First step to facilitate long-term and multi-centre studies of shear wave elastography in solid breast lesions using a computer-assisted algorithm

• Published in: International journal for computer assisted radiology and surgery Vol. 12; no. 9; pp. 1533 - 1542
• Main Authors: Skerl, Katrin, Cochran, Sandy, Evans, Andrew
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2017; Springer Nature B.V
• Subjects: Algorithms; Biopsy; Breast; Computer aided testing; Computer Imaging; Computer Science; Data transfer (computers); Diagnostic systems; Elasticity; Health Informatics; Imaging; Lesions; Mammography; Medical imaging; Medicine; Medicine & Public Health; Original; Original Article; Parameter estimation; Pattern Recognition and Graphics; Radiology; Surgery; Vision; Data assessment; Shear wave elastography; Breast cancer; Diagnosis; Ultrasound; Computer-aided diagnosis
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-017-1596-3

Purpose Shear wave elastography (SWE) visualises the elasticity of tissue. As malignant tissue is generally stiffer than benign tissue, SWE is helpful to diagnose solid breast lesions. Until now, quantitative measurements of elasticity parameters have been possible only, while the images were still saved on the ultrasound imaging device. This work aims to overcome this issue and introduces an algorithm allowing fast offline evaluation of SWE images. Methods The algorithm was applied to a commercial phantom comprising three lesions of various elasticities and 207 in vivo solid breast lesions. All images were saved in DICOM, JPG and QDE (quantitative data export; for research only) format and evaluated according to our clinical routine using a computer-aided diagnosis algorithm. The results were compared to the manual evaluation (experienced radiologist and trained engineer) regarding their numerical discrepancies and their diagnostic performance using ROC and ICC analysis. Results ICCs of the elasticity parameters in all formats were nearly perfect (0.861–0.990). AUC for all formats was nearly identical for E max and E mean (0.863–0.888). The diagnostic performance of SD using DICOM or JPG estimations was lower than the manual or QDE estimation (AUC 0.673 vs. 0.844). Conclusions The algorithm introduced in this study is suitable for the estimation of the elasticity parameters offline from the ultrasound system to include images taken at different times and sites. This facilitates the performance of long-term and multi-centre studies.