The Organ Procurement and Transplantation Network hepatocellular carcinoma classification: Alignment with Liver Imaging Reporting and Data System, current gaps, and future direction

Intraindividual comparison of prognostic imaging features of HCCs between MRIs with extracellular and hepatobiliary contrast agents

BACKGROUND & AIMS

Accumulating evidence suggests that certain imaging features of hepatocellular carcinoma (HCC) may have prognostic implications. This study aimed to intraindividually compare MRIs with extracellular contrast agent (ECA-MRI) and hepatobiliary agent (HBA-MRI) for prognostic imaging features of HCC and to compare the prediction of microvascular invasion (MVI) and early recurrence between the two MRIs.

METHODS

The present study included 102 prospectively enrolled at-risk patients (median age, 61.0 years; 83 men) with surgically resected single HCC with both preoperative ECA-MRI and HBA-MRI between July 2019 and June 2023. The McNemar test was used to compare each prognostic imaging feature between the two MRIs. Significant imaging features associated with MVI were identified by multivariable logistic regression analysis, and early recurrence rates (<2 years) were compared between the two MRIs.

RESULTS

The frequencies of prognostic imaging features were not significantly different between the two MRIs (p = .07 to >.99). Non-smooth tumour margin (ECA-MRI, odds ratio [OR] = 5.30; HBA-MRI, OR = 7.07) and peritumoral arterial phase hyperenhancement (ECA-MRI, OR = 4.26; HBA-MRI, OR = 4.43) were independent factors significantly associated with MVI on both MRIs. Two-trait predictor of venous invasion (presence of internal arteries and absence of hypoattenuating halo) on ECA-MRI (OR = 11.24) and peritumoral HBP hypointensity on HBA-MRI (OR = 20.42) were other predictors of MVI. Early recurrence rates of any two or more significant imaging features (49.8% on ECA-MRI vs 51.3% on HBA-MRI, p = .75) were not significantly different between the two MRIs.

CONCLUSION

Prognostic imaging features of HCC may be comparable between ECA-MRI and HBA-MRI.

Performance of LI-RADS category 5 vs combined categories 4 and 5: a systemic review and meta-analysis

OBJECTIVE

Computed tomography (CT)/magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS, LR) category 5 has high specificity and modest sensitivity for diagnosis of hepatocellular carcinoma (HCC). The purpose of this study was to compare the diagnostic performance of LR-5 vs combined LR-4 and LR-5 (LR-4/5) for HCC diagnosis.

METHODS

MEDLINE and EMBASE databases through January 03, 2023 were searched for studies reporting the performance of LR-5 and combined LR-4/5 for HCC diagnosis, using CT/MRI LI-RADS version 2014, 2017, or 2018. A bivariate random-effects model was used to calculate the pooled per-observation diagnostic performance. Subgroup analysis was performed based on imaging modalities and type of MRI contrast material.

RESULTS

Sixty-nine studies (15,108 observations, 9928 (65.7%) HCCs) were included. Compared to LR-5, combined LR-4/5 showed significantly higher pooled sensitivity (83.0% (95% CI [80.3-85.8%]) vs 65.7% (95% CI [62.4-69.1%]); p < 0.001), lower pooled specificity (75.0% (95% CI [70.5-79.6%]) vs 91.7% (95% CI [90.2-93.1%]); p < 0.001), lower pooled positive likelihood ratio (3.60 (95% CI [3.06-4.23]) vs 6.18 (95% CI [5.35-7.14]); p < 0.001), and lower pooled negative likelihood ratio (0.22 (95% CI [0.19-0.25]) vs 0.38 (95% CI [0.35-0.41]) vs; p < 0.001). Similar results were seen in all subgroups.

CONCLUSIONS

Our meta-analysis showed that combining LR-4 and LR-5 would increase sensitivity but decrease specificity, positive likelihood ratio, and negative likelihood ratio. These findings may inform management guidelines and individualized management.

CLINICAL RELEVANCE STATEMENT

This meta-analysis estimated the magnitude of changes in the sensitivity and specificity of imaging criteria when LI-RADS categories 4 and 5 were combined; these findings can inform management guidelines and individualized management.

KEY POINTS

There is no single worldwide reporting system for liver imaging, partly due to regional needs. Combining LI-RADS categories 4 and 5 increased sensitivity and decreased specificity and positive and negative likelihood ratios. Changes in the sensitivity and specificity of imaging criteria can inform management guidelines and individualized management.

Up-to-Date Role of Liver Imaging Reporting and Data System in Hepatocellular Carcinoma

This article overviews Liver Imaging Reporting and Data System (LI-RADS), a system that standardizes techniques, interpretation and reporting of imaging studies done for hepatocellular carcinoma surveillance, diagnosis, and locoregional treatment response assessment. LI-RADS includes 4 algorithms, each of which defines ordinal categories reflecting probability of the assessed outcome. The categories, in turn, guide patient management. The LI-RADS diagnostic algorithms provide diagnostic criteria for the entire spectrum of lesions found in at-risk patients. In addition, the use of LI-RADS in clinical care improves clarity of communication between radiologists and clinicians and may improve the performance of inexperienced users to the levels of expert liver imagers.

Standardizing liver imaging reporting and interpretation: LI-RADS and beyond

Imaging plays a crucial role in diagnosis and post-treatment monitoring of primary liver cancers. Clear, consistent, and actionable communication of imaging results is crucial to avoid miscommunication and potential detrimental impact on patient care. In this review, we discuss the importance, advantages, and potential impact of universal adoption of standardized terminology and interpretive criteria for liver imaging, from the point of view of radiologists and clinicians.

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.