Prediction model of axillary lymph node status using automated breast ultrasound (ABUS) and ki-67 status in early-stage breast cancer

Abstract

Background Automated breast ultrasound (ABUS) is a useful choice in breast disease diagnosis. The axillary lymph node (ALN) status is crucial for predicting the clinical classification and deciding on the treatment of early-stage breast cancer (EBC) and could be the primary indicator of locoregional recurrence. We aimed to establish a prediction model using ABUS features of primary breast cancer to predict ALN status. Methods A total of 469 lesions were divided into the axillary lymph node metastasis (ALNM) group and the no ALNM (NALNM) group. Univariate analysis and multivariate analysis were used to analyze the difference of clinical factors and ABUS features between the two groups, and a predictive model of ALNM was established. Pathological results were as the gold standard. Results Ki-67, maximum diameter (MD), posterior feature shadowing or enhancement and hyperechoic halo were significant risk factors for ALNM in multivariate logistic regression analysis (P < 0.05). The four risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.791 (95% CI: 0.751, 0.831). The accuracy, sensitivity and specificity of the prediction model were 72.5%, 69.1% and 75.26%. The positive predictive value (PPV) and negative predictive value (NPV) were 66.08% and 79.93%, respectively. Distance to skin, MD, margin, shape, internal echo pattern, orientation, posterior features, and hyperechoic halo showed significant differences between stage I and stage II (P < 0.001). Conclusion ABUS features and Ki-67 can meaningfully predict ALNM in EBC and the prediction model may facilitate a more effective therapeutic schedule.

A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status

“…Age, tumor location, histological grade and Ki-67 have also been found to be independent risk factors for ALNM in earlier research. Nevertheless, due to variances in sample size and population selection, these parameters did not show significant differences in the logistic regression analysis of the present study ( 37 , 38 , 50 – 52 ).…”

“…A number of studies have established a close association between clinicopathological features of tumors and ALNM (36)(37)(38). In the present study, a nomogram model was developed to predict the risk of ALNM in patients with EBC and cN0.…”

Automated Breast Ultrasound (ABUS)-based radiomics nomogram: an individualized tool for predicting axillary lymph node tumor burden in patients with early breast cancer

“…These studies indicated good diagnostic performance of ABUS. Recently, several studies [ 12 , 32 ] showed some ABUS imaging features were significantly correlated with ALN status. In our study, ABUS model displayed adequate discriminative ability (AUC 0.772 in the training set and 0.736 in the test set).…”

“…As an emerging three-dimensional imaging technique, Automated Breast Ultrasound (ABUS), addresses the limitations of conventional handheld ultrasound (HHUS) and automatically scans the breast based on special high-frequency broadband sensors [ 10 ]. Several recent studies [ 11 , 12 ] have shown that some unique features of ABUS, although they are also visual assessment and qualitative descriptions, may provide additional information for breast lesions. Specifically, retraction phenomenon appears as a satellite model around the lesion, with high sensitivity (80%–89%) and specificity (96%–100%) for breast cancer [ 13 – 15 ].…”

Construction and validation of a nomogram prediction model for axillary lymph node metastasis of cT1 invasive breast cancer

“…Nomogram is recognized as a good predictive tool with good discriminating power and has been widely used to predict the type and prognosis of different diseases (Iasonos et al, 2008;Balachandran et al, 2015). The combined model (training set AUC = 0.869, validation set AUC = 0.820) was better than the ultrasound-based model (training set AUC = 0.844, validation set AUC = 0.771) in predicting axillary lymph node metastasis, and was also better than some other clinical prediction models (Gao et al, 2022;Geng et al, 2022;Wang et al, 2022), which may be related to the inclusion of ultrasonic features of lymph nodes in this study. The combined nomogram had an excellent ability to predict axillary lymph node metastasis, which can help clinicians predict axillary lymph node metastasis before surgery.…”