Combination of hepatocyte fraction and diffusion-weighted imaging as a predictor in quantitative hepatic fibrosis evaluation

Liver T1/T2 values with cardiac MRI during respiration

BACKGROUND

Assessing the hepatic status of children with CHD is very important in the post-operative period. This study aimed to assess the usefulness of paediatric liver T1/T2 values and to evaluate the impact of respiration on liver T1/T2 values.

METHODS

Liver T1/T2 values were evaluated in 69 individuals who underwent cardiac MRI. The mean age of the participants was 16.2 ± 9.8 years. Two types of imaging with different breathing methods were possible in 34 participants for liver T1 values and 10 participants for liver T2 values.

RESULTS

The normal range was set at 620-830 msec for liver T1 and 25-40 ms for liver T2 based on the data obtained from 17 healthy individuals. The liver T1/T2 values were not significantly different between breath-hold and free-breath imaging (T1: 769.4 ± 102.8 ms versus 763.2 ± 93.9 ms; p = 0.148, T2: 34.9 ± 4.0 ms versus 33.6 ± 2.4 ms; p = 0.169). Higher liver T1 values were observed in patients who had undergone Fontan operation, tetralogy of Fallot operation, or those with chronic viral hepatitis. There was a trend toward correlation between liver T1 values and liver stiffness (R = 0.65, p = 0.0004); and the liver T1 values showed a positive correlation with the shear wave velocity (R = 0.62, p = 0.0006).

CONCLUSIONS

Liver T1/T2 values were not affected by breathing patterns. Because liver T1 values tend to increase with right heart overload, evaluation of liver T1 values during routine cardiac MRI may enable early detection of future complications.

Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques

Liver fibrosis features excessive protein accumulation in the liver interstitial space resulting from repeated tissue injury due to chronic liver disease. Liver fibrosis eventually proceeds to cirrhosis and associated complications. So, early diagnosis and staging of liver fibrosis are of vital importance for clinical treatment. Liver biopsy remains the gold standard for the diagnosing and staging of fibrosis, but it is suboptimal due to various limitations. Recently, efforts have been made to migrate toward noninvasive techniques for assessing liver fibrosis. CT is relatively easy to perform, relatively standardized for different scanners, and does not require additional hardware in liver fibrosis staging. MRI is frequently performed to characterize indeterminate liver lesions. Because it does not use ionizing radiation and features high image contrast, its role has increased in the staging of liver fibrosis. More recently, several studies on liver fibrosis staging using deep learning algorithms in CT or MRI have been proposed and have shown meaningful results. In this review, we summarize the basic concept, diagnostic performance, and advantages and limitations of each technique to noninvasively stage liver fibrosis.

Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features

PURPOSES

To develop and externally validate a multiphase computed tomography (CT)-based machine learning (ML) model for staging liver fibrosis (LF) by using whole liver slices.

MATERIALS AND METHODS

The development dataset comprised 232 patients with pathological analysis for LF, and the test dataset comprised 100 patients from an independent outside institution. Feature extraction was performed based on the precontrast (PCP), arterial (AP), portal vein (PVP) phase, and three-phase CT images. CatBoost was utilized for ML model investigation by using the features with good reproducibility. The diagnostic performance of ML models based on each single- and three-phase CT image was compared with that of radiologists' interpretations, the aminotransferase-to-platelet ratio index, and the fibrosis index based on four factors (FIB-4) by using the receiver operating characteristic curve with the area under the curve (AUC) value.

RESULTS

Although the ML model based on three-phase CT image (AUC = 0.65-0.80) achieved higher AUC value than that based on PCP (AUC = 0.56-0.69) and PVP (AUC = 0.51-0.74) in predicting various stage of LF, significant difference was not found. The best CT-based ML model (AUC = 0.65-0.80) outperformed the FIB-4 in differentiating advanced LF and cirrhosis and radiologists' interpretation (AUC = 0.50-0.76) in the diagnosis of significant and advanced LF.

CONCLUSION

All PCP, PVP, and three-phase CT-based ML models can be an acceptable in assessing LF, and the performance of the PCP-based ML model is comparable to that of the enhanced CT image-based ML model.