Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study

Abstract

Ki67 expression has been a valuable prognostic variable in breast cancer, but has not seen broad adoption due to lack of standardization between institutions. Automation could represent a solution. Here we investigate the reproducibility of Ki67 measurement between three image analysis platforms with supervised classifiers performed by the same operator, by multiple operators, and finally we compare their accuracy in prognostic potential. Two breast cancer patient cohorts were used for this study. The standardization was done with the 30 cases of ER+ breast cancer that were used in phase 3 of International Ki67 in Breast Cancer Working Group initiatives where blocks were centrally cut and stained for Ki67. The outcome cohort was from 149 breast cancer cases from the Yale Pathology archives. A tissue microarray was built from representative tissue blocks with median follow-up of 120 months. The Mib-1 antibody (Dako) was used to detect Ki67 (dilution 1:100). HALO (IndicaLab), QuantCenter (3DHistech), and QuPath (open source software) digital image analysis (DIA) platforms were used to evaluate Ki67 expression. Intraclass correlation coefficient (ICC) was used to measure reproducibility. Between-DIA platform reproducibility was excellent (ICC: 0.933, CI: 0.879-0.966). Excellent reproducibility was found between all DIA platforms and the reference standard Ki67 values of Spectrum Webscope (QuPath-Spectrum Webscope ICC: 0.970, CI: 0.936-0.986; HALO-Spectrum Webscope ICC: 0.968, CI: 0.933-0.985; QuantCenter-Spectrum Webscope ICC: 0.964, CI: 0.919-0.983). All platforms showed excellent intra-DIA reproducibility (QuPath ICC: 0.992, CI: 0.986-0.996; HALO ICC: 0.972, CI: 0.924-0.988; QuantCenter ICC: 0.978, CI: 0.932-0.991). Comparing each DIA against outcome, the hazard ratios were similar. The inter-operator reproducibility was particularly high (ICC: 0.962-0.995). Our results showed outstanding reproducibility both within and between-DIA platforms, including one freely available DIA platform (QuPath). We also found the platforms essentially indistinguishable with respect to prediction of breast cancer patient outcome. Results justify multi-institutional DIA studies to assess clinical utility.

Analytical validation of a standardised scoring protocol for Ki67 immunohistochemistry on breast cancer excision whole sections: an international multicentre collaboration

“…NordiQC), involving comparison of laboratory scores with reference scores in periodic assessment challenges, will probably improve interobserver reproducibility further. Recent studies suggest that an even higher level of concordance can be achieved with automated image analysis . The IKWG is actively conducting studies in this area to assess how artificial intelligence may help to standardise Ki67 assessment .…”

Good staining quality ensuring the reproducibility of Ki67 assessment

“…Area selection of whole tissue sections in the clinical practice is more complex, and the assessment method may have direct influences on Ki67-LI and therapeutic decision making 18–20. Although digital image analysis (ImmunoRatio)21 was applied in only 3.8% of our participants and its benefits did not appear, the use of automated systems to measure Ki67 LI presents good interlaboratory reproducibility in some studies 22–24. Furthermore, the use of automated systems is thought to reduce the time and labour required for manual counting 22…”

Association of red blood cell distribution width and outcomes in patients with mantle cell lymphoma

“…Nevertheless, substantial variability in Ki67 scoring exists among different laboratories even the most experienced ones . Although Ki67 reproducibility can be improved by using digital image analysis, this technique is unavailable in most pathology laboratories . Other biological factors including SOX11 expression and TP53 mutation have been investigated.…”