Literature DB >> 29535212

Breast cancer histologic grading using digital microscopy: concordance and outcome association.

Emad A Rakha1, Mohamed Aleskandarani1, Michael S Toss1, Andrew R Green1, Graham Ball2, Ian O Ellis1, Leslie W Dalton3.   

Abstract

AIMS: Virtual microscopy utilising digital whole slide imaging (WSI) is increasingly used in breast pathology. Histologic grade is one of the strongest prognostic factors in breast cancer (BC). This study aims at investigating the agreement between BC grading using traditional light microscopy (LM) and digital WSI with consideration of reproducibility and impact on outcome prediction.
METHODS: A large (n=1675) well-characterised cohort of BC originally graded by LM was re-graded using WSI. Two separate virtual-based grading sessions (V1 and V2) were performed with a 3-month washout period. Outcome was assessed using BC-specific and distant metastasis-free survival.
RESULTS: The concordance between LM grading and WSI was strong (LM/WSI Cramer's V: V1=0.576, and V2=0.579). The agreement regarding grade components was as follows: tubule formation=0.538, pleomorphism=0.422 and mitosis=0.514. Greatest discordance was observed between adjacent grades, whereas high/low grade discordance was uncommon (1.5%). The intraobserver agreement for the two WSI sessions was substantial for grade (V1/V2 Cramer's V=0.676; kappa=0.648) and grade components (Cramer's V T=0.628, p=0.573 and M=0.580). Grading using both platforms showed strong association with outcome (all p values <0.001). Although mitotic scores assessed using both platforms were strongly associated with outcome, WSI tends to underestimate mitotic counts.
CONCLUSIONS: Virtual microscopy is a reliable and reproducible method for assessing BC histologic grade. Regardless of the observer or assessment platform, histologic grade is a significant predictor of outcome. Continuing advances in imaging technology could potentially provide improved performance of WSI BC grading and in particular mitotic count assessment. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  agreement; breast pathology; grade; virtual microscopy

Mesh:

Year:  2018        PMID: 29535212     DOI: 10.1136/jclinpath-2017-204979

Source DB:  PubMed          Journal:  J Clin Pathol        ISSN: 0021-9746            Impact factor:   3.411


  6 in total

1.  BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images.

Authors:  Jin Huang; Liye Mei; Mengping Long; Yiqiang Liu; Wei Sun; Xiaoxiao Li; Hui Shen; Fuling Zhou; Xiaolan Ruan; Du Wang; Shu Wang; Taobo Hu; Cheng Lei
Journal:  Bioengineering (Basel)       Date:  2022-06-20

2.  Digital validation of breast biomarkers (ER, PR, AR, and HER2) in cytology specimens using three different scanners.

Authors:  Abeer M Salama; Matthew G Hanna; Dilip Giri; Brie Kezlarian; Marc-Henri Jean; Oscar Lin; Christina Vallejo; Edi Brogi; Marcia Edelweiss
Journal:  Mod Pathol       Date:  2021-09-13       Impact factor: 7.842

3.  A Pyramid Architecture-Based Deep Learning Framework for Breast Cancer Detection.

Authors:  Dong Sui; Weifeng Liu; Jing Chen; Chunxiao Zhao; Xiaoxuan Ma; Maozu Guo; Zhaofeng Tian
Journal:  Biomed Res Int       Date:  2021-10-01       Impact factor: 3.411

4.  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

5.  Defining the area of mitoses counting in invasive breast cancer using whole slide image.

Authors:  Asmaa Ibrahim; Ayat G Lashen; Ayaka Katayama; Raluca Mihai; Graham Ball; Michael S Toss; Emad A Rakha
Journal:  Mod Pathol       Date:  2021-12-11       Impact factor: 8.209

6.  Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis.

Authors:  Ayesha S Azam; Islam M Miligy; Peter K-U Kimani; Heeba Maqbool; Katherine Hewitt; Nasir M Rajpoot; David R J Snead
Journal:  J Clin Pathol       Date:  2020-09-15       Impact factor: 3.411

  6 in total

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