Literature DB >> 28401450

Reproducibility and Prognostic Potential of Ki-67 Proliferation Index when Comparing Digital-Image Analysis with Standard Semi-Quantitative Evaluation in Breast Cancer.

Balázs Ács1, Lilla Madaras1, Kristóf Attila Kovács1, Tamás Micsik2, Anna-Mária Tőkés3, Balázs Győrffy4,5, Janina Kulka1, Attila Marcell Szász6,7.   

Abstract

In this study, the reproducibility of Ki-67 proliferation index (KIPI) was investigated by comparing the semi-quantitative (SQ) results of three assessors with those of digital image-analysis (DIA) methods. The prognostic significance of the two approaches was also correlated with clinical outcome. Tissue microarrays of duplicate 2 mm cores were constructed from representative areas of formalin-fixed and paraffin-embedded tumor blocks of 347 breast cancer patients. SQ evaluation of Ki-67 (MIB1 clone) immunostained slides was performed independently by three pathologists. DIA was completed using a fully automated histological pattern and cell recognition module for KIPI detection (DIA-1) and an adjustable module (DIA-2) with the possibility of manual corrections. To compare SQ and DIA evaluations intra-class correlation (ICC) and concordance correlation coefficients (CCC) were determined. The three SQ evaluations demonstrated a remarkable ICC (0.853). Significant difference and poor concordance occurred between SQ-1 and SQ-2 as well as between SQ-1 and SQ-3 (p ≤ 0.001, CCC ≤ 0.827 for both comparisons). Thus, the reference KIPI value (SQ-RV) was generated from the mean values of SQ-2 and SQ-3. SQ-RV and DIA-2 results showed substantial concordance (CCC = 0.963, at p = 0.754), while SQ-RV and DIA-1 values differed (p ≤ 0.001) at only moderate concordance (CCC = 0.906). In multivariate analysis, lymph node status and SQ-2 assessment were significantly associated with clinical outcome (p ≤ 0.012 for both comparisons). Our results confirm that KIPI is a significant prognostic marker in breast cancer, which can be can be reliably reproduced by using an adjustable DIA-2 image analysis module.

Entities:  

Keywords:  Breast cancer; Concordance correlation; Digital image analysis; Intra-class correlation; Ki-67 proliferation index; Prognosis

Mesh:

Substances:

Year:  2017        PMID: 28401450     DOI: 10.1007/s12253-017-0220-8

Source DB:  PubMed          Journal:  Pathol Oncol Res        ISSN: 1219-4956            Impact factor:   3.201


  26 in total

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Authors:  Rikke Riber-Hansen; Ben Vainer; Torben Steiniche
Journal:  APMIS       Date:  2011-12-19       Impact factor: 3.205

2.  Tailoring therapies--improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015.

Authors:  A S Coates; E P Winer; A Goldhirsch; R D Gelber; M Gnant; M Piccart-Gebhart; B Thürlimann; H-J Senn
Journal:  Ann Oncol       Date:  2015-05-04       Impact factor: 32.976

3.  Production of a mouse monoclonal antibody reactive with a human nuclear antigen associated with cell proliferation.

Authors:  J Gerdes; U Schwab; H Lemke; H Stein
Journal:  Int J Cancer       Date:  1983-01-15       Impact factor: 7.396

Review 4.  Ki67 in breast cancer: prognostic and predictive potential.

Authors:  Rinat Yerushalmi; Ryan Woods; Peter M Ravdin; Malcolm M Hayes; Karen A Gelmon
Journal:  Lancet Oncol       Date:  2010-02       Impact factor: 41.316

5.  Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group.

Authors:  Mitch Dowsett; Torsten O Nielsen; Roger A'Hern; John Bartlett; R Charles Coombes; Jack Cuzick; Matthew Ellis; N Lynn Henry; Judith C Hugh; Tracy Lively; Lisa McShane; Soon Paik; Frederique Penault-Llorca; Ljudmila Prudkin; Meredith Regan; Janine Salter; Christos Sotiriou; Ian E Smith; Giuseppe Viale; Jo Anne Zujewski; Daniel F Hayes
Journal:  J Natl Cancer Inst       Date:  2011-09-29       Impact factor: 13.506

Review 6.  Hallmarks of cancer: the next generation.

Authors:  Douglas Hanahan; Robert A Weinberg
Journal:  Cell       Date:  2011-03-04       Impact factor: 41.582

7.  How to measure diagnosis-associated information in virtual slides.

Authors:  Klaus Kayser; Jürgen Görtler; Stephan Borkenfeld; Gian Kayser
Journal:  Diagn Pathol       Date:  2011-03-30       Impact factor: 2.644

Review 8.  Ki-67: level of evidence and methodological considerations for its role in the clinical management of breast cancer: analytical and critical review.

Authors:  Elisabeth Luporsi; Fabrice André; Frédérique Spyratos; Pierre-Marie Martin; Jocelyne Jacquemier; Frédérique Penault-Llorca; Nicole Tubiana-Mathieu; Brigitte Sigal-Zafrani; Laurent Arnould; Anne Gompel; Caroline Egele; Bruno Poulet; Krishna B Clough; Hubert Crouet; Alain Fourquet; Jean-Pierre Lefranc; Carole Mathelin; Nicolas Rouyer; Daniel Serin; Marc Spielmann; Margaret Haugh; Marie-Pierre Chenard; Etienne Brain; Patricia de Cremoux; Jean-Pierre Bellocq
Journal:  Breast Cancer Res Treat       Date:  2011-11-03       Impact factor: 4.872

9.  Standardization for Ki-67 assessment in moderately differentiated breast cancer. A retrospective analysis of the SAKK 28/12 study.

Authors:  Zsuzsanna Varga; Estelle Cassoly; Qiyu Li; Christian Oehlschlegel; Coya Tapia; Hans Anton Lehr; Dirk Klingbiel; Beat Thürlimann; Thomas Ruhstaller
Journal:  PLoS One       Date:  2015-04-17       Impact factor: 3.240

10.  Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013.

Authors:  A Goldhirsch; E P Winer; A S Coates; R D Gelber; M Piccart-Gebhart; B Thürlimann; H-J Senn
Journal:  Ann Oncol       Date:  2013-08-04       Impact factor: 32.976

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Journal:  Breast       Date:  2020-07-13       Impact factor: 4.380

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4.  Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study.

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Journal:  Mod Pathol       Date:  2022-06-21       Impact factor: 8.209

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