by 2D light microscopy studies. The islands of tumor cells extended into a deeper aspect of the tissue, and were interconnected with each other and with the main tumor with a solid pattern that was surrounded by the islands. The finding raises the question whether the islands of tumor cells should be classified into a solid pattern in the current classification.
  
  
  CONCLUSIONS
  The combination of new technologies enabled us to build an effective 3D-reconstruction of resected lung adenocarcinomas. 3D-reconstruction may help us refine the classification of lung adenocarcinoma by adding detailed spatial/structural information to 2D light microscopy evaluation.
authors.affiliation: Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA. likaono@gmail.com
authors.forename: Maristela L
authors.initials: ML
authors.lastname: Onozato
authors.forename: Veronica E
authors.initials: VE
authors.lastname: Klepeis
authors.forename: Yukako
authors.initials: Y
authors.lastname: Yagi
authors.forename: Mari
authors.initials: M
authors.lastname: Mino-Kenudson
mesh_terms: D000230:Adenocarcinoma / Q000145:classification* / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008175:Lung Neoplasms / Q000145:classification* / Q000473:pathology