Comparative Analysis of Nomogram and Machine Learning Models for Predicting Axillary Lymph Node Metastasis in Early-Stage Breast Cancer: A Study on Clinically and Ultrasound-Negative Axillary Cases Across Two Centers

Meiying Yan; Dilin He; Yu Sun; Long Huang; Linli Cai; Chen Wang; Jincao Yao; Xiangyang Li; Hongping Song; Chen Yang

doi:10.1016/j.ultrasmedbio.2024.11.003

Comparative Analysis of Nomogram and Machine Learning Models for Predicting Axillary Lymph Node Metastasis in Early-Stage Breast Cancer: A Study on Clinically and Ultrasound-Negative Axillary Cases Across Two Centers

Ultrasound Med Biol. 2024 Dec 2:S0301-5629(24)00428-9. doi: 10.1016/j.ultrasmedbio.2024.11.003. Online ahead of print.

Authors

Meiying Yan¹, Dilin He², Yu Sun³, Long Huang⁴, Linli Cai⁵, Chen Wang⁶, Jincao Yao¹, Xiangyang Li³, Hongping Song⁵, Chen Yang⁷

Affiliations

¹ Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou City, Zhejiang, PR China.
² Department of Ultrasound, The First People's Hospital of Fuyang District, Hangzhou City, Zhejiang, PR China.
³ Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou City, Zhejiang, PR China; Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou City, Zhejiang, PR China.
⁴ Legal Department, NetEase Hangzhou Institute, Hangzhou City, Zhejiang, PR China.
⁵ Department of Ultrasonic Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an City, Shaanxi, PR China.
⁶ Department of Breast Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou City, Zhejiang, PR China.
⁷ Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou City, Zhejiang, PR China. Electronic address: yangchen@zjcc.org.cn.

PMID: 39627056
DOI: 10.1016/j.ultrasmedbio.2024.11.003

Abstract

Objective: Early and accurate prediction of axillary lymph node metastasis (ALNM) is crucial in determining appropriate treatment strategies for patients with early-stage breast cancer. The aim of this study was to evaluate the efficacy of radiomic features extracted from ultrasound (US) images combined with machine learning (ML) methods in predicting ALNM to improve diagnostic accuracy and patient prognosis.

Methods: In this retrospective study, data of 282 early-stage breast cancer patients from two centers were analyzed. We considered clinicopathological characteristics, conventional US features, contrast-enhanced ultrasound (CEUS) characteristics, and radiomics features. Radiomics features were extracted from US images, and using least absolute shrinkage and selection operator (LASSO) regression, 12 key features were selected to compute a Radiomics score (Rad-score). A nomogram was developed based on these features, alongside five ML models: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost). Model performance was evaluated using metrics such as the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), negative predictive value (NPV), and positive predictive value (PPV).

Results: Both the nomogram and ML models, including the Rad-score combined with histologic type, significantly predicted ALNM. Among all models, the XGBoost model showed the best performance with an AUC of 0.810 and an accuracy of 84.1% in the external test set, surpassing the nomogram and other ML models. SHapley Additive exPlanations (SHAP) analysis further provided insights into the influence of individual radiomics features on ALNM prediction.

Conclusions: While the nomogram provides a useful traditional statistical approach, integrating radiomics features with ML, particularly the XGBoost model enhanced by SHAP interpretability, offers superior predictive accuracy for ALNM in early-stage breast cancer patients.

Keywords: Algorithm; Axillary; Breast neoplasm; Metastasis; Ultrasonography.