Xin Lu1, Jiahui Li1, Zheng Zhu1, Alina Allen2, Taofic Mounajjed3, Kevin J Glaser1, Jinhang Gao 4, Jingbiao Chen1, Jie Chen1, Safa Hoodeshenas1, Armando Manduca1, Richard L Ehman1, and Meng Yin1
1Department of Radiology, Mayo Clinic, Rochester, MN, United States, 2Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States, 3Anatomic Pathology, Mayo Clinic, Rochester, MN, United States, 4Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
1Department of Radiology, Mayo Clinic, Rochester, MN, United States, 2Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States, 3Anatomic Pathology, Mayo Clinic, Rochester, MN, United States, 4Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
A streamlined liver imaging protocol can be established for NASH
diagnosis with a bi-parametric model using fat fraction and liver stiffness. No
significant improvement was found in diagnostic performance when adding T1 in
the predictive model.
Figure 2. Scatter plots
of four imaging parameters for different pathohistologic outcomes: NASH
diagnosis, steatosis, inflammation, cellular injury, and fibrosis.
*:P-value<0.05 is considered statistically significant.
Figure 4. Nominal
logistic regression with effect likelihood and odds ratio tests for three
imaging parameters. *:P-value<0.05 is considered statistically significant.
