Bibliographic Details
| Title: |
Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images. |
| Authors: |
Sharma, Abhinav, Lövgren, Sandy Kang, Eriksson, Kajsa Ledesma, Wang, Yinxi, Robertson, Stephanie, Hartman, Johan, Rantalainen, Mattias |
| Source: |
Breast Cancer Research; 8/14/2024, Vol. 26 Issue 1, p1-10, 10p |
| Subject Terms: |
BREAST cancer, ARTIFICIAL intelligence, EPIDERMAL growth factor receptors, DISEASE risk factors, CANCER patients |
| Geographic Terms: |
NOTTINGHAM (England) |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
