Multi-phase DCE-MRI delta-radiomics predicts Ki-67 changes in 148 breast cancer patients after neoadjuvant therapyMRI features may help predict Ki-67 changes in breast cancer after treatment

Ki-67 is a key biomarker of tumor proliferation in breast cancer. A reduction in Ki-67 following neoadjuvant therapy (NAT) reflects chemosensitivity and holds significant prognostic value. Therefore, pre-treatment assessment of Ki-67 dynamics during NAT is crucial for evaluating patient prognosis.This study aims to predict the change in Ki-67 index in breast cancer patients following NAT using radiomic features derived from DCE-MRI. This retrospective study enrolled 148 breast cancer patients who underwent surgical resection after 6–8 cycles of NAT, randomly divided into training (n=104) and test cohorts (n=44) at a 7:3 ratio. Multivariable logistic regression (P ROC curve analysis demonstrated that the peak-to-early delta-radiomics model achieved the best diagnostic performance in the testing cohort with an AUC of 0.817(95% CI: 0.685–0.949), significantly outperforming the delayed-to-early delta model [AUC = 0.648(95% CI: 0.484–0.812)]and the standalone peak-phase model [AUC = 0.615(95% CI: 0.444–0.785)]. Logistic regression analysis revealed that HER2 status (p = 0.031) and histological grade (p Delta-radiomics based on MRI, combined with clinical parameters, represents a promising non-invasive approach for more accurately predicting Ki-67 downstaging in breast cancer following NAT, outperforming conventional radiomics models. Integrating radiomic features with clinical information holds the potential to further optimize individualized treatment strategies and improve prognostic assessment for breast cancer patients.