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Updated: Jan 8, 2026
Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
Published on: February 6, 2020
Integration of multi-imaging technique and immunohistochemistry for the development of a predictive nomogram for
Jinxiao Chen1, Mengyao Zhao2, Zeyang Dong2
1Department of Ultrasound, Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, Lishui, Zhejiang, China.
Background:
Axillary lymph node metastasis (ALNM) significantly influences staging, treatment, and prognosis in breast cancer. Current assessment relies on invasive procedures such as sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND), which carry substantial morbidity. A reliable, noninvasive method for preoperative ALNM prediction is urgently needed.
Methods:
This retrospective study included 528 female breast cancer patients without distant metastasis treated at Lishui People's Hospital between February 2019 and February 2024. Preoperative ultrasound (US) and magnetic resonance imaging (MR) features of primary tumors and axillary lymph nodes, along with immunohistochemistry (IHC) markers, were analyzed. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of ALNM, and a nomogram was constructed. Model performance was assessed using receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA), and five-fold cross-validation.
Results:
Multivariate analysis identified six independent predictors of ALNM: SWE min (≤7.16 kPa), microcalcification, suspicious lymph nodes on US and MR, ADC value (≤0.955 × 10-³ mm²/s), and HER-2 positivity (all P < 0.05). The nomogram incorporating these variables achieved area under the ROC curve (AUC) values of 0.854 in the training cohort and 0.826 in the validation cohort. Calibration curves showed good agreement between predicted and observed probabilities. DCA demonstrated favorable net clinical benefit, and five-fold cross-validation confirmed the model's stability with a mean AUC of 0.812.
Conclusions:
A nomogram integrating multimodal imaging features and IHC markers provides a noninvasive, accurate tool for preoperative prediction of ALNM in breast cancer. This model may assist in individualized surgical planning and reduce unnecessary axillary interventions. Further validation in multicenter prospective studies is warranted.
