First CE-IVD marked AI solution for prognostic risk stratification of breast cancer patients.

Stockholm, Sweden, June 01, 2022 (GLOBE NEWSWIRE) — Stratipath, a global leader in AI-based precision diagnostic solutions, today announced that its AI software for prognostic risk stratification of breast cancers, Stratipath Breast, is now CE-IVD marked. This paves the way for clinical implementation in the European Union. Based on the analysis of digital histopathology whole slide images, stained with haematoxylin and eosin (H&E) the software provides novel decision support to clinicians and enables precision medicine for more patients.

Stratipath Breast is the first EU regulatory compliant solution for risk stratification of breast cancer using AI-based precision diagnostics to analyse cancer tissue, and enabling identification of patients with increased risk of disease progression.

In contrast to traditional molecular tests, AI-based risk stratification enables faster turnaround times for results, provides new information at the point of diagnosis and reduces the need for expensive molecular testing, allowing for wider use and benefit to more patients.

“Stratipath Breast offers a faster and cheaper alternative to molecular assays, allowing more patients to have access to precision diagnostics. By using Stratipath Breast, clinicians can diagnose with support from prognostic information, while reducing laboratory time and costs,” says Johan Hartman, professor in pathology at Karolinska Institutet, Stockholm, and co-founder of Stratipath.

Histological tumour grade is a strong prognostic indicator of breast cancer. Grading of invasive breast cancer is performed on all invasive breast cancers based on morphological assessment, according to the Nottingham Histologic Grade (NHG), resulting in the low- to high-risk categories NHG 1, 2 or 3. But currently, more than 50% of all breast cancer patients are categorised as of intermediate risk (i.e NHG 2), which provides little clinical utility for treatment decision-making. The consequential over- and undertreatment of patients with early breast cancer has become one of the main challenges for treating physicians, and the clinical decisions are often dependent on expensive molecular assays that are not accessible to the majority of patients.

Using deep learning, Stratipath Breast enables cancer detection and classification of intermediate risk tumours into low- and high-risk groups, based on grade-related tumour morphology. The stratification comes from a rigorous scientific development process and validation using multi-source real-world datasets, comprising histopathology images and associated clinical outcome data.

The system measures risk-associated morphological patterns locally in the image and aggregates this information across the analysed tissue area to establish whether the tumour belongs to the high- or low-risk group. Results from Stratipath Breast provide prognostic information and are intended to be used as a decision support tool, together with other clinical and pathological information.

Stratipath Breast provides an optimal workflow through integration with leading digital pathology solutions. It can also be used on its own, via the Stratipath customer web portal. Access to Stratipath Breast will be provided as a Software as a Service solution, by a subscription or pay as you use model. Not marketed in the USA.

About Stratipath
Stratipath is a spin-out from Karolinska Institutet in Stockholm, Sweden, bringing pioneering research in AI and precision medicine into clinical use. The company was founded in 2019 by Johan Hartman, M.D., PhD, Mattias Rantalainen, PhD and Fredrik Wetterhall with a mission to radically improve cancer treatment decisions and patient outcomes. Stratipath offers an AI precision diagnostic solution for healthcare and can offer an accelerated path for pharma and biotech companies to identify patients most likely to benefit from novel therapies. 

• Deep learning analysis of digital histopathology whole slide images
• First CE-IVD marked AI solution for prognostic risk stratification of breast cancer patients.