Characterization of soft tissue tumours with ultrasound, shear wave elastography and MRI

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Abstract

OBJECTIVE: To predict accurately whether a soft tissue mass was benign or malignant and to characterize its type using ultrasound, shear wave elastography and MRI. We hypothesized that with the addition of shear wave elastography, it would be possible to determine a threshold velocity value to classify a lesion as benign or malignant. MATERIALS AND METHODS: A total of 151 consecutive, consenting adult patients were prospectively recruited to this study in a tertiary referral musculoskeletal oncology centre. All lesions were assessed with ultrasound, including B mode, Doppler and shear wave elastography measurements. One hundred thirty-eight patients also underwent MRI of the lesion. A histological diagnosis was obtained for all lesions. RESULTS: Malignant lesions were larger than benign lesions and had a greater Doppler activity. There was no useful threshold shear wave velocity to differentiate between benign and malignant lesions. Longitudinal and transverse shear wave velocities were strongly positively correlated with each other. An inverse correlation was shown with lesion size and depth, regardless of whether it was benign or malignant. A logistic regression model combining the ultrasound and MRI characteristics did not confidently classify a lesion as benign or malignant and was inferior to expert opinion. CONCLUSION: The strongest predictors of malignancy are large lesion size and high vascularity. The combination of all ultrasound characteristics (including shear wave elastography) and MRI features does not confidently classify a lesion as benign or malignant, and histological diagnosis remains the gold standard.

• Acceptance Date: Dec 9, 2019
• Publication Date: Jan 2, 2020
• Journal: Skeletal Radiology
• Print ISSN: 0364-2348
• Publisher: Springer Verlag
• Pages: 1-13
• DOI: https://doi.org/10.1007/s00256-019-03363-1
• Keywords: soft tissue tumors, ultrasound, shear wave elastography, MRI.
• Publisher URL: https://link.springer.com/article/10.1007%2Fs00256-019-03363-1