Galen™ Breast HER2 Aids Pathologists in Setting Higher Standards for Accurate and Reproducible Biomarker Scoring to Improve Patient Management
BOSTON, April 19, 2024 /PRNewswire/ — Ibex Medical Analytics (Ibex), the leader in AI-powered cancer diagnostics, today announced that Galen™ Breast HER2 has been named a bronze winner of the 2024 Edison Awards in the Diagnostic Technologies category. The awards, named after inventor Thomas Alva Edison, honor companies and innovations that are shaping the future, creating a positive impact in the world and improving lives.
Breast cancer impacts one in eight American women and as such, accurate and timely diagnosis is key to guiding treatment decisions and improving survival rates. Ibex’s Galen Breast HER2 is an AI-powered scoring solution that helps pathologists assess HER2 expression in tumors to support identification of breast cancer patients eligible for targeted therapies.
“The judges were thrilled to honor Galen Breast HER2 as a groundbreaking category-changer this year, showcasing the power of innovation to improve our lives for the better,” declared Frank Bonafilia, executive director of the Edison Awards.
HER2, one of the proteins responsible for division and proliferation of breast cancer cells, is expressed in many breast tumors and its accurate assessment is critical for identifying patients who are likely to benefit from HER2-directed therapies. Traditionally, pathologists evaluate HER2 in tumor samples visually, which may result in varied interpretations as scoring is semi-quantitative and thus somewhat subjective1. The recent emergence of antibody drug conjugates specifically targeting HER2, which are also effective against HER2-low tumors, meant that a new segment of HER2 expression became clinically actionable. Pathologists now need to be able to evaluate and identify lower levels of HER2 expression, despite limited experience in evaluating those lower cut-offs. AI-powered tools may help pathologists with accurate, rapid, and reproducible interpretation of HER2 protein expression, particularly HER2 low, further supporting oncologists in identifying effective therapies for their patients.
Ibex’s Galen Breast HER2 is an AI-powered HER2 IHC (immunohistochemistry) scoring solution that detects invasive tumor areas and quantifies HER2 expression, to support patient identification for targeted therapies. The solution uses a novel AI-powered computational pipeline to analyze HER2 IHC-stained slides, automatically detect the invasive tumor areas, identify the tumor cells, determine their staining pattern and rapidly calculate the HER2 IHC score with high accuracy and reproducibility. Galen Breast HER2 provides visualization of the AI findings to the pathologist, who can review the invasive areas detected by the algorithm, the cells’ staining patterns, the percentage calculated for each pattern, and make the final determination, thereby retaining full control of the scoring process.
“The advent of new therapies which hold great promise, and the continuously evolving knowledge of breast cancer, require its diagnosis be more precise than ever before to support oncologists in the identification of patients suited for breakthrough treatments,” said Joseph Mossel, Co-Founder and Chief Executive Officer of Ibex Medical Analytics. “We are grateful for this recognition by the prestigious Edison Awards committee as we remain dedicated to bringing accurate, timely and personalized diagnosis to every patient. The award highlights our innovative approach in developing AI-powered tools such as Galen Breast HER2 that set the new standard in breast cancer diagnosis.”
A multi-reader validation study demonstrated that pathologists supported by Galen Breast HER2 showed higher consistency and accuracy for HER2 scoring, particularly on the lower levels of HER2 expression, compared to pathologists who did not use AI. An early evidence program to generate data on the accuracy and efficiency of Galen Breast HER2 in clinical practice is now ongoing across 15 cancer centers and laboratories in the United States, Canada, Europe, the UK, and Brazil.
Galen Breast HER2 complements Galen Breast which helps pathologists detect and grade different types of invasive and non-invasive breast cancer, as well as identify multiple other clinically significant features, such as tumor-infiltrating lymphocytes (TILs), lymphovascular invasion (LVI) and microcalcifications. Galen Breast is used in routine practice in laboratories, hospitals and health systems worldwide and has demonstrated robust outcomes across multiple clinical studies2.3.
About Ibex Medical Analytics
Ibex Medical Analytics (Ibex) is transforming cancer diagnostics with world-leading clinical grade AI-powered solutions for pathology. Empowering physicians and supporting pathologists, Ibex is on a mission to provide accurate, timely and personalized cancer diagnosis for every patient. Ibex’s Galen™ is the first and most widely deployed AI-powered platform in pathology and demonstrated outstanding outcomes in multiple clinical studies2,3,4,5,6,7. Pathologists worldwide use Galen as part of their everyday routine practice to improve the accuracy of cancer diagnosis, implement comprehensive quality control measures, reduce turnaround times and boost productivity with more efficient workflows. For additional company information, please visit https://ibex-ai.com/ and follow us on LinkedIn and X.
The Galen™ platform includes solutions which are for Research Use Only (RUO) in the United States and not cleared by the FDA. Multiple Galen solutions are CE marked (IVDD and IVDR) and registered with the UK MHRA. For more information, including indication for use and regulatory approval in other countries, contact Ibex Medical Analytics.
Ibex Media Contact
Nechama Rosengarten
FINN Partners
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[1] Robbins C.J. et al. Multi-institutional Assessment of Pathologist Scoring HER2 Immunohistochemistry. Modern Pathology. 2023, 36(1):100032
[3] Vincent-Salomon et al., Primary Diagnosis of Breast Biopsies supported by AI versus Microscope: Multi-Site Clinical Reader Study. San Antonio Breast Cancer Symposium 2022.
[5] Rodriguez-Justo et al., Multi-Site Multi Reader Study on Artificial Intelligence-Assisted Primary Diagnosis
of Gastric Biopsies, Virchows Arch (2023) 483 (Suppl 1): S1
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