Machine Learning Models to Quantify HER2 for Real-Time Tissue Image Analysis in Prospective Clinical Trials

Here, we developed, trained, and validated an automated ML-based model as a quality control tool for HER2 testing and monitoring in clinical trials. The ML model was trained using whole slide images (WSI) from multiple
sources to quantify HER2 expression, and measure stain intensity,
artifact content, tumor area, and DCIS (ductal carcinoma in-situ) across
a diversity of breast cancer phenotypes. Model quantified HER2 scores
were consistent with pathologist consensus scores across breast
cancer tissue types. These results support incorporation of ML-based
algorithms into clinical trial workflows to monitor HER2 testing quality
including scoring, tissue quality, and assay performance.