ORIGINAL RESEARCH article
Front. Oncol.
Sec. Breast Cancer
Ultrasound radiomics predicts preoperative axillary lymph node metastasis status in early-stage breast cancer to support surgical decisions:A machine learning, monocenter study
Provisionally accepted
- 1Fujian Provincial Hospital, Fuzhou, China
- 2Fujian Medical University, Fuzhou, China
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ABSTRACT Background: The usual assessment for axillary lymph node (ALN) status in breast cancer (BC) in current clinical practice is based on an invasive procedure that has a low efficiency rate and frequently results in operative-associated problems for patients. Therefore, our goal was to create an effective preoperative ultrasound (US) radiomics evaluation method for ALN status in patients with clinical stages T1–2 invasive BC using machine learning (ML) approaches. Methods:Between January 2020 and January 2024, we retrospectively analyzed the medical records of 671 patients with histologically proven malignant breast tumors in our hospital.The data set was divided into model training group and validation testing group with a 75/25 split.There were two categories for ALN tumor burden: low (1-2 metastatic ALNs) and high (≥3 metastatic ALNs). The PyRadiomics package was used to obtain radiomic features (RF), and a support vector machine (SVM) with the LASSO approach was used to create a radiomic signature (RS).The training group's multivariate logistic regression results were used to create a nomogram that combined the BC US radiomics score with a clinical parameter.Additionally, the area under the operating characteristic curve (AUC) was used to evaluate their prediction performance. Results: With an AUC of 0.920 (95% CI: 0.901, 0.943) in the test cohort, clinical parameter coupled RS provides the greatest diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastases.In the testing cohort, the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were 90%, 82%, 83%, 89%, and 86%, respectively. With an AUC of 0.939 (95% CI: 0.892, 0.970) in the test cohort, this clinical measure paired with RS can also distinguish between a low and a substantial metastatic burden of axillary illness. Conclusions: For patients with early-stage BC, our work provides a noninvasive imaging biomarker to forecast the extent of ALN metastases.The imaging biomarker demonstrated strong predictive value and the potential for extended application to customize surgical care.
Keywords: axillary lymphnode metastasis (ALNM), breast cancer (BC), Clinical parameters, clinical stages T1–2, Machine Learning(ML), Radiomics
Received: 05 Aug 2025; Accepted: 10 Dec 2025.
Copyright: © 2025 Hong, Peng, Liang, Zeng, Yang and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Song Song Wu
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