1028 Background: Oncotype DX (ODX) is widely used to guide adjuvant treatment decisions in early-stage ER+/HER2− breast cancer. However, ODX provides limited stratification of residual metastatic risk within patients receiving hormonal therapy (HT) alone or hormonal therapy following chemotherapy (HT+CT). We developed a cross-modality AI approach that derives risk scores directly from routine H p <0.01), identifying patients who developed distant recurrence despite low ODX scores. Among patients treated with HT+CT, AI score also stratified outcomes (HR=3.94; p <0.03), identifying patients who experienced metastasis despite combination therapy. Across treatment groups, AI score provided prognostic information beyond ODX and clinicopathologic variables. In multivariable analyses adjusting for ODX and clinical covariates, AI score remained independently associated with metastatic risk and demonstrated consistent stratification across clinically relevant subgroups. Validation of these findings in an expanded cohort is ongoing. Conclusions: This AI-based approach further stratifies outcomes among patients receiving HT or HT+CT as guided by ODX, directly from routine histopathology images in early-stage ER+/HER2− breast cancer. Thus, integration of AI-derived outcome stratification with ODX scores may improve individualized treatment decision-making and support consideration of alternative therapies.
Muhammad et al. (Wed,) studied this question.
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