How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer

Introduction The global increase in breast cancer incidence is determining substantial augment of pathologists' workload. Digital pathology (DP), which consists of digitizing haematoxylin–eosin (H&E)-stained slides into whole-slide images, enables new workflows and horizons. The integration of artificial intelligence (AI) with DP led to the emergence of computational pathology (CP), which has the potential of offering valuable diagnostic, prognostic and predictive tools. The aim of this narrative review is to provide an overview of the state of the art in this rapidly evolving field. Diagnostic, predictive and prognostic application Several AI tools demonstrated enhanced performances as compared with visual evaluation of pathologists, such as identification of invasive breast cancers, sentinel lymph node metastasis, histological grading, Ki-67, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2) and programmed death-ligand 1 (PD-L1) quantification. Due to this, most of them are currently authorized in the European market. Beyond diagnosis, CP has shown strong potential in predicting, from a simple H&E digital slide, treatment response (e.g. neoadjuvant chemotherapy, anti-HER2 therapy and endocrine therapy), immunohistochemistry status (ER, PR, HER2, PD-L1), molecular alterations (e.g. BRCA1/2 and homologous recombination deficiency status), genomic signatures (e.g. Oncotype DX) and ultimately the patient's risk of recurrence. Conclusion Despite challenges such as high validation costs, initial economic investments and potential AI biases, CP holds great promise. Continued effort to address these barriers and improve AI reliability are crucial. Emerging AI tools, including agent-driven systems, point towards the potential integration of CP into routine clinical workflow practice, even though clinical validation has not yet been established.