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Updated: May 27, 2025
Application of Ultrasound and Shear Wave Elastography Imaging in a Rat Model of NAFLD/NASH
Published on: April 20, 2021
Sequential ultrasound molecular imaging for noninvasive identification and assessment of non-alcoholic
Tingting Sha1,2, Yujia You1, Xiaoyan Miao1
1Department of Medical Ultrasound, Laboratory of Novel Optoacoustic (Ultrasonic) Imaging, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Background And Objective:
Noninvasive non-alcoholic steatohepatitis (NASH) assessment is a clinical challenge to the management of non-alcoholic fatty liver disease. We aim to develop diagnostic models based on sequential ultrasound molecular imaging (USMI) for the noninvasive identification of NASH in mouse models.
Methods:
Animal experiments were approved by the Animal Ethics Committee of South China Agricultural University. Forty-nine C57BL/6 mice were divided into normal control, non-alcoholic fatty liver, NASH, and hepatitis groups. Sequential USMI was implemented using CD36-targeted microbubbles (MBs-CD36) and intercellular adhesion molecule-1 (ICAM-1)-targeted microbubbles (MBs-ICAM-1) to visualize hepatic steatosis and inflammation. The targeting signal of USMI was quantified as the normalized intensity difference (NID) with the destruction-replenishment method. Correlation analysis was conducted between the NID-MBs-CD36 and pathological steatosis score and between the NID-MBs-ICAM-1 and pathological inflammation score. Finally, diagnostic models combining NID-MBs-CD36 with NID-MBs-ICAM-1 were established for NASH diagnosis.
Results:
MBs-CD36 and MBs-ICAM-1 were successfully prepared and used for sequential USMI in all mice. NID-MBs-CD36 values increased with the progression of steatosis, while NID-MBs-ICAM-1 values increased in parallel with the progression of inflammation. A strong positive correlation was identified between NID-MBs-CD36 and pathological steatosis grade (rs = 0.9078, P < 0.0001) and between NID-MBs-ICAM-1 and pathological inflammation grade (rs = 0.9071, P < 0.0001). Among various sequential USMI-based diagnostic models, the serial testing model showed high diagnostic performance in detecting NASH, with 95% sensitivity, 97% specificity, 95% positive predictive values, 97% negative predictive values, and 96% accuracy.
Conclusions:
Sequential USMI using MBs-CD36 and MBs-ICAM-1 allows noninvasive grading of hepatic steatosis and inflammation. Sequential USMI-based diagnostic models hold great potential in the noninvasive identification of NASH.
