Ibex reports positive outcomes for AI-supported diagnosis of breast cancer

Kintor Pharmaceutical drug gets positive results for breast cancer

Ibex Medical Analytics have just announced positive outcomes for its Galen Breast solution in diagnosing multiple cancer types and further expanding its portfolio for breast cancer diagnosis.

The CE-marked artificial-intelligence is generally available as the company partners with laboratories, hospitals and health systems across Europe.

Ibex Medical Analytics says Galen Breast supports improved quality and efficiency in the diagnosis of breast biopsies.

The company notes that 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.

Ibex Medical Analytics diagnostics platform

Galen is Ibex’s integrated diagnostics platform supporting pathologists in the diagnosis of breast, prostate, and gastric biopsies. The company says it 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.

It says the combination of a skilled pathologist together with the accuracy, speed and objectivity provided by AI has the potential to improve the quality of diagnosis, user experience, operational efficiency, and ultimately patient outcomes.

Ibex Medical Analytics says that the 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.

Study results

The full study results will be presented by Judith Sandbank, one of the principal investigators in the study at the European Congress of Pathology which takes place in Basel, Switzerland, between September 3-7.

Anne Vincent-Salomon, director of pathology at Institut Curie and one of the principal investigators in the study, said: “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.

“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.” 

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.

Angiolymphatic invasion

The company says the solution successfully detected clinically important cancer-related and non-cancer features, including tumor infiltrating lymphocytes, angiolymphatic invasion, microcalcifications and more.

Ibex Medical Analytics said that 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 earlier.

Stuart Schnitt, professor of pathology at Harvard Medical School, said: “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.

“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.”

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