Follow Up Imaging in Hepatocellular Cancer Ultrasound Screening Exams With Poor Visualization Scores

Cost-Effectiveness Analysis of Hepatocellular Carcinoma Surveillance in Nonalcoholic Fatty Liver Disease Cirrhosis Using US Visualization Score C-Triggered Abbreviated MRI

INTRODUCTION

Ultrasound (US) is associated with severe visualization limitations (US Liver Imaging Reporting and Data System visualization score C) in one-third of patients with nonalcoholic fatty liver disease (NAFLD) cirrhosis undergoing hepatocellular carcinoma (HCC) screening. Data suggest abbreviated MRI (aMRI) may improve HCC screening efficacy. This study analyzed the cost-effectiveness of HCC screening strategies, including an US visualization score-based approach with aMRI, in patients with NAFLD cirrhosis.

METHODS

We constructed a Markov model simulating adults with compensated NAFLD cirrhosis in the United States undergoing HCC screening, comparing strategies of US plus visualization score, US alone, or no surveillance. We modeled aMRI in patients with visualization score C and negative US, while patients with scores A/B did US alone. We performed a sensitivity analysis comparing US plus visualization score with US plus alpha fetoprotein or no surveillance. The primary outcome was the incremental cost-effectiveness ratio (ICER), with a willingness-to-pay threshold of $100,000 per quality-adjusted life-year. Sensitivity analyses were performed for all variables.

RESULTS

US plus visualization score was the most cost-effective strategy, with an ICER of $59,005 relative to no surveillance. The ICER for US alone to US plus visualization score was $822,500. On sensitivity analysis, screening using US plus visualization score remained preferred across several parameters. Even with alpha fetoprotein added to US, the US plus visualization score strategy remained cost-effective, with an ICER of $62,799 compared with no surveillance.

DISCUSSION

HCC surveillance using US visualization score-based approach, using aMRI for visualization score C, seems to be the most cost-effective strategy in patients with NAFLD cirrhosis.

Ultrasound LI-RADS Visualization Scores on Surveillance Ultrasound for Hepatocellular Carcinoma: A Systematic Review With Meta-analysis

We performed a systematic review and meta-analysis to determine the proportions of each surveillance ultrasound (US) visualization score for hepatocellular carcinoma based on the US Liver Imaging Reporting and Data System (LI-RADS) and to identify the factors associated with visualization score C. Original publications reporting US LI-RADS visualization scores were identified in MEDLINE and EMBASE from January 1, 2017, to November 25, 2022. The meta-analytic pooled proportion of each visualization score based on US examination was calculated using the DerSimonian‒Laird random-effects model. Subgroup meta-regression analyses were performed to explore study heterogeneity. US LI-RADS visualization scores were reported from a total of 25,698 US examinations across 12 studies. The pooled proportions of visualization scores A, B and C were 56.7% (95% confidence interval [CI]: 38.6-73.2%, I2 = 99.2%), 30.3% (95% CI: 21.5-40.7%, I2 = 98.8%) and 6.9% (95% CI: 3.9-11.7%, I2 = 97.7%), respectively. Significantly higher proportions of visualization score C were found in studies that exclusively enrolled cirrhosis patients and a study in which the disease etiology was non-alcoholic fatty liver disease (NAFLD) (p < 0.05). In addition, the pooled proportion of visualization score C was higher in studies with a mean or median body mass index >25 kg/m2 (10.7%, 95% CI: 4.3-24.3%). In conclusion, a substantial proportion of surveillance US examinations exhibited moderate to severe limitations on visualization. There was a tendency toward higher proportions of US LI-RADS visualization score C in patients with cirrhosis, NAFLD and obesity.

LI-RADS: Looking Back, Looking Forward

Since its initial release in 2011, the Liver Imaging Reporting and Data System (LI-RADS) has evolved and expanded in scope. It started as a single algorithm for hepatocellular carcinoma (HCC) diagnosis with CT or MRI with extracellular contrast agents and has grown into a multialgorithm network covering all major liver imaging modalities and contexts of use. Furthermore, it has developed its own lexicon, report templates, and supplementary materials. This article highlights the major achievements of LI-RADS in the past 11 years, including adoption in clinical care and research across the globe, and complete unification of HCC diagnostic systems in the United States. Additionally, the authors discuss current gaps in knowledge, which include challenges in surveillance, diagnostic population definition, perceived complexity, limited sensitivity of LR-5 (definite HCC) category, management implications of indeterminate observations, challenges in reporting, and treatment response assessment following radiation-based therapies and systemic treatments. Finally, the authors discuss future directions, which will focus on mitigating the current challenges and incorporating advanced technologies. Tha authors envision that LI-RADS will ultimately transform into a probability-based system for diagnosis and prognostication of liver cancers that will integrate patient characteristics and quantitative imaging features, while accounting for imaging modality and contrast agent.