Introduction
Worldwide, breast cancer is one of the most common cancer diagnoses among women1. Breast cancer also is one of the most studied cancers. A review of annual research spending in the United States of America shows more funding allocated for breast cancer research than for any other cancer type2. The drop in breast cancer mortality in the United States by 34% between 1990 and 20103, has been attributed to improvements in both detection and treatment, which reflects the high level of cancer research. However breast cancer remains a leading cause of cancer death in women1,3, highlighting the need for continued research and use of new technologies to drive breast cancer breakthroughs.
Researchers across a variety of fields, are increasingly using quantitative image analysis tools to support their research methods. These tools allow researchers to measure biomarker data in a truly objective, quantitative fashion, which offers a number of advantages over manual qualitative or semi-quantitative biomarker review. This includes generation of research data that are highly standardized and reproducible, reduction of inter- and intra-observer variability and subjectivity. In addition, it offers the ability to analyze histology images in a high-throughput fashion with minimal user interaction, reducing manual effort and study turnaround time. With the emergence of digital pathology, users now have access to a wide assortment of computer-assisted image analysis options, from basic pixel counting to highly specialized tools for specific applications. Leica Biosystems Aperio ePathology offers a suite of customizable algorithms, which can be trained by the user to work across a range of tissue and biomarker types. The flexibility of these algorithms makes them ideal for research applications, allowing scientists to utilize each tool for multiple studies. This review paper addresses recent peer-reviewed publications on use of Aperio Image Analysis algorithms for breast cancer research, including applications such as elucidation of tumorigenic pathways, identification of novel prognostic indicators, development of therapeutic targets, and validation for clinical decision support.
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