Ultrasound radiomics predicts preoperative axillary lymph node metastasis status in early-stage breast cancer to support surgical decisions: a machine learning, monocenter study

TL;DR

This study uses ultrasound radiomics and machine learning to predict axillary lymph node metastasis in early-stage breast cancer, offering a non-invasive alternative to guide surgical decisions.

Contribution

A novel non-invasive ultrasound radiomics method combined with clinical parameters to predict axillary lymph node metastasis in breast cancer.

Findings

The combined radiomic signature and clinical parameters achieved an AUC of 0.920 in predicting axillary metastasis.

The model could distinguish between low and high metastatic burden with an AUC of 0.939.

The method showed high sensitivity (90%) and specificity (82%) in test data.

Abstract

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.

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.

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.

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.

Figures