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

Balázs Ács, Lilla Madaras, Kristóf Attila Kovács, Tamás Micsik, Anna Mária Tőkés, Balázs Győrffy, Janina Kulka, Attila Marcell Szász

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)115-127
Number of pages13
JournalPathology and Oncology Research
Volume24
Issue number1
DOIs
Publication statusPublished - Jan 1 2018

Keywords

  • Breast cancer
  • Concordance correlation
  • Digital image analysis
  • Intra-class correlation
  • Ki-67 proliferation index
  • Prognosis

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Oncology
  • Cancer Research

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