Evaluating the Predictive Value of Post-Treatment Superb Microvascular Imaging for Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Cancer

Purpose: To compare the efficacy of Superb Microvascular Imaging (SMI) with grayscale ultrasound (US) and dynamic contrast-enhanced MRI in predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in invasive breast cancer. Methods: A total of 115 patients included in the study were evaluated based on their pre-treatment imaging findings (US, mammography, and MRI). Following completion of NAC, all patients underwent grayscale US and SMI examinations. In patients with available post-NAC MRI, treatment response was additionally assessed by comparing MRI findings. Imaging results were correlated with postoperative pathological outcomes, which served as the reference standard. pCR was defined as the absence of residual invasive carcinoma, regardless of ductal carcinoma in situ. Molecular subtype, Ki-67, and axillary status were recorded. Statistical analyses included chi-square tests and stepwise multiple logistic regression. Significance was set at p < 0.05 (95% CI). Results: The median age was 51 years (range: 30–75). Most tumors were high-grade (55%) and invasive ductal carcinoma (95%). Breast-pCR was achieved in 43% of patients. Significant predictors of pCR included hormone receptor negativity, HER-2 positivity, high Ki-67 expression (≥40%), non-luminal subtype, and complete radiologic response on US and MRI (p < 0.05). Lower SMI index values were strongly associated with pCR (p < 0.001), with an optimal cut-off of 1.8 demonstrating good diagnostic performance (AUC = 0.804, 95% CI: 0.721–0.887). In multivariate analysis, the combined model including US, SMI, HER-2 status, and MRI showed the highest predictive performance (AUC = 0.890, 95% CI: 0.829–0.950), explaining 55.1% of the variance in pCR. Conclusions: An SMI index < 1.8, HER-2 positivity, and complete response on US and MRI are independent predictors of pCR after NAC. Combining SMI with multimodal imaging significantly improves predictive accuracy.