Premium
Computerized image analysis of the Ki‐67 proliferation index in mantle cell lymphoma
Author(s) -
Blaker Yngvild N,
Brodtkorb Marianne,
Maddison John,
Hveem Tarjei S,
Nesheim John Arne,
Mohn Hans Martin,
Kolstad Arne,
Geisler Christian H,
Liestøl Knut,
Smeland Erlend B,
Holte Harald,
Delabie Jan,
Danielsen Håvard
Publication year - 2015
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.12624
Subject(s) - mantle cell lymphoma , image analysis , medicine , lymphoma , proliferation index , ki 67 , nuclear medicine , image processing , image (mathematics) , pathology , computer science , artificial intelligence , digital image , immunohistochemistry
Aims Manual counting of the fraction of Ki‐67‐positive cells (the Ki‐67 index) in 1000 tumour cells is considered the ‘gold standard’ to predict prognosis in mantle cell lymphoma ( MCL ). This time‐consuming method is replaced by the faster, but less accurate, semiquantitative estimation in routine practice. The aim of this study was to investigate the use of computerized image analysis software for scoring of Ki‐67 in MCL . Methods and results We developed an automated method for determining the Ki‐67 index by computerized image analysis and tested it using a cohort of 62 MCL patients. The data were compared to Ki‐67 scores obtained by semiquantitative estimation and image‐based manual counting. When using the Ki‐67 index as a continuous parameter, both image‐based manual counting and computerized image analysis were related inversely to survival ( P = 0.020 and P = 0.025, respectively). Ki‐67 index obtained by semiquantitative estimation was not associated significantly with survival ( P = 0.093). The results were validated in a second patient cohort with similar results. Conclusion Computerized image analysis of the Ki‐67 index in MCL is an attractive alternative to semiquantitative estimation and can be introduced easily in a routine diagnostic setting for risk stratification in MCL .