Literature DB >> 29402206

Validation of Digital Microscopy Compared With Light Microscopy for the Diagnosis of Canine Cutaneous Tumors.

Christof A Bertram1, Corinne Gurtner1,2, Martina Dettwiler2, Olivia Kershaw1, Kristina Dietert1, Laura Pieper3, Hannah Pischon1, Achim D Gruber1, Robert Klopfleisch1.   

Abstract

Integration of new technologies, such as digital microscopy, into a highly standardized laboratory routine requires the validation of its performance in terms of reliability, specificity, and sensitivity. However, a validation study of digital microscopy is currently lacking in veterinary pathology. The aim of the current study was to validate the usability of digital microscopy in terms of diagnostic accuracy, speed, and confidence for diagnosing and differentiating common canine cutaneous tumor types and to compare it to classical light microscopy. Therefore, 80 histologic sections including 17 different skin tumor types were examined twice as glass slides and twice as digital whole-slide images by 6 pathologists with different levels of experience at 4 time points. Comparison of both methods found digital microscopy to be noninferior for differentiating individual tumor types within the category epithelial and mesenchymal tumors, but diagnostic concordance was slightly lower for differentiating individual round cell tumor types by digital microscopy. In addition, digital microscopy was associated with significantly shorter diagnostic time, but diagnostic confidence was lower and technical quality was considered inferior for whole-slide images compared with glass slides. Of note, diagnostic performance for whole-slide images scanned at 200× magnification was noninferior in diagnostic performance for slides scanned at 400×. In conclusion, digital microscopy differs only minimally from light microscopy in few aspects of diagnostic performance and overall appears adequate for the diagnosis of individual canine cutaneous tumors with minor limitations for differentiating individual round cell tumor types and grading of mast cell tumors.

Entities:  

Keywords:  diagnostic concordance; digital pathology; laboratory proficiency testing; scanning magnification; technical quality; validation study; virtual microscopy; whole-slide images

Mesh:

Year:  2018        PMID: 29402206     DOI: 10.1177/0300985818755254

Source DB:  PubMed          Journal:  Vet Pathol        ISSN: 0300-9858            Impact factor:   2.221


  6 in total

1.  Agreement in Histological Assessment of Mitotic Activity Between Microscopy and Digital Whole Slide Images Informs Conversion for Clinical Diagnosis.

Authors:  Bih-Rong Wei; Charles H Halsey; Shelley B Hoover; Munish Puri; Howard H Yang; Brandon D Gallas; Maxwell P Lee; Weijie Chen; Amy C Durham; Jennifer E Dwyer; Melissa D Sánchez; Ryan P Traslavina; Chad Frank; Charles Bradley; Lawrence D McGill; D Glen Esplin; Paula A Schaffer; Sarah D Cramer; L Tiffany Lyle; Jessica Beck; Elizabeth Buza; Qi Gong; Stephen M Hewitt; R Mark Simpson
Journal:  Acad Pathol       Date:  2019-07-11

2.  Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study.

Authors:  Massimo Salvi; Filippo Molinari; Selina Iussich; Luisa Vera Muscatello; Luca Pazzini; Silvia Benali; Barbara Banco; Francesca Abramo; Raffaella De Maria; Luca Aresu
Journal:  Front Vet Sci       Date:  2021-03-26

Review 3.  Validation of digital microscopy: Review of validation methods and sources of bias.

Authors:  Christof A Bertram; Nikolas Stathonikos; Taryn A Donovan; Alexander Bartel; Andrea Fuchs-Baumgartinger; Karoline Lipnik; Paul J van Diest; Federico Bonsembiante; Robert Klopfleisch
Journal:  Vet Pathol       Date:  2021-08-26       Impact factor: 2.221

4.  Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy.

Authors:  Christof A Bertram; Marc Aubreville; Taryn A Donovan; Alexander Bartel; Frauke Wilm; Christian Marzahl; Charles-Antoine Assenmacher; Kathrin Becker; Mark Bennett; Sarah Corner; Brieuc Cossic; Daniela Denk; Martina Dettwiler; Beatriz Garcia Gonzalez; Corinne Gurtner; Ann-Kathrin Haverkamp; Annabelle Heier; Annika Lehmbecker; Sophie Merz; Erica L Noland; Stephanie Plog; Anja Schmidt; Franziska Sebastian; Dodd G Sledge; Rebecca C Smedley; Marco Tecilla; Tuddow Thaiwong; Andrea Fuchs-Baumgartinger; Donald J Meuten; Katharina Breininger; Matti Kiupel; Andreas Maier; Robert Klopfleisch
Journal:  Vet Pathol       Date:  2021-12-30       Impact factor: 2.221

5.  Evaluation of a Probability-Based Predictive Tool on Pathologist Agreement Using Urinary Bladder as a Pilot Tissue.

Authors:  Emily Jones; Solomon Woldeyohannes; Fernanda Castillo-Alcala; Brandon N Lillie; Mee-Ja M Sula; Helen Owen; John Alawneh; Rachel Allavena
Journal:  Vet Sci       Date:  2022-07-18

6.  Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset.

Authors:  Frauke Wilm; Marco Fragoso; Christian Marzahl; Jingna Qiu; Chloé Puget; Laura Diehl; Christof A Bertram; Robert Klopfleisch; Andreas Maier; Katharina Breininger; Marc Aubreville
Journal:  Sci Data       Date:  2022-09-27       Impact factor: 8.501

  6 in total

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