Comparison of Quantitative High-definition Microvessel Imaging and Shear Wave Elastography in Preoperative Prediction of Axillary Lymph Node Breast Cancer Metastasis

Abstract

Background Accurate assessment of axillary lymph nodes (ALNs) is a critical step for timely diagnosis of metastasis and proper subsequent intervention in breast cancer patients. Herein, we compare the diagnostic utility of quantitative high-definition microvasculature imaging (qHDMI), shear wave elastography (SWE) and their combination for differentiation of metastatic ALNs from reactive. Methods A total of 85 female patients with suspicious ALNs recommended for fine needle aspiration biopsy (FNAB) were included in the study, and the pathology results were used as the gold standard for labeling the status of each ALN. Three SWE metrics and ten qHDMI-derived biomarkers were used in our analyses. Additionally, age, as well as clinical ultrasound features such as nodal size and cortical thickness were included as clinical factors. The Wilcoxon rank-sum test was utilized to analyze distributional differences in biomarkers between metastatic and reactive ALNs. Multiple elastic-net logistic regression models were developed based on varying combinations of clinical, qHDMI, and SWE feature sets. A 70%/30% train/test split was adopted, and ROC curve analyses were performed to evaluate and compare classification performance. Moreover, distributional differences in qHDMI and SWE biomarkers between ALNs corresponding to breast cancer immunohistochemical subtypes luminal A and B were investigated. Results Of the total of 85 ALNs included in the analysis, 42 were metastatic. Statistically significant (p-value < 0.05) differences were observed in all but one of the qHDMI biomarkers, as well as all the SWE metrics. Test-set discrimination defined by area under ROC curve (AUC) was low for the model using only clinical features (0.62; 95% CI = [0.39,0.84]), with higher performance observed for models using SWE only (0.93; [0.82,1.00]), qHDMI only (0.97; [0.91,1.00]), qHDMI-SWE (0.97; [0.92,1.00]), and qHDMI-SWE plus clinical biomarkers (0.98; [0.94,1.00]). No statistically significant improvements were seen in the combined SWE-qHDMI and SWE-qHDMI-C classification models relative to the qHDMI-only model, although power for comparison was limited. Four qHDMI biomarkers and two SWE measures exhibited statistically significant distributions among breast cancer luminal A and B subtypes. Conclusions qHDMI classification model was able to separate metastatic from reactive ALNs with high accuracy. qHDMI, SWE, and the combined models had improved classification performance over the baseline Clinical model. qHDMI biomarkers can be valuable in determining the malignancy status of suspicious ALNs, providing helpful information regarding breast cancer prognosis.