Melanie Dawe, Wei Shi, Tian Yu Liu, Katherine Lajkosz, Yukiko Shibahara, Nakita E K Gopal, Rokshana Geread, Seyed Mirjahanmardi, Carrie X Wei, Sehrish Butt, Moustafa Abdalla, Sabrina Manolescu, Sheng-Ben Liang, Dianne Chadwick, Michael H A Roehrl, Trevor D McKee, Adewunmi Adeoye, David McCready, April Khademi, Fei-Fei Liu, Anthony Fyles, Susan J Done
Ki-67 is a nuclear protein associated with proliferation, and a strong potential biomarker in breast cancer, but is not routinely measured in current clinical management due to a lack of standardization. Digital image analysis (DIA) is a promising technology that could allow high throughput analysis and standardization. There is a dearth of data on the clinical reliability as well as intra- and inter-algorithmic variability of different DIA methods. In this study, we scored and compared a set of breast cancer cases in which manually counted Ki-67 has already been demonstrated to have prognostic value (n=278) to five DIA methods; namely, Aperio ePathology, Definiens Tissue Studio, Qupath, an unsupervised IHC color histogram (IHCCH) algorithm and a deep learning pipeline piNET...
January 25, 2024: Laboratory Investigation; a Journal of Technical Methods and Pathology