Liver imaging reporting and data system (LI-RADS) version 2014: understanding and application of the diagnostic algorithm

Liver Imaging Reporting and Data System (LI-RADS) is a system for interpreting and reporting of computed tomography and magnetic resonance imaging of the liver in patients at risk for hepatocellular carcinoma (HCC). LI-RADS has been developed to address the limitations of prior imaging-based criteria including the lack of established consensus regarding the exact definitions of imaging features, binary categorization (either definite or not definite HCC), and failure to consider non-HCC malignancies. One of the most important goals of LI-RADS is to facilitate clear communication between all the personnel involved in the diagnosis and treatment of HCC, such as radiologists, hepatologists, surgeons, and pathologists. Therefore, clinicians should also be familiar with LI-RADS. This article reviews the LI-RADS diagnostic algorithm, and the definitions and management implications of LI-RADS categories

Magnetization-tagged MRI is a simple method for predicting liver fibrosis

Background/Aims:To assess the usefulness of magnetization-tagged magnetic resonance imaging (MRI) in quantifying cardiac-induced liver motion and deformation in order to predict liver fibrosis. Methods:This retrospective study included 85 patients who underwent liver MRI including magnetization-tagged sequences from April 2010 to August 2010. Tagged images were acquired in three coronal and three sagittal planes encompassing both the liver and heart. A Gabor filter bank was used to measure the maximum value of displacement (MaxDisp) and the maximum and minimum values of principal strains (MaxP1 and MinP2, respectively). Patients were divided into three groups (no fibrosis, mild-to-moderate fibrosis, and significant fibrosis) based on their aspartate-aminotransferase-to-platelet ratio index (APRI) score. Group comparisons were made using ANOVA tests. Results:The patients were divided into three groups according to APRI scores: no fibrosis (≤0.5; n=41), moderate fibrosis (0.5–1.5; n=23), and significant fibrosis (>1.5; n=21). The values of MaxDisp were 2.9±0.9 (mean±SD), 2.3±0.7, and 2.1±0.6 in the no fibrosis, moderate fibrosis, and significant fibrosis groups, respectively (P<0.001); the corresponding values of MaxP1 were 0.05±0.2, 0.04±0.02, and 0.03±0.01, respectively (P=0.002), while those of MinP2 were –0.07±0.02, –0.05±0.02, and –0.04±0.01, respectively (P<0.001). Conclusions:Tagged MRI to quantify cardiac-induced liver motion can be easily incorporated in routine liver MRI and may represent a helpful complementary tool in the diagnosis of early liver fibrosis

Growth rate of early-stage hepatocellular carcinoma in patients with chronic liver disease

Background/AimsThe goal of this study was to estimate the growth rate of hepatocellular carcinoma (HCC) and identify the host factors that significantly affect this rate.MethodsPatients with early-stage HCC (n=175) who underwent two or more serial dynamic imaging studies without any anticancer treatment at two tertiary care hospitals in Korea were identified. For each patient, the tumor volume doubling time (TVDT) of HCC was calculated by comparing tumor volumes between serial imaging studies. Clinical and laboratory data were obtained from the medical records of the patients.ResultsThe median TVDT was 85.7 days, with a range of 11 to 851.2 days. Multiple linear regression revealed that the initial tumor diameter (a tumor factor) and the etiology of chronic liver disease (a host factor) were significantly associated with the TVDT. The TVDT was shorter when the initial tumor diameter was smaller, and was shorter in HCC related to hepatitis B virus (HBV) infection than in HCC related to hepatitis C virus (HCV) infection (median, 76.8 days vs. 137.2 days; P=0.0234).ConclusionsThe etiology of chronic liver disease is a host factor that may significantly affect the growth rate of early-stage HCC, since HBV-associated HCC grows faster than HCV-associated HCC

Pitfalls and problems to be solved in the diagnostic CT/MRI Liver Imaging Reporting and Data System (LI-RADS).

