Literature DB >> 29220095

Digital image analysis of Ki67 in hot spots is superior to both manual Ki67 and mitotic counts in breast cancer.

Gustav Stålhammar1,2, Stephanie Robertson1,3, Lena Wedlund3,4, Michael Lippert5, Mattias Rantalainen6, Jonas Bergh1,7, Johan Hartman1,3,4.   

Abstract

AIMS: During pathological examination of breast tumours, proliferative activity is routinely evaluated by a count of mitoses. Adding immunohistochemical stains of Ki67 provides extra prognostic and predictive information. However, the currently used methods for these evaluations suffer from imperfect reproducibility. It is still unclear whether analysis of Ki67 should be performed in hot spots, in the tumour periphery, or as an average of the whole tumour section. The aim of this study was to compare the clinical relevance of mitoses, Ki67 and phosphohistone H3 in two cohorts of primary breast cancer specimens (total n = 294). METHODS AND
RESULTS: Both manual and digital image analysis scores were evaluated for sensitivity and specificity for luminal B versus A subtype as defined by PAM50 gene expression assays, for high versus low transcriptomic grade, for axillary lymph node status, and for prognostic value in terms of prediction of overall and relapse-free survival. Digital image analysis of Ki67 outperformed the other markers, especially in hot spots. Tumours with high Ki67 expression and high numbers of phosphohistone H3-positive cells had significantly increased hazard ratios for all-cause mortality within 10 years from diagnosis. Replacing manual mitotic counts with digital image analysis of Ki67 in hot spots increased the differences in overall survival between the highest and lowest histological grades, and added significant prognostic information.
CONCLUSIONS: Digital image analysis of Ki67 in hot spots is the marker of choice for routine analysis of proliferation in breast cancer.
© 2017 John Wiley & Sons Ltd.

Entities:  

Keywords:  Ki67; breast cancer; digital image analysis; hot spots; mitoses; phosphohistone H3; proliferation

Mesh:

Substances:

Year:  2018        PMID: 29220095     DOI: 10.1111/his.13452

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


  25 in total

1.  AI Model for Prostate Biopsies Predicts Cancer Survival.

Authors:  Kevin Sandeman; Sami Blom; Ville Koponen; Anniina Manninen; Juuso Juhila; Antti Rannikko; Tuomas Ropponen; Tuomas Mirtti
Journal:  Diagnostics (Basel)       Date:  2022-04-20

2.  Digital Image Analysis of Ki-67 Stained Tissue Microarrays and Recurrence in Tamoxifen-Treated Breast Cancer Patients.

Authors:  Deirdre Cronin-Fenton; Emiel A M Janssen; Nina Gran Egeland; Kristin Jonsdottir; Kristina Lystlund Lauridsen; Ivar Skaland; Cathrine F Hjorth; Einar G Gudlaugsson; Stephen Hamilton-Dutoit; Timothy L Lash
Journal:  Clin Epidemiol       Date:  2020-07-20       Impact factor: 4.790

3.  Quantitative Assessment of Epithelial Proliferation in Rat Mammary Gland Using Artificial Intelligence Independent of Choice of Proliferation Marker.

Authors:  Tobias H Dovmark; Peter H Kvist; Anne-Marie Mølck; Henning Hvid
Journal:  J Histochem Cytochem       Date:  2022-01-20       Impact factor: 2.479

4.  Sequential immunohistochemistry and virtual image reconstruction using a single slide for quantitative KI67 measurement in breast cancer.

Authors:  Garazi Serna; Sara Simonetti; Roberta Fasani; Francesca Pagliuca; Xavier Guardia; Paqui Gallego; Jose Jimenez; Vicente Peg; Cristina Saura; Serenella Eppenberger-Castori; Santiago Ramon Y Cajal; Luigi Terracciano; Paolo Nuciforo
Journal:  Breast       Date:  2020-07-13       Impact factor: 4.380

5.  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

Review 6.  Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association.

Authors:  Famke Aeffner; Mark D Zarella; Nathan Buchbinder; Marilyn M Bui; Matthew R Goodman; Douglas J Hartman; Giovanni M Lujan; Mariam A Molani; Anil V Parwani; Kate Lillard; Oliver C Turner; Venkata N P Vemuri; Ana G Yuil-Valdes; Douglas Bowman
Journal:  J Pathol Inform       Date:  2019-03-08

7.  Digital Image Analysis of BAP-1 Accurately Predicts Uveal Melanoma Metastasis.

Authors:  Gustav Stålhammar; Thonnie Rose O See; Stephen Phillips; Stefan Seregard; Hans E Grossniklaus
Journal:  Transl Vis Sci Technol       Date:  2019-05-06       Impact factor: 3.283

Review 8.  Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.

Authors:  Esther Abels; Liron Pantanowitz; Famke Aeffner; Mark D Zarella; Jeroen van der Laak; Marilyn M Bui; Venkata Np Vemuri; Anil V Parwani; Jeff Gibbs; Emmanuel Agosto-Arroyo; Andrew H Beck; Cleopatra Kozlowski
Journal:  J Pathol       Date:  2019-09-03       Impact factor: 7.996

9.  Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.

Authors:  Haydee Lara; Zaibo Li; Esther Abels; Famke Aeffner; Marilyn M Bui; Ehab A ElGabry; Cleopatra Kozlowski; Michael C Montalto; Anil V Parwani; Mark D Zarella; Douglas Bowman; David Rimm; Liron Pantanowitz
Journal:  Appl Immunohistochem Mol Morphol       Date:  2021-08-01

10.  Piloting a Deep Learning Model for Predicting Nuclear BAP1 Immunohistochemical Expression of Uveal Melanoma from Hematoxylin-and-Eosin Sections.

Authors:  Hongrun Zhang; Helen Kalirai; Amelia Acha-Sagredo; Xiaoyun Yang; Yalin Zheng; Sarah E Coupland
Journal:  Transl Vis Sci Technol       Date:  2020-09-01       Impact factor: 3.283

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.