Gold-standard of HER2 breast cancer biopsies using supervised learning based on multiple pathologist annotations

TL;DR

This study analyzes multiple pathologist annotations of HER2 breast cancer biopsies to develop a supervised learning gold-standard, aiming to reduce subjectivity and variability in diagnosis.

Contribution

It provides a preliminary data analysis of multi-expert annotations to establish a reliable gold-standard for HER2 scoring using supervised learning.

Findings

Achieved substantial intra-expert agreement

Achieved moderate inter-expert agreement

Analyzed variability in biopsy scoring

Abstract

Breast cancer is one of the most common cancer in women around the world. For diagnosis, pathologists evaluate biomarkers such as HER2 protein using immunohistochemistry over tissue extracted by a biopsy. Through microscopic inspection, this assessment estimates the intensity and integrity of the membrane cells' staining and scores the sample as 0, 1+, 2+, or 3+: a subjective decision that depends on the interpretation of the pathologist. This paper presents the preliminary data analysis of the annotations of three pathologists over the same set of samples obtained using 20x magnification and including $1,252$ non-overlapping biopsy patches. We evaluate the intra- and inter-expert variability achieving substantial and moderate agreement, respectively, according to Fleiss' Kappa coefficient, as a previous stage towards a generation of a HER2 breast cancer biopsy gold-standard using supervised learning from multiple pathologist annotations.