  Immunohistochemistry (IHC) analysis is a well-accepted and widely used method
for molecular subtyping, a procedure for prognosis and targeted therapy of
breast carcinoma, the most common type of tumor affecting women. There are four
molecular biomarkers namely progesterone receptor (PR), estrogen receptor (ER),
antigen Ki67, and human epidermal growth factor receptor 2 (HER2) whose
assessment is needed under IHC procedure to decide prognosis as well as
predictors of response to therapy. However, IHC scoring is based on subjective
microscopic examination of tumor morphology and suffers from poor
reproducibility, high subjectivity, and often incorrect scoring in low-score
cases. In this paper, we present, a deep learning-based semi-supervised
trained, fully automatic, decision support system (DSS) for IHC scoring of
invasive ductal carcinoma. Our system automatically detects the tumor region
removing artifacts and scores based on Allred standard. The system is developed
using 3 million pathologist-annotated image patches from 300 slides, fifty
thousand in-house cell annotations, and forty thousand pixels marking of HER2
membrane. We have conducted multicentric trials at four centers with three
different types of digital scanners in terms of percentage agreement with
doctors. And achieved agreements of 95, 92, 88 and 82 percent for Ki67, HER2,
ER, and PR stain categories, respectively. In addition to overall accuracy, we
found that there is 5 percent of cases where pathologist have changed their
score in favor of algorithm score while reviewing with detailed algorithmic
analysis. Our approach could improve the accuracy of IHC scoring and subsequent
therapy decisions, particularly where specialist expertise is unavailable. Our
system is highly modular. The proposed algorithm modules can be used to develop
DSS for other cancer types.