The 2017 Core of the computed tomography (CT)/magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) provides clear definitions and concise explanations of the CT/MRI diagnostic algorithm. Nevertheless, there remain some practical and controversial issues that radiologists should be aware of when using the system. This article discusses pitfalls and problems which may be encountered when the version 2017 diagnostic algorithm is used for CT and MRI. The pitfalls include challenges in applying major features and assigning the LR-M category, as well as categorisation discrepancy between CT and MRI. The problems include imprecision of category codes, application of ancillary features, and regional practice variations in hepatocellular carcinoma (HCC) diagnosis. Potential solutions are presented along with these pitfalls and problems. KEY POINTS: • Although the diagnostic algorithm provides clear and detailed explanations, major feature evaluation can be subject to pitfalls and differentiation of HCC and non-HCC malignancy remains challenging. • Ancillary features are optional and equally weighted. However, features such as hepatobiliary phase hypointensity and restricted diffusion have greater impact on HCC diagnosis than other ancillary features and may merit greater emphasis or weighting. • LI-RADS was initially developed from a Western paradigm, which may limit its applicability in the East due to regional practice variations. In Eastern Asia, high sensitivity is prioritised over near-perfect specificity for HCC diagnosis in order to detect tumours at early stages

Pitfalls and problems to be solved in the diagnostic CT/MRI Liver Imaging Reporting and Data System (LI-RADS).

The 2017 Core of the computed tomography (CT)/magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) provides clear definitions and concise explanations of the CT/MRI diagnostic algorithm. Nevertheless, there remain some practical and controversial issues that radiologists should be aware of when using the system. This article discusses pitfalls and problems which may be encountered when the version 2017 diagnostic algorithm is used for CT and MRI. The pitfalls include challenges in applying major features and assigning the LR-M category, as well as categorisation discrepancy between CT and MRI. The problems include imprecision of category codes, application of ancillary features, and regional practice variations in hepatocellular carcinoma (HCC) diagnosis. Potential solutions are presented along with these pitfalls and problems. KEY POINTS: • Although the diagnostic algorithm provides clear and detailed explanations, major feature evaluation can be subject to pitfalls and differentiation of HCC and non-HCC malignancy remains challenging. • Ancillary features are optional and equally weighted. However, features such as hepatobiliary phase hypointensity and restricted diffusion have greater impact on HCC diagnosis than other ancillary features and may merit greater emphasis or weighting. • LI-RADS was initially developed from a Western paradigm, which may limit its applicability in the East due to regional practice variations. In Eastern Asia, high sensitivity is prioritised over near-perfect specificity for HCC diagnosis in order to detect tumours at early stages

Feasibility of radiation dose reduction with iterative reconstruction in abdominopelvic CT for patients with inappropriate arm positioning.

BackgroundThe arms-down position increases computed tomography (CT) radiation dose. Iterative reconstruction (IR) could enhance image quality without increasing radiation dose in patients with arms-down position.AimTo investigate the feasibility of reduced-dose CT with IR for patients with inappropriate arm positioning.MethodsTwenty patients who underwent two-phase abdominopelvic CT including standard-dose and reduced-dose CT (performed with 80% of the radiation dose of the standard protocol) with their arms positioned in the abdominal area were included in this study. Reduced-dose CT images were reconstructed using filtered back projection (FBP), hybrid IR, and iterative model reconstruction (IMR). These images were compared with standard-dose CT images reconstructed with FBP. Objective image noise in the liver and subcutaneous fat was measured by standard deviation for the quantitative analysis. Then, two radiologists qualitatively assessed beam hardening artifacts, artificial texture, noise, sharpness, and overall image quality in consensus.ResultsReduced-dose CT with all IR levels had lower objective image noise compared to standard-dose CT with FBP reconstruction (P ConclusionsIR algorithms can reduce beam hardening artifacts in a reduced-dose CT setting in patients with arms-down position, and an intermediate level of hybrid IR allows radiologists to obtain the best image quality. Because the retrospective and single-center nature of our study limited the number of patients, multicenter prospective clinical studies are required to validate our results

