Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts

Abstract

Breast cancer is one of the most common cancer in women around the world. For diagnosis, pathologists evaluate the expression of biomarkers such as HER2 protein using immunohistochemistry over tissue extracted by a biopsy. This assessment is performed through microscopic inspection, estimating intensity and integrity of the membrane cells’s staining and scoring the sample as 0 (negative), 1+, 2+, or 3+ (positive): a subjective decision that depends on the interpretation of the pahologist. This work is aimed to achieve consensus among opinions of pathologists in cases of HER2 breast cancer biopsies, using supervised learning methods based on multiple experts. The main goal is to generate a reliable public breast cancer gold-standard, to be used as training/testing dataset in future developments of machine learning methods for automatic HER2 overexpression assessment. There were collected 30 breast cancer biopsies, with positive and negative diagnosis, where tumor regions were marked as regions-of-interest (ROIs). Magnification of 20× was used to crop non-overlapping rectangular sections according to a grid over the ROIs, leading a dataset with 1.250 images. In order to collect the pathologists’ opinions, an Android application was developed. The biopsy sections are presented in a random way, and for each image, the expert must assign a score (0, 1+, 2+, 3+). Currently, six referent Chilean breast cancer pathologists are working on the same set of samples. Getting the pathologists’ acceptance was a hard and time consuming task. Even more, obtaining the scoring of pathologists is a task that requires subtlety communication and time to manage their progress in the use of the application.