Literature DB >> 30636543

Toxicologic Pathology Forum*: Opinion on Considerations for the Use of Whole Slide Images in GLP Pathology Peer Review.

Alys Bradley1, Matt Jacobsen2.   

Abstract

Whole slide imaging (WSI) technology has advanced to a point where it has replaced the glass slide as the primary means of pathology evaluation within many areas of medical pathology. The deployment of WSI in the field of toxicologic pathology has been delayed by a lack of clarity around the degree of validation required for its use on Good Laboratory Practice (GLP) studies. The current opinion piece attempts to provide a high-level overview of WSI technology to include basic methodology, advantages and disadvantages over a conventional microscope, validation status of WSI scanners, and perceived concerns over regulatory acceptance for the use of WSI for (GLP) peer review in the field of toxicologic pathology. Observations are based on the extensive use by AstraZeneca of WSI for the peer review of non-GLP studies conducted at Charles River facilities and represent the experiences of the authors. Note: This is an opinion article submitted to the Toxicologic Pathology Forum. It represents the views of the author(s). It does not constitute an official position of the Society of Toxicologic Pathology, British Society of Toxicological Pathology, or European Society of Toxicologic Pathology, and the views expressed might not reflect the best practices recommended by these Societies. This article should not be construed to represent the policies, positions, or opinions of their respective organizations, employers, or regulatory agencies.

Keywords:  digital pathology systems; digital peer review; imaging chain; pathology peer review; regulatory toxicology studies; validation; whole slide imaging

Mesh:

Year:  2019        PMID: 30636543     DOI: 10.1177/0192623318818790

Source DB:  PubMed          Journal:  Toxicol Pathol        ISSN: 0192-6233            Impact factor:   1.902


  1 in total

1.  Classification of Mouse Lung Metastatic Tumor with Deep Learning.

Authors:  Ha Neul Lee; Hong-Deok Seo; Eui-Myoung Kim; Beom Seok Han; Jin Seok Kang
Journal:  Biomol Ther (Seoul)       Date:  2022-03-01       Impact factor: 4.634

  1 in total

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