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Automated assessment of Ki‐67 in breast cancer: the utility of digital image analysis using virtual triple staining and whole slide imaging
Author(s) -
Hida Akira I,
Omanovic Dzenita,
Pedersen Lars,
Oshiro Yumi,
Ogura Takashi,
Nomura Tsunehisa,
Kurebayashi Junichi,
Kanomata Naoki,
Moriya Takuya
Publication year - 2020
Publication title -
histopathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.626
H-Index - 124
eISSN - 1365-2559
pISSN - 0309-0167
DOI - 10.1111/his.14140
Subject(s) - medicine , breast cancer , hazard ratio , cytokeratin , ki 67 , immunohistochemistry , image analysis , digital image analysis , cancer , pathology , staining , oncology , radiology , digital image , image processing , artificial intelligence , computer science , confidence interval , computer vision , image (mathematics)
Aims Precise evaluation of proliferative activity is essential for the stratified treatment of luminal‐type breast cancer (BC). Immunohistochemical staining of Ki‐67 has been widely used to determine proliferative activity and is recognised to be a useful prognostic marker. However, there remains discussion concerning the methodology. We aimed to develop an automated and reliable Ki‐67 assessment approach for invasive BC. Materials and results A retrospective study was designed to include two cohorts consisting of 152 and 261 consecutive patients with luminal‐type BC. Representative tissue blocks following surgery were collected, and three serial sections were stained automatically with Ki‐67, pan‐cytokeratin and p63. The whole slides were scanned digitally and aligned using VirtualTripleStaining – an extension to the VirtualDoubleStaining™ technique provided by Visiopharm software. The aligned files underwent automated invasive cancer detection, hot‐spot identification and Ki‐67 counting. The automated scores showed a significant positive correlation with the pathologists' scores ( r = 0.82, P < 0.0001). Among selected patients with curative surgery and standard adjuvant therapies ( n = 130), the digitally assessed low Ki‐67 group (<20%) demonstrated a significantly better prognosis (breast cancer‐specific survival, P = 0.030; hazard ratio = 0.038) than the high Ki‐67 group. Conclusions Digital image analysis yielded similar results to the scores determined by experienced pathologists. The prognostic utility was verified in our cohort, and an automated process is expected to have high reproducibility. Although some pitfalls were confirmed and thus need to be monitored by laboratory staff, the application could be utilised for the assessment of BC.