<p>Establishment of Simple Nomograms for Predicting Axillary Lymph Node Involvement in Early Breast Cancer</p>

Abstract

These authors contributed equally to this workPurpose: Axillary lymph node (ALN) involvement is an important prognostic factor of early invasive breast cancer. The objective of this study was to establish simple nomograms for predicting ALN involvement based on ultrasound (US) characteristics and evaluate the predictive value of US in the detection of ALN involvement. Patients and Methods: A total of 1328 patients with cT1-2N0 breast cancer by physical exam were retrospectively analyzed. Univariate analysis was used for the comparison of variables, and multivariate analysis was performed by binary logistic regression analysis. The R software was used to establish simple nomograms based on the US characteristics alone. The receiver operating characteristic (ROC) curves of the prediction model and the verification group were drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of the prediction model. A calibration curve was plotted to assess the nomogram predictions vs the actual observations of the ALN metastasis rate and axillary tumor burden rate. Results: The ALN metastasis rates of the training group and the validation group were 35.1% and 34.1%, respectively. Multivariate analysis showed that molecular subtype, lymphovascular invasion, mass descriptors (size, margin, microcalcification and blood flow signal) and LN descriptors (shape, cortical thickness and long-to-short ratio) were independent impact factors in early breast cancer. The AUC of ALN metastasis rate of prediction model based on US features was 0.802, the AUC of high tumor burden rate was 0.873, and the AUC of external validation group was 0.731 and 0.802, respectively. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual metastasis rate and the high tumor burden rate. The results showed that preoperative US had a sensitivity of 59.4% and a specificity of 88.9% for predicting the ALN metastasis rate. Conclusion: The successfully established nomograms based on US characteristics to predict ALN metastasis rate and high axillary tumor burden rate in early breast cancer can achieve individual prediction. Compared with other nomogram predictions, it is more intuitive, and can help clinical decision-making; thus, it should be promoted. However, at this time US features alone are insufficient to replace sentinel lymph node biopsy.

PUMCH-SLN nomogram for the preoperative prediction of sentinel lymph node metastasis in breast cancer

“…In recent years, more and more nomograms have been built using imaging data, for example, predicting ALN in early breast cancer (36), or non-SLN metastasis in patients during neoadjuvant chemotherapy (37). These models share similarities with ours, but are also fundamentally different.…”

Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

“…A retrospective study analyzed US features of 1328 cT1-2N0 BC and established nomograms for ALNM prediction. The AUC of the prediction model and external validation group was 0.802 and 0.73, respectively [ 38 ]. Another study using deep learning algorithms based on US images established an ALNM prediction model and got AUC of 0.805 [ 39 ].…”

A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP

“…In addition, ultrasonographic signs such as microcalcification in the lesion, burr-like edges of the lesion, and disordered and distorted tissue structure around the lesion had a significant positive effect on the diagnosis of SLN metastasis by the XGBoost model. This may be because tumor cells infiltrate the surrounding tissues, invading the Cooper’s ligament and the lymph nodes through the lymphatic vessels ( 22 ). In this study, suspicious lymph nodes were assigned the largest contribution value in the SHAP map, which is consistent with previous studies ( 23 ) in which the detection of suspicious lymph nodes by ultrasound improved the diagnostic specificity of breast cancer SLN metastases.…”