Stratipath Breast

Stratipath Breast is the first AI-based prognostic risk profiling tool, regulatory aligned for clinical use in the EU and the UK. The solution analyses cancer tissue, enabling the identification of patients with either an increased, or low, risk of disease progression. It is already in clinical use and validated in large-scale studies, ensuring robust and reliable performance.

AI-based risk stratification enables faster turnaround times for results, provides new information at the point of diagnosis, and can reduce the need for expensive gene expression testing, allowing for wider use and benefit to more patients.

Stratipath Breast is the first AI-based prognostic risk profiling tool, regulatory aligned for clinical use in the EU and the UK. The solution analyses cancer tissue, enabling the identification of patients with either an increased, or low, risk of disease progression. It is already in clinical use and validated in large-scale studies, ensuring robust and reliable performance.

AI-based risk stratification enables faster turnaround times for results, provides new information at the point of diagnosis, and can reduce the need for expensive gene expression testing, allowing for wider use and benefit to more patients.

How Stratipath breast works

The system measures risk-associated morphological patterns locally in the image and aggregates this information across the analysed tissue area to establish the risk stratification level. 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.

The AI analysis is carried out in parallel with the existing diagnostic workflow

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 model.

2. Stratipath Breast can also be used on its own, via the Stratipath customer web portal.

3. Routine slides is accessed through a file server.

4. Stratipath Breast cloud-based AI-analysis.

5. Results are accessible within the hour.

Stratification of invasive breast tumours into low- and high-risk groups

Histological tumour grade is a strong prognostic factor in breast cancer^1,². 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 ³. But currently, more than 50% of all breast cancer patients are categorised as of intermediate risk (i.e. NHG 2) ⁴, 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 clinical decisions are often dependent on expensive gene expression tests that are not accessible to the majority of patients. Computer-based image analysis enables precise and reproducible classification of digitised histopathology images, and has shown to be able to divide NHG 2 tumours into a low- and high-risk group based on grade-related morphology⁵. Using deep learning, Stratipath Breast enables stratification of invasive breast tumours, and especially those of intermediate risk, into low- and high-risk groups, based on risk-associated tumour morphology. The stratification comes from a rigorous scientific development and validation process using multi-source real-world datasets, comprising histopathology images and associated clinical outcome data.

References

^{1. Rakha EA, El-Sayed ME, Lee AH, Elston CW, Grainge MJ, Hodi Z, et al. Prognostic significance of Nottingham histologic grade in invasive breast carcinoma. J Clin Oncol. 2008;26(19):3153-8.↩}
^{2. Rakha EA, Reis-Filho JS, Baehner F, Dabbs DJ, Decker T, Eusebi V, et al. Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res. 2010;12(4):207.}^↩
^{3. Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology. 1991;19(5):403-10.↩}
^{4. Acs B, Rönnlund C, Hagerling C, Ehinger A, Kovács A, Røge R, et al. Variability in breast cancer biomarker assessment and the effect on oncological treatment decisions: A nationwide 5-year population-based study. Cancers. 2021;13(5):1166.↩}
^{5. Wang Y, Acs B, Robertson S, Liu B, Solorzano L, Wählby C, et al. Improved breast cancer histological grading using deep learning. Ann Oncol. 2022;33(1):89-98.↩}

Easily interpretable and actionable results

The results from the Stratipath Breast analysis are available as a PDF report, ready to download, share or include in the diagnostic report available for the tumour board.