Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis.

ABSTRACT:

Objective

To assess significant liver fibrosis by multiparametric ultrasomics data using machine learning.

Materials and methods

This prospective study consisted of 144 patients with chronic hepatitis B. Ultrasomics-high-throughput quantitative data from ultrasound imaging of liver fibrosis-were generated using conventional radiomics, original radiofrequency (ORF) and contrast-enhanced micro-flow (CEMF) features. Three categories of features were explored using pairwise correlation and hierarchical clustering. Features were selected using diagnostic tests for fibrosis, activity and steatosis stage, with the histopathological results as the reference. The fibrosis staging performance of ultrasomics models with combinations of the selected features was evaluated with machine-l

SUBMITTER: Li W 

PROVIDER: S-EPMC6510867 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature