What’s New in Liver Imaging – August 2021

2 years ago

LIVER: Ultrasound

a) US LI-RADS Visualization Score: Interobserver Variability and Association With Cause of Liver Disease, Sex, and Body Mass Index.

Kiri L, Abdolell M, Costa AF, et al. SAGE Publications. Sage CA: Los Angeles, CA; 2021.

Available from: https://journals.sagepub.com/doi/full/10.1177/08465371211012104

Keywords: CEUS, HCC, LI-RADS

Clinical question: What are the associations between the US LI-RADS Visualization Score and various clinical factors?

What was done: Uni/Multivariate analyses between US Visualization Score and various clinical factors.

How was it done: Retrospective evaluation of 237 HCC surveillance US studies. US LI-RADS Visualization Score was assigned (A: no/minimal limitations, B: moderate limitations, C: severe limitations).

Findings and results: Visualization Scores of B or C were assigned in 148/237 cases, and found to be associated with elevated BMI >25 kg/m2 and underlying NASH (rather than viral hepatitis).

Conclusion and Implications: Moderate or severe limitations on US examinations was significantly associated with NASH and elevated BMI.


b) Ultrasound Liver Imaging Reporting and Data System (US LI-RADS) Visualization Score: a reliability analysis on inter-reader agreement.

Tiyarattanachai T, Bird KN, Lo EC, et al. Abdominal Radiology. Springer; 2021; 1-8.

Available from https://link.springer.com/article/10.1007/s00261-021-03067-y

Keywords: CEUS, HCC, LI-RADS

Clinical question: What is the degree of inter-reader agreement for the CEUS LI-RADS Visualization Score?

What was done: Retrospective review of 3115 HCC screening/surveillance US studies.

How was it done: 30 studies within each Visualization score were randomly selected and 9 radiologists had to re-assign a Visualization Score independently to these 90 studies. Intraclass correlation coefficient (ICC) was calculated.

Findings and results: ICC between all 9 radiologists was 0.70, highest in the patient group with normal liver parenchyma, lower in steatosis, lowest in cirrhosis.

Conclusions/Implications: CEUS LI-RADS Visualization Score has good inter-reader agreement.

c) Can Risk Stratification Based on Ultrasound Elastography of Background Liver Assist CEUS LI-RADS in the Diagnosis of HCC?

Li J, Ling W, Chen S, et al. Frontiers in Oncology. Frontiers Media SA; 2021;11.

Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120148/

Keywords: CEUS, HCC, LI-RADS

Clinical question: Can the liver background on ultrasound elastography be used for risk stratification that is meaningful with respect to CEUS LI-RADS, especially in patients without chronic hepatitis B?

What was done: Retrospective study of 304 patients with pathology results on focal liver lesions and liver stiffness measurements.

How was it done: Liver stiffness stratification was applied and analyzed against assigned CEUS LI-RADS categories for each focal liver lesion with regards to diagnostic performance.

Findings and results: Liver stiffness categories were 5.8-6.8 kPa, 6.8-9.1 kPa, 9.1-10.3 kPa, and ≥10.3 kPa. Higher specificity and AUC for HCC was observed in patients without chronic hepatitis B in patients with liver stiffness ≥9.1 kPa.

Conclusion/Implications: CEUS LI-RADS can be applied in patients without chronic hepatitis B who have liver stiffness ≥9.1 kPa.


LIVER: Prognostic Biomarkers

a) Gadoxetate-enhanced MRI Features of Proliferative Hepatocellular Carcinoma Are Prognostic after Surgery.

Kang H-J, Kim H, Lee DH, et al. Radiology. Radiological Society of North America ; 2021;204352.

Available from: https://pubs.rsna.org/doi/abs/10.1148/radiol.2021204352

Keywords: HCC, MRI, prognostic value, gadoxetate

Clinical question: What is the significance that the findings seen on gadoxetate-enhanced MRI have with respect to proliferative class HCC and post-surgery prognosis?

What was done: Retrospective study of 158 patients with surgically resected HCC and had preoperative gadoxetate-enhanced MRI.

How was it done: Predictive factors for overall survival, intrahepatic distance recurrence, and extrahepatic metastasis were determined using Cox analysis. Multivariable logistic regression was used to determine factors associated with the proliferative class of HCC.

Findings and results: Proliferative class assignment was associated with worse post-surgical outcomes. Rim arterial phase hyperenhancement (APHE) on preoperative MRI and high serum AFP were independent predictors for proliferative class. Independently, rim APHE was associated with poor overall survival and higher rates of extrahepatic metastases.

Conclusions/Implications: Proliferative class and rim APHE on preoperative gadoxetate-enhanced MRI are independent factors for poor overall survival and increased extrahepatic metastasis.


b) Comparison of Conventional Gadoxetate Disodium–Enhanced MRI Features and Radiomics Signatures With Machine Learning for Diagnosing Microvascular Invasion.

Chen Y, Xia Y, Tolat PP, et al. Am J Roentgenol. ARRS; 2021;216(6):1510–1520.

Available from: https://doi.org/102214/AJR2023255

Keywords: HCC, MRI, prognostic value, gadoxetate

Clinical question: Can a machine learning model predict microvascular invasion (MVI) of HCC based on gadoxetate-enhanced MRI?

What was done: Retrospective study of 269 patients with postoperative pathology-proven HCC.

How was it done: Gadoxetate-enhanced MRI features were incorporated into a machine learning model. The least absolute shrinkage and selection operator (LASSO) was used for feature selection, and LASSO models were built with six classifiers. Predictive capability was assessed using the ROC AUC.

Findings and results: MVI was confirmed on histopathology in 111 of 269 patients. ADC value, nonsmooth tumor margin, and 20-minute T1 relaxation time showed higher diagnostic accuracy with AUC >0.84. During hepatobiliary phase (HBP), the model showed greater diagnostic efficiency for MVI prediction with AUC>0.93.

Conclusions/Implications: ADC value, nonsmooth tumor margin, and 20-minute T1 relaxation time showed high diagnostic accuracy for predicting MVI. Machine learning radiomics models can further improve the diagnostic accuracy and especially during HBP.


c) Imaging Biomarkers of Hepatic Fibrosis: Reliability and Accuracy of Hepatic Periportal Space Widening and Other Morphologic Features on MRI.

Ludwig DR, Fraum TJ, Ballard DH, Narra VR, Shetty AS. Am J Roentgenol. ARRS; 2021;216(5):1229–1239.

Available from: https://doi.org/102214/AJR2023099

Keywords: MRI, diagnostic value

Clinical question: What is the reliability and accuracy of hepatic periportal space widening (and other imaging features of chronic liver disease) for the prediction of hepatic fibrosis?

What was done: Retrospective study of 229 patients with fibrosis and advanced fibrosis who had undergone liver MR elastography.

How was it done: Periportal space at the main portal vein and right portal vein was measured and then analyzed for sensitivity and specificity for detection of fibrosis or advanced fibrosis.

Findings and results: Moderate agreement for periportal space widening and fibrosis; moderate to substantial agreement for the remaining features evaluated. Specifically, periportal space at the main portal vein had moderate agreement, while agreement was near-perfect for periportal space at the right portal vein. Periportal space widening had 83% sensitivity and 52% specificity for fibrosis. In combination with additional features, sensitivity was 73% and specificity 76%.

Conclusions/Implications: Periportal space widening has high sensitivity for hepatic fibrosis and if combined with additional imaging features, also has moderate specificity.

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