Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay

A significant proportion of oestrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer patients are categorised as intermediate risk based on classic clinicopathological variables, thus providing limited information to guide treatment decisions. The Prosigna assay is one of the established prognostic multigene assays in clinical practice for risk profiling. Stratipath Breast is a novel deep learning-based image analysis tool that utilises histopathological images for risk profiling. In this study, we aimed to evaluate the Stratipath Breast tool for image-based risk profiling and compare it with the Prosigna assay. In a real-world breast cancer case series comprising 234 invasive tumours from patients with early ER+/HER2- breast cancer, clinically intermediate risk and eligible for chemotherapy, clinicopathological data including Prosigna results and corresponding haematoxylin and eosin (HE)-stained tissue slides were retrieved. The digitised HE slides were analysed by Stratipath Breast. Our findings showed that the Stratipath Breast analysis identified 49.6% of the clinically intermediate tumours as low risk and 50.4% as high risk. The Prosigna assay classified 32.5%, 47.0% and 20.5% tumours as low, intermediate and high risk, respectively. Among Prosigna intermediate-risk tumours, 47.3% were stratified as Stratipath low risk and 52.7% as high risk. In addition, 89.6% of Stratipath low-risk cases were classified as Prosigna low/intermediate risk. The overall agreement between the two tests for low-risk and high-risk groups was 71.0%, with a Cohen’s kappa of 0.42. For both risk profiling tests, grade and Ki67 differed significantly between risk groups. In conclusion, for the first time, we here present the results from a clinical evaluation of image-based risk stratification and show a considerable agreement to an established gene expression assay in routine breast pathology. The findings demonstrate that image-based risk profiling may aid in the identification of low-risk patients who could potentially be spared adjuvant chemotherapy. ### Competing Interest Statement The authors declare the following financial interest/personal relationships that may be considered as potential competing interests: Y.W. is employee at Stratipath AB and holds employee stock options. W.S. has nothing to declare. E.K. is employee at Stratipath AB. S.K.L. is employee at Stratipath AB and holds employee stock options. M.R. has obtained speakers honoraria from Pfizer and is co-founder and shareholder of Stratipath AB. S.R. is employee at Stratipath AB and holds employee stock options. J.H. has obtained speakers honoraria or advisory board renumerations from Roche, Novartis, AstraZeneca, Pfizer, Eli Lily, MSD and Gilead, and has received institutional research support from Cepheid, Novartis, Roche and AstraZeneca. J.H. is co-founder and shareholder of Stratipath AB. ### Funding Statement The work was supported by grants from Swedish Cancer Society, Region Stockholm, MedTechLabs, Cancer Society in Stockholm, Swedish Research Council, VINNOVA, SweLife, Swedish Breast Cancer Association. Stratipath AB provided the Stratipath Breast analysis. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Ethics committee of the Swedish Ethical Review Authority gave ethical approval for this work (2019-01908 with amendment 2021-00017, 2022-01102-02). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets analysed during the current study are not publicly available due to local privacy laws but are available from the corresponding author on reasonable request.