The predictive model demonstrates potential in assessing pathological complete response in HER2+ breast cancer, suggesting refined treatment strategies.
Key Points
The predictive model for pathological complete response integrates clinical and pathological parameters for HER2+ breast cancer.
Patients receiving neoadjuvant therapy with trastuzumab and pertuzumab showed varied pCR rates, emphasizing the need for refined models.
Logistic regression and machine learning techniques provided an accuracy of 62.4% to 65.9%, indicating effective prediction capabilities.
Validation in larger cohorts is necessary to confirm the model's robustness and its potential impact on personalized cancer treatment.