Ultrasound and clinicopathologic feature based model for early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer: A case-control study

Abstract Objective : The objective of this study was to develop a model combining ultrasound (US) and clinicopathological features for early prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer. Methods ： From October 2020 to March 2022, 304 breast cancer patients who underwent NAC in our Hospital were enrolled in this retrospective study. US and clinicopathological characteristics were collected from all patients in this study, and characteristics obtained by using univariate analysis at p<0.05 were subjected to multivariate analysis, and then the conventional US and clinicopathological characteristics independently associated with pathologic complete response (PCR) from the analysis were used to develop US models, clinicopathological models and their combined models by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity and specificity to assess their predictive efficacy. Results: The combined model had an AUC of 0.818, a sensitivity of 78.79%, a specificity of 74.15%, and an accuracy of 75.81% for early prediction of PCR to NAC in breast cancer, which was significantly better than the clinicopathological model (AUC=0.733) and the US model (AUC=0.758). In addition, seven features were screened as independent predictors, namely T-stage, lateral acoustic shadow, margin, calcification, blood flow score, HER2 expression, and maximum diameter reduction rate. Conclusions ： Lateral acoustic shadowing, calcification, margin, blood flow score, maximum diameter reduction rate measured by US, as well as clinical T-stage and HER2 expression are independent predictors of obtaining PCR after NAC in breast cancer patients, The conventional US combined with clinicopathological features to construct a combined model has a good diagnostic effect for early prediction of PCR in breast cancer and is expected to be a useful tool to assist clinicians in effectively determining the efficacy of NAC in breast cancer patients.