Dear Editor,
We read the publication “Development and Validation of a Simple Index Based on Non-Enhanced CT and Clinical Factors for Prediction of Non-Alcoholic Fatty Liver Disease” about building a prediction model for the evaluation of non-alcoholic fatty liver disease (NAFLD) with great interest. In that study, the diagnostic efficacy of the clinical-CT index was superior than that of CTL-S or the clinical indices (all p < 0.05) based on the pathologic diagnosis of NAFLD as the reference standard (1). This indicates that the clinical-CT index is more accurate than other indices and may have potential clinical application in the evaluation of NAFLD. We would like to share our opinions concerning noninvasive imaging for the evaluation of NAFLD spectrum.
NAFLD is a complicated hepatic disease spectrum, which includes NAFL, borderline non-alcoholic steatohepatitis (borderline NASH), and NASH. Additionally, NAFLD related fibrosis can be detected in borderline NASH or NASH liver, and hepatic steatosis is one of the pathologic characteristics in NAFLD livers. Therefore, different image modalities may have different diagnostic efficacy in the evaluation of NAFLD spectrum. Our previous animal study (2) suggested that multislice computed tomography (MSCT) was better able to differentiate normal or early NAFLD livers from higher severity NAFLD livers compared with ultrasound shear wave elastography (US-SWE). This means that MSCT could be recommended to evaluate or monitor the early stage of NAFLD livers. However, the diagnostic efficacy of US-SWE is superior to MSCT in differentiating NASH from normal or less severe NAFLD. Hence, US-SWE could be recommended for the assessment or follow-up of the dynamic changes in advanced stage NAFLD livers. Thus, the diagnostic efficacies of these two techniques supplement each other in the assessment of NAFLD spectrum (2). In addition, as for hepatic steatosis, the modified Dixon MRI technique is a good method for histological quantification in the assessment of hepatic steatosis (3), as well as intravoxel incoherent motion diffusion-weighted MR imaging for the evaluation of NAFLD spectrum (4).
The present characteristic signs on imaging modalities (e.g., stiffness, attenuation, and signal intensity) do not provide enough diagnostic information to assess or differentiate between the severity of NAFLD livers. Consequently, radiomics and deep learning may be a better way to evaluate NAFLD severity in the near future. Recently, several studies have suggested that it is feasible to use a deep learning method to evaluate the grade of fatty livers based on ultrasonography (5). Deep learning radiomics have a superior diagnostic performance for predicting liver fibrosis severity than 2D-SWE and biomarker evaluation (6).
Noninvasive imaging play an important role in the diagnosis of NAFLD spectrum. Combining multimodality imaging technologies or clinical factors, as well as radiomics and deep learning based on medical images, can improve the diagnostic accuracy of NAFLD spectrum. However, because NAFLD is complicated, the precise diagnostic accuracy in the clinical practice still requires large-scale, long-term research. Deep learning radiomics of multimodality imaging may be a novel way to improve the diagnostic efficacy of NAFLD spectrum in the future.
Footnotes
This work was supported by the National Natural Science Foundation of China (Grants nos. 81960338, and 81760312).
Conflicts of Interest: The authors have no potential conflicts of interest to disclose.
References
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