Literature DB >> 33251977

Impact of Preanalytical Factors During Histology Processing on Section Suitability for Digital Image Analysis.

Elizabeth A Chlipala1, Mark Butters1, Miles Brous1, Jessica S Fortin2, Roni Archuletta1, Karen Copeland3, Brad Bolon4.   

Abstract

Digital image analysis (DIA) is impacted by the quality of tissue staining. This study examined the influence of preanalytical variables-staining protocol design, reagent quality, section attributes, and instrumentation-on the performance of automated DIA software. Our hypotheses were that (1) staining intensity is impacted by subtle differences in protocol design, reagent quality, and section composition and that (2) identically programmed and loaded stainers will produce equivalent immunohistochemical (IHC) staining. We tested these propositions by using 1 hematoxylin and eosin stainer to process 13 formalin-fixed, paraffin-embedded (FFPE) mouse tissues and by using 3 identically programmed and loaded immunostainers to process 5 FFPE mouse tissues for 4 cell biomarkers. Digital images of stained sections acquired with a commercial whole slide scanner were analyzed by customizable algorithms incorporated into commercially available DIA software. Staining intensity as viewed qualitatively by an observer and/or quantitatively by DIA was affected by staining conditions and tissue attributes. Intrarun and inter-run IHC staining intensities were equivalent for each tissue when processed on a given stainer but varied measurably across stainers. Our data indicate that staining quality must be monitored for each method and stainer to ensure that preanalytical factors do not impact digital pathology data quality.

Entities:  

Keywords:  digital pathology; histology process validation; image analysis; immunohistochemistry; preanalytical factors; precision; reproducibility

Year:  2020        PMID: 33251977     DOI: 10.1177/0192623320970534

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


  2 in total

1.  Technical Note: Measuring the thickness of histological sections by detecting fluorescence intensity of embedding foam.

Authors:  David Ibsen Dadash-Khanlou; Benedicte Heegaard; Henrik Holten-Rossing; Thomas Hartvig Lindkær Jensen
Journal:  J Pathol Inform       Date:  2022-08-01

Review 2.  Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives.

Authors:  Shima Mehrvar; Lauren E Himmel; Pradeep Babburi; Andrew L Goldberg; Magali Guffroy; Kyathanahalli Janardhan; Amanda L Krempley; Bhupinder Bawa
Journal:  J Pathol Inform       Date:  2021-11-01
  2 in total

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