Publication Type:
Journal ArticleSource:
Ultrasound Med Biol, Volume 46, Number 1, p.26-33 (2020)ISBN:
1879-291X (Electronic)<br/>0301-5629 (Linking)Accession Number:
31611074URL:
https://web.stanford.edu/group/rubinlab/pubs/1-s2.0-S0301562919315042-main.pdfKeywords:
Liver fibrosis, machine learning, Shear wave elastography, UltrasoundAbstract:
The purpose of the work described here was to determine if the diagnostic performance of point and 2-D shear wave elastography (pSWE; 2-DSWE) using shear wave velocity (SWV) with a new machine learning (ML) technique applied to systems from different vendors is comparable to that of magnetic resonance elastography (MRE) in distinguishing non-significant (<F2) from significant (>/=F2) fibrosis. We included two patient groups with liver disease: (i) 144 patients undergoing pSWE (Siemens) and MRE; and (ii) 60 patients undergoing 2-DSWE (Philips) and MRE. Four ML algorithms using 10 SWV measurements as inputs were trained with MRE. Results were validated using twofold cross-validation. The performance of median SWV in binary grading of fibrosis was moderate for pSWE (area under the curve [AUC]: 0.76) and 2-DSWE (0.84); the ML algorithm support vector machine (SVM) performed particularly well (pSWE: 0.96, 2-DSWE: 0.99). The results suggest that the multivendor ML-based algorithm SVM can binarily grade liver fibrosis using ultrasound elastography with excellent diagnostic performance, comparable to that of MRE.