Researchers discussed how artificial intelligence (AI) and digital pathology are improving risk assessment in HR-positive, HER2-negative early breast cancer. By combining clinical factors with AI analysis of digital slides and gene expression, doctors can now identify patients who appear high-risk clinically but are actually low-risk biologically.
AI helps predict both prognosis and treatment response. For example, a model combining clinical and pathology features identified 20% of high-risk patients who had only a 4.6% recurrence rate over nine years. AI can also guide endocrine therapy: patients with high SET ER/PR scores benefit from extended hormone therapy, while those with low scores may gain more from certain chemotherapy regimens.
Digital H&E slides analyzed by AI can mimic expensive genomic tests like the 21-gene Recurrence Score, making precise treatment decisions faster and more accessible. Overall, this AI-driven approach allows doctors to spare low-risk patients from unnecessary treatments while focusing aggressive therapy on those who truly need it.