Literature DB >> 31994224

Digital pathology for primary diagnosis of screen-detected breast lesions - experimental data, validation and experience from four centres.

Bethany Williams1,2, Andrew Hanby1,2, Rebecca Millican-Slater1, Eldo Verghese1,2, Anju Nijhawan1, Imogen Wilson2, Justinas Besusparis3, David Clark4, David Snead5,6, Emad Rakha7,8, Darren Treanor1,2.   

Abstract

AIM: The rate of deployment of digital pathology (DP) systems for primary diagnosis in the UK is accelerating. The flexibility and resilience of digital versus standard glass slides could be of great benefit in the NHS breast screening programme (NHSBSP). This study aims to document the safety and benefits of DP for preoperative tissue diagnosis of screen-detected breast lesions. METHODS AND
RESULTS: Concordance data for glass and digital slides of the same cases from four sites were subjected to detailed concordance-discordance analysis. A literature review of DP in the primary diagnosis of breast lesions is presented, making this the most comprehensive synthesis of digital breast cancer histopathological diagnostic data to date. Detailed concordance analysis of experimental data from two histopathology departments reveals clinical concordance rates for breast biopsies of 96% (216 of 225) and 99.6% (249 of 250). Data from direct comparison validation studies in two histopathology departments, utilising the protocol recommended by the Royal College of Pathologists, found concordance rates for breast histology cases of 99.4% (180 of 181) and 99.0% (887 of 896). An intraobserver variation study for glass versus digital slides for difficult cases from the NHSBSP yielded a kappa statistic of 0.80, indicating excellent agreement. Discordances encountered in the studies most frequently concerned discrepancies in grading attributable to mitotic count-scoring and identification of weddelite.
CONCLUSIONS: The experience of four histopathology laboratories and our review of pre-existing literature suggests that DP is safe for the primary diagnosis of NHSBSP breast histology specimens, and does not increase the risk of misclassification.
© 2020 John Wiley & Sons Ltd.

Entities:  

Keywords:  breast cancer; breast screening; digital pathology; patient safety; validation

Mesh:

Year:  2020        PMID: 31994224     DOI: 10.1111/his.14079

Source DB:  PubMed          Journal:  Histopathology        ISSN: 0309-0167            Impact factor:   5.087


  4 in total

1.  Fully digital pathology laboratory routine and remote reporting of oral and maxillofacial diagnosis during the COVID-19 pandemic: a validation study.

Authors:  Anna Luíza Damaceno Araújo; Gleyson Kleber do Amaral-Silva; Maria Eduarda Pérez-de-Oliveira; Karen Patricia Domínguez Gallagher; Cinthia Veronica Bardalez López de Cáceres; Ana Luiza Oliveira Corrêa Roza; Amanda Almeida Leite; Bruno Augusto Linhares Almeida Mariz; Carla Isabelly Rodrigues-Fernandes; Felipe Paiva Fonseca; Marcio Ajudarte Lopes; Paul M Speight; Syed Ali Khurram; Jacks Jorge Júnior; Manoela Domingues Martins; Oslei Paes de Almeida; Alan Roger Santos-Silva; Pablo Agustin Vargas
Journal:  Virchows Arch       Date:  2021-03-13       Impact factor: 4.064

2.  Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset.

Authors:  H El Agouri; M Azizi; H El Attar; M El Khannoussi; A Ibrahimi; R Kabbaj; H Kadiri; S BekarSabein; S EchCharif; C Mounjid; B El Khannoussi
Journal:  BMC Res Notes       Date:  2022-02-19

Review 3.  Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers.

Authors:  Alex Ngai Nick Wong; Zebang He; Ka Long Leung; Curtis Chun Kit To; Chun Yin Wong; Sze Chuen Cesar Wong; Jung Sun Yoo; Cheong Kin Ronald Chan; Angela Zaneta Chan; Maribel D Lacambra; Martin Ho Yin Yeung
Journal:  Cancers (Basel)       Date:  2022-08-03       Impact factor: 6.575

4.  The International Collaboration for Cancer Classification and Research.

Authors:  Ian A Cree; Blanca Iciar Indave Ruiz; Jiri Zavadil; James McKay; Magali Olivier; Zisis Kozlakidis; Alexander J Lazar; Chris Hyde; Stefan Holdenrieder; Ros Hastings; Nasir Rajpoot; Arnaud de la Fouchardiere; Brian Rous; Jean Claude Zenklusen; Nicola Normanno; Richard L Schilsky
Journal:  Int J Cancer       Date:  2020-10-09       Impact factor: 7.396

  4 in total

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