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

TLDR

HER2 expression by immunohistochemistry is a critical prognostic and therapeutic marker in invasive breast cancer, yet visual scoring is subjective and prone to interobserver variability. The study aims to establish a more objective method for HER2 assessment. An automated HER2 scoring contest was organized using 86 digitized whole‑slide images, where AI algorithms predicted IHC scores that were compared against expert consensus. The contest results showed that automated methods outperformed pathologists, highlighting the substantial potential of AI for objective HER2 scoring.

Abstract

Evaluating expression of the human epidermal growth factor receptor 2 (HER2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognized importance as a predictive and prognostic marker in clinical practice. However, visual scoring of HER2 is subjective, and consequently prone to interobserver variability. Given the prognostic and therapeutic implications of HER2 scoring, a more objective method is required. In this paper, we report on a recent automated HER2 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 HER2 scoring.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 HER2. 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 HER2 and show that the automated methods could beat the pathology experts on this contest data set.This paper presents a benchmark for comparing the performance of automated algorithms for scoring of HER2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.