Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer

Objective: This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer.Methods: Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treat-ment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were mea-sured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (?) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predic-tive value of each parameter.Results: ?V, ?TTP and ?PI were independent predictors of pCR. The CEUS-ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone.Conclusion: The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.

Automated summary

This prospective study aimed to evaluate the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in predicting the treatment response of breast cancer patients receiving neoadjuvant chemotherapy (NAC). The study found that changes in tumor volume, time to reach peak intensity, and peak intensity were independent predictors of treatment response. The CEUS-ABUS model achieved the highest AUC, indicating its potential for optimizing treatment in breast cancer patients.