Predicting Pathological Complete Response (pCR) in Breast Cancer Neoadjuvant Therapy: A Nomogram Based on Contrast-Enhanced Ultrasound and Clinical-Pathological Features

When pCR is defined as the absence of invasive cancer in both the breast and axillary lymph nodes, regardless of the presence of ductal carcinoma in situ in the primary lesion, the long-term survival outcome is better than the absence of invasive cancer solely in the breast and better than the presence of invasive cancer in the breast or axillary lymph nodes [34-36]. Furthermore, as the burden of residual cancer lesions increases, the disease-free survival time of patients decreases [37]. Considering that the primary lesion of breast cancer and axillary lymph nodes have consistent sensitivity to NAT drugs [4], and grayscale ultrasound and contrast-enhanced ultrasound have good diagnostic ability for the status of axillary lymph nodes [38], in this study, pCR is defined as the absence of invasive cancer in the breast, allowing for the presence of ductal carcinoma in situ, regardless of the status of axillary lymph nodes [39].

Increasing research indicates that different subtypes of breast cancer have varying pCR rates after the completion of neoadjuvant treatment. pCR is associated with various clinical-pathological factors such as estrogen receptor negativity (ER-), progesterone receptor negativity (PR-), positive HER2, low T staging, high histological grading, and high Ki-67 index [6, 7, 40-44]. Consequently, some studies have already established multifactorial models predicting pCR based on combined clinical-pathological imaging features [18, 41, 42]. Rui Zhao et al. utilized features of baseline and post-2-cycle CE-MRI and the apparent diffusion coefficient (ADC) of MRI DWI from 87 cases to establish a nomogram for predicting pCR, with AUC values of 0.939 and 0.944 for the training and validation cohorts, respectively [18]. Soo-Yeon Kim et al. collected clinical-pathological data from 359 patients, analyzed breast X-ray and MRI images before and after NAT treatment, and constructed a nomogram for predicting pCR, with an AUC of 0.90 (95% CI: 0.86-0.94) [41]. Currently, there are fewer reports on the establishment of multifactorial models predicting pCR at the early and mid phases of NAT and post-NAT completion using contrast-enhanced ultrasound combined with clinical-pathological features. Therefore, the main objective of this study is to explore the value of CEUS combined with clinical-pathological features in predicting pCR before surgery at the early, middle, and post-NAT stages in breast cancer patients undergoing NAT and to construct corresponding nomograms for these time periods.