Literature DB >> 30143750

An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer.

David L Rimm1, Samuel C Y Leung2, Lisa M McShane3, Yalai Bai4, Anita L Bane5, John M S Bartlett6,7, Jane Bayani6, Martin C Chang8, Michelle Dean9, Carsten Denkert10, Emeka K Enwere9, Chad Galderisi11, Abhi Gholap12, Judith C Hugh13, Anagha Jadhav12, Elizabeth N Kornaga9, Arvydas Laurinavicius14, Richard Levenson15, Joema Lima6, Keith Miller16, Liron Pantanowitz17, Tammy Piper7, Jason Ruan15, Malini Srinivasan17, Shakeel Virk18, Ying Wu5, Hua Yang13, Daniel F Hayes19, Torsten O Nielsen2, Mitch Dowsett20.   

Abstract

The nuclear proliferation biomarker Ki67 has potential prognostic, predictive, and monitoring roles in breast cancer. Unacceptable between-laboratory variability has limited its clinical value. The International Ki67 in Breast Cancer Working Group investigated whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories using automated machine-based scoring. Sets of pre-stained core-cut biopsy sections of 30 breast tumors were circulated to 14 laboratories for scanning and automated assessment of the average and maximum percentage of tumor cells positive for Ki67. Seven unique scanners and 10 software platforms were involved in this study. Pre-specified analyses included evaluation of reproducibility between all laboratories (primary) as well as among those using scanners from a single vendor (secondary). The primary reproducibility metric was intraclass correlation coefficient between laboratories, with success considered to be intraclass correlation coefficient >0.80. Intraclass correlation coefficient for automated average scores across 16 operators was 0.83 (95% credible interval: 0.73-0.91) and intraclass correlation coefficient for maximum scores across 10 operators was 0.63 (95% credible interval: 0.44-0.80). For the laboratories using scanners from a single vendor (8 score sets), intraclass correlation coefficient for average automated scores was 0.89 (95% credible interval: 0.81-0.96), which was similar to the intraclass correlation coefficient of 0.87 (95% credible interval: 0.81-0.93) achieved using these same slides in a prior visual-reading reproducibility study. Automated machine assessment of average Ki67 has the potential to achieve between-laboratory reproducibility similar to that for a rigorously standardized pathologist-based visual assessment of Ki67. The observed intraclass correlation coefficient was worse for maximum compared to average scoring methods, suggesting that maximum score methods may be suboptimal for consistent measurement of proliferation. Automated average scoring methods show promise for assessment of Ki67 scoring, but requires further standardization and subsequent clinical validation.

Entities:  

Year:  2018        PMID: 30143750     DOI: 10.1038/s41379-018-0109-4

Source DB:  PubMed          Journal:  Mod Pathol        ISSN: 0893-3952            Impact factor:   7.842


  19 in total

1.  Independent Prognostic Value of Intratumoral Heterogeneity and Immune Response Features by Automated Digital Immunohistochemistry Analysis in Early Hormone Receptor-Positive Breast Carcinoma.

Authors:  Dovile Zilenaite; Allan Rasmusson; Renaldas Augulis; Justinas Besusparis; Aida Laurinaviciene; Benoit Plancoulaine; Valerijus Ostapenko; Arvydas Laurinavicius
Journal:  Front Oncol       Date:  2020-06-16       Impact factor: 6.244

2.  Prognostic potential of automated Ki67 evaluation in breast cancer: different hot spot definitions versus true global score.

Authors:  Stephanie Robertson; Balazs Acs; Michael Lippert; Johan Hartman
Journal:  Breast Cancer Res Treat       Date:  2020-06-22       Impact factor: 4.872

3.  The use of digital pathology and image analysis in clinical trials.

Authors:  Robert Pell; Karin Oien; Max Robinson; Helen Pitman; Nasir Rajpoot; Jens Rittscher; David Snead; Clare Verrill
Journal:  J Pathol Clin Res       Date:  2019-03-25

4.  Evaluating ZNF217 mRNA Expression Levels as a Predictor of Response to Endocrine Therapy in ER+ Breast Cancer.

Authors:  Julie A Vendrell; Jérôme Solassol; Balázs Győrffy; Paul Vilquin; Marta Jarlier; Caterina F Donini; Laurent Gamba; Thierry Maudelonde; Philippe Rouanet; Pascale A Cohen
Journal:  Front Pharmacol       Date:  2019-01-25       Impact factor: 5.810

5.  Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features.

Authors:  Alberto Stefano Tagliafico; Bianca Bignotti; Federica Rossi; Joao Matos; Massimo Calabrese; Francesca Valdora; Nehmat Houssami
Journal:  Eur Radiol Exp       Date:  2019-08-14

6.  Ki67 Assessment in Breast Cancer: Are We There Yet?

Authors:  Jorge S Reis-Filho; Nancy E Davidson
Journal:  J Natl Cancer Inst       Date:  2021-07-01       Impact factor: 13.506

Review 7.  Challenging, Accurate and Feasible: CAF-1 as a Tumour Proliferation Marker of Diagnostic and Prognostic Value.

Authors:  Alexandros G Sykaras; Alexandros Pergaris; Stamatios Theocharis
Journal:  Cancers (Basel)       Date:  2021-05-24       Impact factor: 6.639

Review 8.  Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy.

Authors:  Ella F Jones; Deep K Hathi; Rita Freimanis; Rita A Mukhtar; A Jo Chien; Laura J Esserman; Laura J Van't Veer; Bonnie N Joe; Nola M Hylton
Journal:  Cancers (Basel)       Date:  2020-06-09       Impact factor: 6.575

9.  Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling.

Authors:  Sanguo Zhang; Yu Fan; Tingyan Zhong; Shuangge Ma
Journal:  Sci Rep       Date:  2020-09-14       Impact factor: 4.379

10.  Investigations on the Role of the MicroRNA-338-5p/Wnt Family Member 2B (WNT2B) Axis in Regulating the Pathogenesis of Nasopharyngeal Carcinoma (NPC).

Authors:  Suzhen Wang; Tianning Yang; Zhengxiang He
Journal:  Front Oncol       Date:  2021-06-29       Impact factor: 6.244

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