A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors

Abstract

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.

Keywords: Liver fibrosis; Machine learning; Shear wave elastography; Ultrasound.

Figure 1.

Flow diagram of the enrollment process in this retrospective study.

Figure 2.

Ultrasound elastography images of the liver in segment 8 (transverse plane) obtained in the two different groups. a. Point shear wave elastography (pSWE) on a Siemens scanner in group 1 (51-year old female patient with abnormal liver function studies); b. 2-dimensional shear wave elastography (2DSWE) on a Philips scanner in group 2 (42-year old male patient with chronic hepatitis B).

Figure 3.

The proposed multi-model framework for machine learning (ML) based fibrosis staging. This approach will provide a fibrosis staging between 0 to 100 regardless of vendor. In this work we only tested ultrasound elastography shear wave velocity (USE SWV) measurements obtained using Siemens and Philips scanners, with magnetic resonance elastography (MRE) as ground truth. However, in the future this model could be extended to other vendors after additional training and validation on those datasets.

Figure 4.

Receiver operating characteristic (ROC) curves compare the performance of each machine learning (ML) algorithm and the baseline technique using median shear wave velocity to predict clinically non-significant versus significant liver fibrosis, as determined by MRE as gold standard. Support vector machines (blue) had the highest performance of all ML algorithms in both groups.

Figure 5.

Scores for non-significant and significant fibrosis separation using median shear wave velocity (SWV) as well as the new machine learning (ML) algorithms in dataset 1 (a. pSWE), and dataset 2 (b. 2DSWE). The different scores reflect the likelihood that the label came from each class (non-significant or significant fibrosis). Boxplots show excellent score separation in both datasets when a support vector machine (SVM) is used to perform classification, compared to worse score separation with median SWV. Note that ML scores differ between systems from different vendors as well as for the different ML algorithms. MRE = magnetic resonance elastography; GLRM = generalized linear regression model; QDA = quadratic discriminant analysis. The ends of the box are the upper and lower quartiles; the vertical line inside the box represents the median; and the whiskers extend to the highest and lowest values.