Editorial for “Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T1‐Weighted Imaging: Comparison of Different Radiomics Models”
Abstract
Liver fibrosis is one of the major causes of mortality in patients with chronic liver diseases. Fibrotic liver injury activates the hepatic stellate cells and leads to synthesis of excess interstitial collagens and other matrix components. Accurately assessing the extent and progression of fibrosis is important for clinical management of chronic liver diseases. Magnetic resonance image (MRI) techniques, such as diffusion-weighted imaging (DWI), and perfusion-weighted imaging with contrast medium, have shown promising results in staging liver fibrosis. Recently, some researchers reported that T1-weighted (T1w) imaging could be used for liver fibrosis staging. However, unlike apparent diffusion coefficient (ADC) measurement, T1w images are usually used for qualitative visual assessment. Radiomics, which extracts a large number of quantitative features from radiographic medical images, such as statistical metrics and textual features, could provide a powerful tool for quantitative analysis of T1w images. Recently, some studies have shown promising results in applying radiomics methods in staging liver fibrosis and characterizing hepatocellular carcinoma. However, there is no agreement on how to properly select radiomics features and classifiers for specific applications. In this issue of JMRI, an article published by Ni et al investigates how radiomics feature and classifier selection affect the T1w imaging-based liver fibrosis staging. 9 This well-designed study compares two…
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