Abstract
BACKGROUND: Accurate preoperative assessment of axillary lymph node (ALN) status is crucial for breast cancer staging and subsequent clinical decision-making. Consequently, identifying a reliable noninvasive method for evaluating ALN status before surgery remains a key clinical priority. This study aimed to evaluate the value of the new proposed qualitative shear wave elastography (SWE) patterns in the differentiation of suspicious ALNs observed on B-mode ultrasound (BMUS). METHODS: A retrospective multicenter study was performed on patients with breast cancer with suspicious axillary nodes on BMUS from May 2022 to June 2025. BMUS characteristics [cortical thickness, absence of fatty hilum, and longitudinal-to-transverse ratio (L/T) <2], vascularity distribution, quantitative SWE parameters [maximum value (Emax), mean value (Emean), minimal value (Emin), standard deviation (SD), and elasticity ratio (Eratio)], and the proposed qualitative SWE patterns of ALN status were evaluated. The diagnostic performance of BMUS characteristics, vascular distribution, quantitative SWE parameters, and the newly qualitative SWE patterns were compared with that of ALN pathological status, with either ALN dissection (ALND) or sentinel lymph node biopsy (SLNB) serving as the reference standard. RESULTS: A total of 144 ALNs in 144 patients with breast cancer were examined in this study. ALND was performed in 52 (36.1%) of patients, while 92 (63.9%) underwent SLNB. The optimal cutoff value of Emin, Emean, Emax, SD, and Eratio were 4.35 KPa, 17.30 KPa, 41.55 KPa, 1.95 KPa, and 2.25, respectively. Compared with the BMUS characteristics, vascularity distribution, and quantitative SWE parameters, the newly qualitative SWE patterns obtained the highest diagnostic performance, with an area under the curve of 0.972, a sensitivity of 96.72%, a specificity of 97.59%, and an accuracy of 97.22%. CONCLUSIONS: The novel qualitative SWE patterns could achieve reliable diagnostic discrimination for benign and metastatic BMUS-suspicious ALNs. They may thus be able to optimize surgical planning and reduce overtreatment, improving the quality of life of patients treated with breast cancer.