94P ML-based pathological and radiological features reveal deeper insights into histological response to neoadjuvant therapies in HER2-positive early breast cancer

Tumoral response to neoadjuvant therapy (NAT) and lymph node involvement (LN+) are key risk factors for patients (pts) with HER2-positive (HER2+) early breast (eBC). Machine learning (ML) enables predictive modeling by integrating clinical, immunological, and radiomic features to predict response to NAT.