HER HER 2 challenge contest: a detailed assessment of automated HER HER 2 scoring algorithms in whole slide images of breast cancer tissues

摘要

Aims Evaluating expression of the human epidermal growth factor receptor 2 ( HER 2) by visual examination of immunohistochemistry ( IHC ) on invasive breast cancer ( BC a) is a key part of the diagnostic assessment of BC a due to its recognized importance as a predictive and prognostic marker in clinical practice. However, visual scoring of HER 2 is subjective, and consequently prone to interobserver variability. Given the prognostic and therapeutic implications of HER 2 scoring, a more objective method is required. In this paper, we report on a recent automated HER 2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state‐of‐the‐art artificial intelligence ( AI )‐based automated methods for HER 2 scoring. Methods and results The contest data set comprised digitized whole slide images ( WSI ) of sections from 86 cases of invasive breast carcinoma stained with both haematoxylin and eosin (H&E) and IHC for HER 2. The contesting algorithms predicted scores of the IHC slides automatically for an unseen subset of the data set and the predicted scores were compared with the ‘ground truth’ (a consensus score from at least two experts). We also report on a simple ‘Man versus Machine’ contest for the scoring of HER 2 and show that the automated methods could beat the pathology experts on this contest data set. Conclusions This paper presents a benchmark for comparing the performance of automated algorithms for scoring of HER 2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.