Ibex Presents New Data from Multiple Studies Showcasing Accuracy of AI in Prostate, Breast and Gastric Cancer Diagnosis
Ibex: Studies to Be Presented at the United States and Canadian Academy of Pathology (USCAP) Annual Meeting
- Brigham and Women’s Hospital and Champalimaud Foundation research shows high accuracy levels of Galen Breast in identifying microinvasive carcinoma of the breast.
- University of Pittsburgh Medical Center study demonstrates pathologists’ high performance when using Galen Prostate to support primary diagnosis in a live clinical setting.
- Ohio State University Wexner Medical Center studies show high accuracy for Galen Breast and Galen Gastric in detecting and distinguishing between different types of cancer and in identifying multiple important pathologies.
BOSTON, March 21, 2024 /PRNewswire/ — Ibex Medical Analytics (Ibex), the leader in AI-powered cancer diagnostics, today announced excellent results across multiple clinical studies conducted with the University of Pittsburgh Medical Center (UPMC), Brigham and Women’s Hospital, Champalimaud Foundation and Ohio State University (OSU). These studies, highlighting the value of Ibex’s AI-powered cancer diagnostics solutions in both diagnostic quality and efficiency, will be presented next week at the United States and Canadian Academy of Pathology (USCAP) Annual Meeting.
Cancer incidence is rising around the world while its diagnosis becomes more complex and nuanced in an era of precision medicine, in which accurate diagnosis is key for enabling personalized and tailored therapies. Increasing demand and diagnostic workloads are compounded by a global shortage of pathologists who still rely heavily on manual work requiring visual analysis of biopsies. Ibex’s Galen™ platform helps overcome these challenges with Artificial Intelligence (AI)-powered workflows and decision-support tools that pathologists use in their everyday practice.
A study conducted at Brigham and Women’s Hospital and Champalimaud Foundation evaluated Galen Breast’s performance in identifying microinvasive carcinoma of the breast in a challenging patient cohort. Microinvasive carcinoma is defined as the focus of invasive breast cancer no larger than 1mm, in which accurate detection has important implications for patient management. The results indicate that Galen Breast’s AI algorithm is highly accurate in detecting microinvasive lesions and has the potential to improve consistency in diagnosis and support more efficient workflows in pathology laboratories.
“Microinvasive carcinoma is challenging and time-consuming to identify, and is often subject to overdiagnosis and underdiagnosis, adversely affecting treatment decisions. Therefore, Galen Breast’s accuracy in detecting and classifying these minute tumors is a vital step forward in enhancing diagnostic confidence and providing oncologists with a comprehensive pathology diagnosis,” said Stuart Schnitt, MD, Chief of Breast Oncologic Pathology at the Dana-Farber/Brigham Cancer Center, and an investigator in the study.
In another study conducted at UPMC, researchers assessed Galen Prostate’s performance in primary diagnosis of prostate biopsies. The study found the AI-powered solution was highly accurate in identifying and grading cancer in a live clinical setting, with NPV and PPV of 0.991. The study also demonstrated positive feedback from participating pathologists, showing high satisfaction, particularly with Galen Prostate’s Gleason scoring, detection of perineural invasion and automated measurement of tissue and tumor length.
In addition, researchers from OSU Wexner Medical Center will present studies validating Galen Breast’s performance in detecting different types of breast cancer. This includes invasive and in situ carcinoma, as well as other clinically important pathologies, such as lobular neoplasia and microcalcifications across a diverse cohort of breast biopsies. They will also present a study validating the performance of Galen Gastric in the diagnosis of gastric cancer, neuroendocrine tumors, H.pylori and other gastric lesions, such as adenoma and low-grade dysplasia.
“Galen offers an unparalleled breadth of detection capabilities going well beyond cancer and becoming an integral part of everyday clinical practice in laboratories, hospitals and health systems globally,” said Dr. Chaim Linhart, Chief Technology Officer and Co-Founder of Ibex Medical Analytics. “With these outstanding outcomes achieved in leading US healthcare institutions across various tissue types, amplified by very positive user feedback, our technology continues to set the standard for AI solutions in pathology, helping clinicians improve patient outcomes.”
Ibex will be showcasing these findings at the USCAP Annual Meeting taking place in Baltimore, Maryland, between March 23 to 28, at booth number 755.
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. 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
Nechama.rosengarten@finnpartners.com
[1] NPV and PPV are statistical terms which define the precision of a diagnostic test in detecting a relevant feature.
[4] 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
SOURCE: PR Newswire
