AI Article Synopsis

  • Stratipath Breast is an AI tool designed to classify breast cancer patients into high- and low-risk categories based on histopathology images, specifically using H&E-stained slides.
  • A study involving 2,719 patients from two Swedish hospitals evaluated this tool's prognostic performance, focusing on progression-free survival (PFS) among patients, particularly those with ER-positive/HER2-negative tumors.
  • Results showed that Stratipath Breast effectively predicts risk in various breast cancer subgroups, suggesting its potential to guide treatment decisions and reduce unnecessary therapies at a lower cost compared to molecular methods.

Article Abstract

Background: Stratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts.

Methods: This retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint.

Results: In the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63-4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2- Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22-3.98, p-value = 0.009) between low- and high-risk groups.

Conclusion: The results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2- subgroup and the NHG2/ER+/HER2- subgroup. Improved risk stratification of intermediate-risk ER+/HER2- breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11323658PMC
http://dx.doi.org/10.1186/s13058-024-01879-6DOI Listing

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