Ultrasound-based shrinkage patterns at mid-neoadjuvant chemotherapy for predicting pathological complete response in invasive breast cancer.

Objective: To develop an ultrasound-based tumor shrinkage pattern and subsequently construct a predictive model for pathological complete response by analyzing changes in ultrasonic features and measurements at mid-treatment of neoadjuvant chemotherapy (NAC) in invasive breast cancer, ultimately evaluating its clinical applicability. Methods: This study included all breast cancer patients who underwent NAC and subsequent surgery between January 2017 and December 2024. All patients received breast ultrasound examinations before and during NAC. All breast tumor images were reviewed by two experienced sonographers, who assessed and documented the ultrasound characteristics of the tumors. Based on the comparison of parameter changes, the tumors were classified into two patterns: centripetal shrinkage (CS) and non-centripetal shrinkage (NCS). NCS was further subdivided into four patterns: echogenic change, partial shrinkage, dendritic shrinkage, and fragmentation. According to the postoperative pathological results, patients were categorized into two groups: the pathological complete response (pCR) group and the non-pCR group. Univariate analysis and multivariate logistic regression analysis were performed to identify independent predictors for the predictive model. A nomogram was constructed, and receiver operating characteristic curves were plotted to evaluate the model's performance. Results: The study included a total of 287 lesions. The results of the logistic shrinkage analysis indicated CS, ΔS(the rate of area change), echogenicity changes, Adler decrease, HER-2 positivity, and Ki-67 were identified as independent predictors of pCR (all P< 0.05). The nomogram model based on these factors demonstrated areas under the curve of 0.906 (95% CI = 0.866 - 0.946) in the training set and 0.850 (95%CI = 0.770 - 0.929) in the testing set. The univariate model using only the shrinkage pattern (CS vs NCS) yielded an AUC of 0.741 (95% CI: 0.680 - 0.803). Conclusion: It is feasible to establish tumor shrinkage patterns based on ultrasound examinations during the mid-NAC stage. The nomogram-based predictive model, which combines ultrasound image changes and tumor pathological indicators, can predict NAC outcomes and holds certain clinical application value.