Digital Cytopathology Quality Guidelines

Digital cytopathology is increasingly used in veterinary medicine, yet standardized quality guidance for its safe and effective implementation has been lacking. As more reference laboratories and individual clinics adopt this technology, challenges related to sample preparation, staining variability, scanner capabilities, software design, operator training, and limitations in optical resolution have emerged. To address these issues, the ASVCP Quality Assurance and Laboratory Standards Committee developed comprehensive guidelines for the preanalytical, analytical, and postanalytical phases of digital cytologic evaluation, with the aim of enhancing diagnostic accuracy, improving workflow efficiency, and supporting safer clinical decision-making. The target audience is veterinary pathologists, residents, laboratory personnel, and practices currently using or planning to adopt digital cytopathology. Key recommendations include ensuring appropriate sample preparation and placement to support accurate digital capture; selecting and validating scanners and software with performance characteristics appropriate for cytologic specimens; implementing standardized training for operators and pathologists; and establishing procedures for assessing digital image quality, managing incomplete or low-quality scans, and determining when glass slide review is required. Because digital cytopathology continues to evolve rapidly and the current evidence base remains limited, these guidelines are intended as a minimum standard and should be revisited within five years to incorporate emerging QA data and technological advancements.

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Publication Details

Journal
Veterinary Clinical Pathology
Published
2025-12-01
DOI
https://doi.org/10.1111/vcp.70094
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
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article

Digital Cytopathology Quality Guidelines

Julie Piccione, Jennifer D. Steinberg, Tamara S. Hancock, Jennifer R. Matlow et al.
Veterinary Clinical Pathology
AI in cancer detection
article

Digital Cytopathology Quality Guidelines

Julie Piccione, Jennifer D. Steinberg, Tamara S. Hancock, Jennifer R. Matlow, Kathleen P. Freeman, Jérémie Korchia, Francesco Cian, Eric J. Fish, Kendal E. Harr, Melanie A. Ammersbach, Natalie Hoepp, A. Russell Moore
article en

Abstract

Digital cytopathology is increasingly used in veterinary medicine, yet standardized quality guidance for its safe and effective implementation has been lacking. As more reference laboratories and individual clinics adopt this technology, challenges related to sample preparation, staining variability, scanner capabilities, software design, operator training, and limitations in optical resolution have emerged. To address these issues, the ASVCP Quality Assurance and Laboratory Standards Committee developed comprehensive guidelines for the preanalytical, analytical, and postanalytical phases of digital cytologic evaluation, with the aim of enhancing diagnostic accuracy, improving workflow efficiency, and supporting safer clinical decision-making. The target audience is veterinary pathologists, residents, laboratory personnel, and practices currently using or planning to adopt digital cytopathology. Key recommendations include ensuring appropriate sample preparation and placement to support accurate digital capture; selecting and validating scanners and software with performance characteristics appropriate for cytologic specimens; implementing standardized training for operators and pathologists; and establishing procedures for assessing digital image quality, managing incomplete or low-quality scans, and determining when glass slide review is required. Because digital cytopathology continues to evolve rapidly and the current evidence base remains limited, these guidelines are intended as a minimum standard and should be revisited within five years to incorporate emerging QA data and technological advancements.

Veterinary Clinical PathologyVol. 54(4)
Merck & Co., Inc., Rahway, NJ, USA (United States) (US), Pfizer (United States) (US), Missouri College (US), University of Warwick (GB), IDEXX Laboratories (United States) (US), Wellborn Road Veterinary Medical Center (US), University of Washington Bothell (US), ABS Consulting (United States) (US), Lantiq (Germany) (DE), University of California, Davis (US), Michigan State University (US), Colorado State University (US)
Openalex Percentile: Top 17%
AI in cancer detection
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