Shear wave elastography as a supplemental tool in the assessment of unsuspicious axillary lymph nodes in patients undergoing breast ultrasound examination.

OBJECTIVES: To define reference values for shear wave elastography (SWE) in unsuspicious axillary lymph nodes in patients undergoing breast ultrasound examination. METHODS: In total, 177 clinically and sonographically unsuspicious axillary lymph nodes were prospectively evaluated with SWE using Virtual Touch Tissue Imaging Quantification (VTIQ) in 175 women. Mean values of tissue stiffness for axillary fatty tissue, lymph node cortex, and lymph node hilus were measured. Additionally, test-retest reliability of SWE in the assessment of axillary lymph node stiffness was evaluated by repeating each measurement three times. RESULTS: In 177 axillary lymph nodes, the mean stiffness of lymph node cortex, hilus, and surrounding fatty tissue as quantified by SWE was 1.90 m/s (SD: 0.34 m/s), 2.02 m/s (SD: 0.37 m/s), and 1.75 m/s (SD: 0.38 m/s), respectively. The mean stiffness of cortex and hilus was significantly higher compared to fatty tissue (p < 0.0001). SWE demonstrated good test-retest reliability in the assessment of stiffness of the lymph node hilus, cortex, and the surrounding fatty tissue with an intraclass correlation of 0.79 (95% CI: 0.75; 0.83), 0.75 (95% CI: 0.70; 0.79), and 0.78 (95% CI: 0.74; 0.82), respectively, (p < 0.0001). CONCLUSIONS: Reference values for SWE in unsuspicious axillary lymph nodes are determined. These results may help to better identify axillary lymph node metastasis for breast cancer patients when combined with other lymph node features. SWE is a reliable method for the objective quantification of tissue stiffness of axillary lymph nodes. ADVANCES IN KNOWLEDGE: This study presents physiological reference values for tissue stiffness by examining the axillary lymph nodes with SWE in 175 women with sonomorphologically unsuspicious lymph nodes.

LECANDUS study (LEsion CANdidate Detection in UltraSound Data): evaluation of image analysis algorithms for breast lesion detection in volume ultrasound data.

PURPOSE: This study aims at developing and evaluating a prototype of a lesion candidate detection algorithm for a 3D-US computer-aided diagnosis (CAD) system. METHODS: Additionally, to routine imaging, automated breast volume scans (ABVS) were performed on 63 patients. All ABVS exams were analyzed and annotated before the evaluation with different algorithm blob detectors characterized by different blob-radiuses, voxel-sizes and the quantiles of blob filter responses to find lesion candidates. Lesions found in candidates were compared to the prior annotations. RESULTS: All histologically proven lesions were detected with at least one algorithm. The algorithm with optimal sensitivity detected all cancers (sensitivity = 100 %) with a very low positive predictive value due to a high false-positive rate. CONCLUSIONS: ABVS is a new technology which can be analyzed by a CAD software. Using different algorithms, lesions can be detected with a very high and accurate sensitivity. Further research for feature extraction and lesion classification is needed aiming at reducing the false-positive hits.