Introduction
Worldwide, breast cancer is one of the most common cancer diagnoses among women1. Breast cancer is also 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 volume of 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 treatment breakthroughs.
Researchers across a variety of fields are increasingly using quantitative image analysis tools to support their research. These tools allow researchers to measure biomarker data in a quantitative manner, which offers a number of advantages over manual qualitative or semi-quantitative review. This includes generation of research data that are standardized and reproducible, as well as 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 analysis 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 Digital Pathology 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.
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