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.
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 humanlung 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.
Authors: Mirabela Rusu; Prabhakar Rajiah; Robert Gilkeson; Michael Yang; Christopher Donatelli; Rajat Thawani; Frank J Jacono; Philip Linden; Anant Madabhushi Journal: Eur Radiol Date: 2017-04-06 Impact factor: 5.315
Authors: Maristela L Onozato; Alexandra E Kovach; Beow Y Yeap; Vicente Morales-Oyarvide; Veronica E Klepeis; Swathi Tammireddy; Rebecca S Heist; Eugene J Mark; Dora Dias-Santagata; A John Iafrate; Yukako Yagi; Mari Mino-Kenudson Journal: Am J Surg Pathol Date: 2013-02 Impact factor: 6.394