Added Value of Shear Wave Elastography for Noninvasive Detection of Axillary Lymph Node Metastasis and IHC-Defined Surrogate Subtype Characterization in Breast Cancer
- 1Department of Radiology, Mayo Clinic College of Medicine, 200 1st Street SW, Rochester, MN 55902, USA
- 2Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, MN 55902, USA
- 3Department of Quantitative Health Science, Mayo Clinic College of Medicine, Rochester, MN 55902, USA
Abstract
Background: Accurate preoperative characterization of axillary lymph nodes (ALNs) is important in breast cancer, but conventional ultrasound has variable diagnostic performance. We evaluated quantitative shear wave elastography (SWE) for distinguishing reactive from metastatic ALNs, and we explored associations between nodal SWE parameters, breast cancer subtypes, and histological grades. Methods: We prospectively enrolled women with suspicious breast lesions and ALNs identified by clinical ultrasound who were scheduled for biopsy. After exclusions, 141 patients (57 reactive and 84 metastatic ALNs) were included. ElasticNet-regularized logistic regression with nested cross-validation compared clinical ultrasound alone with ultrasound plus SWE. Among 70 patients with metastatic ALNs and immunohistochemical characterization, SWE parameters were compared across surrogate subtypes and histological grades using Kruskal–Wallis tests with Holm adjustments. Results: The combined ultrasound–SWE model achieved an area under the receiver operating characteristic curve of 0.911 (95% CI, 0.859–0.961) compared to 0.798 (95% CI, 0.723–0.873) for ultrasound alone. At the Youden-derived threshold, the combined model achieved 88.1% sensitivity, 82.5% specificity, and 85.8% accuracy. Among metastatic ALNs, SWE parameters, namely Vmean, Vsd, and Vmax, differed significantly across surrogate subtypes, with the most pronounced differences observed between Luminal A and Luminal B tumors, (p = 0.0250, p = 0.0069, and p = 0.0065). No statistically significant differences were detected with histological grades in the five SWE parameters. Conclusions: Quantitative SWE complemented conventional ultrasound for assessing axillary nodal metastasis, with higher cross-validated discrimination for the combined model. Exploratory analyses identified differences in several SWE stiffness metrics between Luminal A and Luminal B tumors; these subtype-associated biomechanical differences require validation in larger cohorts.
Related
- In vivo characterization of the fascia lata by shear wave elastography
- Analytical and clinical validation of the EdenDx methylation assay as a noninvasive supportive triage tool for early detection of endometrial cancer in cervical scraping specimens
- Multi-modal AI: Integrating electrocardiography with molecular biomarkers for noninvasive detection of cardiac allograft rejection
- Scalp EEG and Intrinsic Optical Signal Imaging for Noninvasive Detection of Spreading Depolarizations in the Rat Cortex
- Noninvasive Detection and Subtyping of Esophageal Cancer by Circulating Cell-Free mtDNA Fragmentomics: A Multi-Center Validation Study.
BibTeX
@article{chen2026added,
title = {{Added Value of Shear Wave Elastography for Noninvasive Detection of Axillary Lymph Node Metastasis and IHC-Defined Surrogate Subtype Characterization in Breast Cancer}},
author = {Jie Chen and David P. Rosen and Tanin Adl Parvar and Robert T. Fazzio and Nicholas B. Larson and Mostafa Fatemi and Azra Alizad},
journal = {Cancers},
year = {2026},
month = nov,
doi = {10.3390/cancers18213549},
url = {https://www.mdpi.com/2072-6694/18/21/3549},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Added Value of Shear Wave Elastography for Noninvasive Detection of Axillary Lymph Node Metastasis and IHC-Defined Surrogate Subtype Characterization in Breast Cancer
AU - Jie Chen
AU - David P. Rosen
AU - Tanin Adl Parvar
AU - Robert T. Fazzio
AU - Nicholas B. Larson
AU - Mostafa Fatemi
AU - Azra Alizad
PY - 2026
DA - 2026/11/04
JO - Cancers
DO - 10.3390/cancers18213549
UR - https://www.mdpi.com/2072-6694/18/21/3549
AB - Background: Accurate preoperative characterization of axillary lymph nodes (ALNs) is important in breast cancer, but conventional ultrasound has variable diagnostic performance. We evaluated quantitative shear wave elastography (SWE) for distinguishing reactive from metastatic ALNs, and we explored associations between nodal SWE parameters, breast cancer subtypes, and histological grades. Methods: We prospectively enrolled women with suspicious breast lesions and ALNs identified by clinical ultrasound who were scheduled for biopsy. After exclusions, 141 patients (57 reactive and 84 metastatic ALNs) were included. ElasticNet-regularized logistic regression with nested cross-validation compared clinical ultrasound alone with ultrasound plus SWE. Among 70 patients with metastatic ALNs and immunohistochemical characterization, SWE parameters were compared across surrogate subtypes and histological grades using Kruskal–Wallis tests with Holm adjustments. Results: The combined ultrasound–SWE model achieved an area under the receiver operating characteristic curve of 0.911 (95% CI, 0.859–0.961) compared to 0.798 (95% CI, 0.723–0.873) for ultrasound alone. At the Youden-derived threshold, the combined model achieved 88.1% sensitivity, 82.5% specificity, and 85.8% accuracy. Among metastatic ALNs, SWE parameters, namely Vmean, Vsd, and Vmax, differed significantly across surrogate subtypes, with the most pronounced differences observed between Luminal A and Luminal B tumors, (p = 0.0250, p = 0.0069, and p = 0.0065). No statistically significant differences were detected with histological grades in the five SWE parameters. Conclusions: Quantitative SWE complemented conventional ultrasound for assessing axillary nodal metastasis, with higher cross-validated discrimination for the combined model. Exploratory analyses identified differences in several SWE stiffness metrics between Luminal A and Luminal B tumors; these subtype-associated biomechanical differences require validation in larger cohorts.
SN - 2072-6694
ER -