Objectives Sentinel lymph node (SLN) biopsy remains the standard for axillary staging in early-stage breast cancer, though ongoing clinical investigations are exploring the omission of axillary procedures in specific subgroups. This study assessed whether axillary ultrasonography (US) and MRI can predict SLN involvement and developed a predictive tool to identify patients who may safely forgo axillary surgery. Materials and methods We retrospectively analyzed 8114 patients with cT1–T2N0 invasive breast cancer across three cancer centers in China. All patients underwent preoperative axillary US and/or MRI. Multivariate logistic regression identified independent predictors of SLN metastases, which were used to construct a predictive model. The model was validated using a 70:30 training-validation split and visualized through a nomogram. Subgroup analyses evaluated the risk of SLN involvement among patients with negative imaging findings. Results SLN metastases were observed in 2545 patients (31.37%), with clinical T2 stage, lymphovascular invasion, Ki-67 ≥ 20%, ER + /HER2− subtype, and positive findings on US or MRI independently associated with SLN involvement (p < 0.001). Among 2282 patients with negative US and MRI findings, the SLN metastasis rate was 16.39%. The multivariable predictive model integrating imaging findings with clinicopathologic variables demonstrated good performance, with an AUC of 0.775 (95% CI: 0.750–0.801) in the training set and 0.759 (95% CI: 0.740–0.778) in the validation set. Notably, omission of axillary surgery would miss nodal metastases in 17.4% of patients eligible for CDK4/6 inhibitors. Conclusion Preoperative US and MRI are valuable for identifying low-risk patients. The prediction model may help select early-stage breast cancer patients for whom axillary surgery can be safely omitted while minimizing undertreatment risks for adjuvant therapies. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Can preoperative axillary ultrasound and MRI reliably stratify lymph node metastasis risk in patients with early-stage breast cancer? <Emphasis Type="BoldItalic">Findings</Emphasis> A multivariate nomogram developed from 8,114 patients showed consistent predictive accuracy for axillary metastasis (AUC: 0.775 in training; 0.759 in validation cohort). <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> This model supports individualized axillary management by identifying patients who may safely avoid axillary surgery while preserving accurate nodal risk stratification for adjuvant systemic therapy and radiotherapy decision-making. Graphical Abstract

Prediction model to prevent axillary surgery using axillary US and MRI in early breast cancer

期刊论文
发表日期：

2026年4月2日

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Haitong Xie,
Xin Wang,
Wanying Guo,
Yunhao Wu,
Ruixian Chen,
Xin Zhao,
Juan Huang,
Heqing Zhang,
Chihua Wu,
Jie Chen

摘要

Objectives

Sentinel lymph node (SLN) biopsy remains the standard for axillary staging in early-stage breast cancer, though ongoing clinical investigations are exploring the omission of axillary procedures in specific subgroups. This study assessed whether axillary ultrasonography (US) and MRI can predict SLN involvement and developed a predictive tool to identify patients who may safely forgo axillary surgery.

Materials and methods

We retrospectively analyzed 8114 patients with cT1–T2N0 invasive breast cancer across three cancer centers in China. All patients underwent preoperative axillary US and/or MRI. Multivariate logistic regression identified independent predictors of SLN metastases, which were used to construct a predictive model. The model was validated using a 70:30 training-validation split and visualized through a nomogram. Subgroup analyses evaluated the risk of SLN involvement among patients with negative imaging findings.

Results

SLN metastases were observed in 2545 patients (31.37%), with clinical T2 stage, lymphovascular invasion, Ki-67 ≥ 20%, ER + /HER2− subtype, and positive findings on US or MRI independently associated with SLN involvement (p < 0.001). Among 2282 patients with negative US and MRI findings, the SLN metastasis rate was 16.39%. The multivariable predictive model integrating imaging findings with clinicopathologic variables demonstrated good performance, with an AUC of 0.775 (95% CI: 0.750–0.801) in the training set and 0.759 (95% CI: 0.740–0.778) in the validation set. Notably, omission of axillary surgery would miss nodal metastases in 17.4% of patients eligible for CDK4/6 inhibitors.

Conclusion

Preoperative US and MRI are valuable for identifying low-risk patients. The prediction model may help select early-stage breast cancer patients for whom axillary surgery can be safely omitted while minimizing undertreatment risks for adjuvant therapies.

Key Points

Question Can preoperative axillary ultrasound and MRI reliably stratify lymph node metastasis risk in patients with early-stage breast cancer?

Findings A multivariate nomogram developed from 8,114 patients showed consistent predictive accuracy for axillary metastasis (AUC: 0.775 in training; 0.759 in validation cohort).

Clinical relevance This model supports individualized axillary management by identifying patients who may safely avoid axillary surgery while preserving accurate nodal risk stratification for adjuvant systemic therapy and radiotherapy decision-making.

Graphical Abstract