A role of three-dimensional (3D) reconstruction in the classification of lung adenocarcinoma

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. Conclusion: 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.