Ibex Reports Excellent Outcomes for AI-supported Diagnosis of Breast Cancer

News provided by

Sep 01, 2022, 07:00 ET

Pathologists who used Ibex's Galen™ Breast for cancer diagnosis obtained very high accuracy

In a multi-site clinical study

TEL AVIV, Israel, Sept. 1, 2022 /PRNewswire/ -- Ibex Medical Analytics, the leader in AI-powered cancer diagnostics, today announced outstanding outcomes for the Galen™ Breast solution in diagnosing multiple cancer types and further expansion of its AI portfolio for breast cancer diagnosis. The CE-Marked AI solution is generally available as Ibex partners with laboratories, hospitals and health systems across Europe to deploy Galen™ Breast into their routine workflow, supporting improved quality and efficiency in the diagnosis of breast biopsies.

Breast cancer is the most common malignant disease in women worldwide, with over two million new cases each year. As such, accurate and timely diagnosis is key to guiding treatment decisions by oncologists and improving patient survival rates.

Over the last several years, rapid advances in personalized medicine have resulted in a growing complexity of cancer diagnosis. Coupled with the increase in overall cancer incidence and a global decline in the number of pathologists, these trends have led to growing workloads imposed on pathology departments. Clearly, there is a growing need for automated solutions and decision-support tools that help pathologists detect cancer to the utmost accuracy more rapidly.

Galen is Ibex's integrated diagnostics platform supporting pathologists in the diagnosis of breast, prostate, and gastric biopsies. Galen is the most widely deployed AI technology in pathology, and laboratories, hospitals and health systems worldwide already use it as part of their everyday practice. The combination of a skilled pathologist together with the accuracy, speed and objectivity provided by Artificial Intelligence has the potential to improve the quality of diagnosis, user experience, operational efficiency, and ultimately patient outcomes.

Galen Breast demonstrated excellent outcomes in a blinded, multi-site clinical study at Institut Curie in France and Maccabi Healthcare Services in Israel. The study evaluated the performance of pathologists who used Ibex AI for diagnosing breast biopsies and compared them to pathologists who used only a microscope across multiple types of breast cancer including invasive and in-situ carcinoma as well as rare subtypes, such as metaplastic, mucinous, and other types of breast cancer. The study results showed very high accuracy and utility of Galen Breast across multiple scanning and staining platforms, and established its potential for improving the quality of diagnosis, compared to using a microscope alone. The full study results will be presented by Judith Sandbank, MD, Director of the Pathology Institute at Maccabi Healthcare Services⁵ and one of the principal investigators in the study, at the European Congress of Pathology which takes place in Basel, Switzerland, between September 3-7 (Ibex presents at booth no. 1).

Previous studies on Galen Breast established its AI algorithm's accuracy in detecting cancer, distinguishing between multiple subtypes such as ducal and lobular carcinomas, grading DCIS (ductal carcinoma in-situ) and identifying rare tumors. Moreover, the solution successfully detected clinically important cancer-related and non-cancer features, including tumor infiltrating lymphocytes, angiolymphatic invasion, microcalcifications and more^1,2. Galen also enables automated pre-ordering of the breast immunohistochemistry (IHC) panel and other tests which may help shorten the turnaround time for diagnosis of cancer cases, maximizing efficiency gains for laboratories and enabling patients to start treatment earlier³. Ibex is also working to expand its technology to support improved prognosis of breast cancer, by providing automated quantification of HER2, Ki67, ER and PR receptors in immunohistochemistry stained slides as part of its product portfolio. These innovative AI algorithms are intended to assist pathologists in their diagnosis which may help further enhance diagnostic efficiency and enable more accurate and objective scoring of breast biomarkers, allowing for improved treatment decisions and patient care.

"We are impressed with the successful study outcomes and the performance of Galen Breast, that was evaluated in a diagnostic setting which is identical to how our pathologists review cases in their daily routine," said Anne Vincent-Salomon, MD, Director of Pathology at Institut Curie and one of the principal investigators in the study⁴. "Our team demonstrated that when pathologists use Ibex's AI technology they achieve very high accuracy levels in diagnosing breast cancer over a broad range of subtypes, with higher quality than when using a microscope alone. With these results, and as more and more laboratories transition to digital pathology workflows, I look forward to seeing Artificial Intelligence broadly adopted in the field."

"AI is going to be a critical adjunct to diagnostic pathology going forward. Pathologists reviewing whole-slide images, in combination with an AI algorithm, will provide better diagnoses and better care than either a pathologist alone or AI alone," said Stuart Schnitt, MD, Chief of Breast Oncologic Pathology at the Dana-Farber/Brigham and Women's Cancer Center and Professor of Pathology at Harvard Medical School⁴. "AI has already demonstrated its value in helping pathologists improve the quality of breast cancer diagnosis and reduce misdiagnosis, and I am impressed with the outcomes of this new study, demonstrating the robustness and utility of Ibex AI in a primary diagnosis setting. I look forward to seeing the impact this solution will have on the overall performance of pathology departments and patient outcomes, as it becomes widely adopted."

"We are excited to announce this milestone results and enable breast pathologists to further adopt AI into their routine diagnosis of breast biopsies, following excellent outcomes in a clinical study" said Chaim Linhart, PhD, Co-founder and CTO of Ibex at Ibex Medical Analytics. "We believe that AI has an essential role to play in pathology and cancer diagnosis, extending beyond cancer detection to ultimately support pathologists across most of the diagnostic pathway, as demonstrated by the scope of clinical features available with Galen Breast, as well as the expansion toward automated quantification and scoring of breast biomarkers".

About Ibex Medical Analytics

Ibex pioneers AI-powered cancer diagnostics in pathology. We empower physicians to provide every patient with an accurate, timely and personalized cancer diagnosis by developing clinical-grade AI algorithms and digital workflows that help detect and grade cancer in biopsies. Our Galen™ platform is the first-ever AI-powered integrated diagnostics solution in pathology and used in routine clinical practice worldwide, supporting pathologists and providers in improving the quality and accuracy of diagnosis, implementing comprehensive quality control, reducing turnaround times and boosting productivity with more efficient workflows. Ibex's Artificial Intelligence technology is built on Deep Learning algorithms trained by a team of pathologists, data scientists and software engineers. For more information, go to www.ibex-ai.com.

Media Contacts
Nechama Feuerstein
FINN Partners
[email protected]
+1-551-444-0784

[1] Vincent-Salomon et al, Multi-Feature AI Algorithm for Cancer Diagnosis in Breast Biopsies: A Multi-Site Clinical Validation Study. USCAP 2022.
[2] Sandbank et al, Deployment of a Multi-Tissue AI-based Quality Control System in Routine Clinical Workflow. European Congress of Pathology 2020.
[3] Sandbank et al, Reporting and Diagnosis Cancer Breast for Solution AI an of Implementation in Clinical Practice. USCAP 2022.
[4] Dr. Vincent-Salomon and Dr. Schnitt are advisors to Ibex Medical Analytics
[5] Dr. Sandbank also serves as the Chief Medical Officer at Ibex Medical Analytics

Logo: https://mma.prnewswire.com/media/1839936/Ibex_Logo.jpg

SOURCE Ibex Medical Analytics