Evaluation of an Automated Tissue Sectioning Machine for Digital Pathology

  • Xiujun Fu Memorial Sloan Kettering Cancer Center
  • Veronica Klepeis Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston
  • Yukako Yagi Department of Pathology, Memorial Sloan Kettering Cancer Center, New York

Abstract

Background: Automation and digital pathology are the trends for future anatomic pathology with the increasing workload in histology laboratories. While tissue process, embedding, staining and coverslipping, and digitizing have been available for automated use, tissue sectioning appears to be the biggest roadblock to a fully automated histology process. In this study we were aimed to investigate a tissue automated sectioning machine for both clinical and research use.


Methods: Totally 77 surgical resection blocks of various organs embedded with clinical standard paraffin were sectioned automatically using AS-410 (Dainippon Seiki Co. LTD., Japan) at 5 μm thickness with the default setting (Setting A). 10 slides per block were sectioned and the last 5 slides were stained with H&E. All stained slides were digitized with whole-slide imaging scanner, and then evaluated by the image scientist and the pathologist. The image scientist scored the images base on the extent of imperfection (Evaluation I), while the pathologist scored the images based on the clinical diagnosis purpose (Evaluation II). Both scoring systems were scored from 1 to 5, with 1 the worst quality and 5 the highest quality. Tissues with unsatisfied score were sectioned with modified setting (Setting B), and evaluated again by the same image scientist and pathologist using the same scoring systems. And the scores from the two different settings were compared. Auto-trimming and barcode reading and printing of AS-410 were also evaluated.


Results: The AS-410 provided auto-trimming function to detect exposed tissue for cutting, accomplished by the installed camera and calculation software. It read sample information and printed barcode as well as input text and automatically generated slide order information. It produced good quality of sections for most cases with median score more than 4 in both Evaluation I and Evaluation II using setting A. The scores of the unsatisfied blocks sectioned with setting A improved significantly when those blocks were sectioned with setting B.


Conclusion: The AS-410 tissue sectioning machine produces high-quality sections with clinical standard paraffin tissue blocks of a variety of organs with proper settings. It promises high automation with sound sectioning quality in the era of digital pathology for both clinical and research use.   



Lung



Breast



Prostate

References

[1] Azorides R. Morales, Mehdi Nassiri. Automation of the Histology Laboratory. Lab medicine. 2007, 38(7): 406-10.
[2] Onozato ML, Hammond S, Merren M, Yagi Y. Evaluation of a Completely Automated Tissue-Sectioning Machine for Paraffin Blocks. Stud Health Technol Inform. 2012, 179: 233-8.
[3] Roberts N, Magee D, Song Y, Brabazon K, Shires M, Crellin D, Orsi NM, Quirke R, Quirke P, Treanor D. Toward routine use of 3D histopathology as a research tool. Am J Pathol. 2012, 180(5): 1835-42.
[4] Wang J, Xu R, Luo G, Wu J. Three-dimensional reconstruction of light microscopy image sections: present and future. Front Med. 2015, 9(1): 30-45.
[5] Onozato ML, Kovach AE, Yeap BY, Morales-Oyarvide V, Klepeis VE, Tammireddy S, Heist RS, Mark EJ, Dias-Santagata D, Iafrate AJ, Yagi Y, Mino-Kenudson M. Tumor Islands in Resected Early Stage Lung Adenocarcinomas Are Associated with Unique Clinicopathological and Molecular Characteristics and Worse Prognosis. Am J Surg Pathol. 2013, 37(2): 287-294.
[6] Hanna MG, Pantanowitz L. Bar Coding and Tracking in Pathology. Clin Lab Med. 2016, 36(1): 13-30.
Published
2018-11-25
How to Cite
FU, Xiujun; KLEPEIS, Veronica; YAGI, Yukako. Evaluation of an Automated Tissue Sectioning Machine for Digital Pathology. Diagnostic Pathology, [S.l.], v. 4, n. 1, nov. 2018. ISSN 2364-4893. Available at: <http://www.diagnosticpathology.eu/content/index.php/dpath/article/view/267>. Date accessed: 12 dec. 2018. doi: https://doi.org/10.17629/www.diagnosticpathology.eu-2018-4:267.
Section
Short Report

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