Improved Sensitivity and Reader Confidence in CT Colonography Using Dual-Layer Spectral CT: A Phantom Study

Background Limited cathartic preparations for CT colonography with fecal tagging can improve patient comfort but may result in nondiagnostic examinations from poorly tagged stool. Dual-energy CT may overcome this limitation by improving the conspicuity of the contrast agent, but more data are needed. Purpose To investigate whether dual-energy CT improves polyp detection in CT colonography compared with conventional CT at different fecal tagging levels in vitro. Materials and Methods In this HIPAA-compliant study, between December 2017 and August 2019, a colon phantom 30 cm in diameter containing 60 polyps of different shapes (spherical, ellipsoid, flat) and size groups (5-9 mm, 11-15 mm) was constructed and serially filled with simulated feces tagged with four different iodine concentrations (1.26, 2.45, 4.88, and 21.00 mg of iodine per milliliter), then it was scanned with dual-energy CT with and without an outer fat ring to simulate large body size (total diameter, 42 cm). Two readers independently reviewed conventional 120-kVp CT and 40-keV monoenergetic dual-energy CT images to record the presence of polyps and confidence (three-point scale.) Generalized estimating equations were used for sensitivity comparisons between conventional CT and dual-energy CT, and a Wilcoxon signed-rank test was used for reader confidence. Results Dual-energy CT had higher overall sensitivity for polyp detection than conventional CT (58.8%; 95% confidence interval [CI]: 49.7%, 67.3%; 564 of 960 polyps vs 42.1%; 95% CI: 32.1%, 52.8%; 404 of 960 polyps; P &lt; .001), including with the fat ring (48% and 31%, P &lt; .001). Reader confidence improved with dual-energy CT compared with conventional images on all tagging levels (P &lt; .001). Interrater agreement was substantial (κ = 0.74; 95% CI: 0.70, 0.77). Conclusion Compared with conventional 120-kVp CT, dual-energy CT improved polyp detection and reader confidence in a dedicated dual-energy CT colonography phantom, especially with suboptimal fecal tagging. © RSNA, 2020

A lexicon for hepatocellular carcinoma surveillance ultrasonography: benign versus malignant lesions

Background/Aims To suggest a lexicon for liver ultrasonography and to identify radiologic features indicative of benign or malignant lesions on surveillance ultrasonography. Methods This retrospective study included 188 nodules (benign, 101; malignant, 87) from 175 at-risk patients identified during surveillance ultrasonography for hepatocellular carcinoma. We created a lexicon for liver ultrasonography by reviewing relevant literature regarding the ultrasonographic features of hepatic lesions. Using this lexicon, two abdominal radiologists determined the presence or absence of each ultrasonographic feature for the included hepatic lesions. Independent factors associated with malignancy and interobserver agreement were determined by logistic regression analysis and kappa statistics, respectively. Results Larger tumor size (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06-1.183; P<0.001), multinodular confluent morphology (OR, 7.712; 95% CI, 1.053-56.465; P=0.044), thick hypoechoic rim (OR, 5.878; 95% CI, 2.681-12.888; P<0.001), and posterior acoustic enhancement (OR, 3.077; 95% CI, 1.237-7.655; P=0.016) were independently associated with malignant lesions. In a subgroup analysis of lesions <2 cm, none of the ultrasonographic features were significantly associated with malignancy or benignity. Interobserver agreement for morphology was fair (κ=0.36), while those for rim (κ=0.427), echogenicity (κ=0.549), and posterior acoustic enhancement (κ=0.543) were moderate. Conclusions For hepatic lesions larger than 2 cm, some ultrasonography (US) features might be suggestive of malignancy. We propose a lexicon that may be useful for surveillance US