Expanding the Liver Imaging Reporting and Data System (LI-RADS) v2018 diagnostic population: performance and reliability of LI-RADS for distinguishing hepatocellular carcinoma (HCC) from non-HCC primary liver carcinoma in patients who do not meet strict LI-RADS high-risk criteria

Evidence Supporting Diagnostic Value of Liver Imaging Reporting and Data System for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis

Background

Based on the Liver Imaging Data and Reporting System (LI-RADS) guidelines, Hepatocellular Carcinoma (HCC) can be diagnosed using imaging criteria in patients at risk of HCC.

Objective

This study aimed to assess the diagnostic value of LI-RADS in high-risk patients with HCC.

Material and Methods

This systematic review is conducted on international databases, including Google Scholar, Web of Science, PubMed, Embase, PROQUEST, and Cochrane Library, with appropriate keywords. Using the binomial distribution formula, the variance of each study was calculated, and all the data were analyzed using STATA version 16. The pooled sensitivity and specificity were determined using a random-effects meta-analysis approach. Also, we used the chi-squared test and I2 index to calculate heterogeneity among studies, and Funnel plots and Egger tests were used for evaluating publication bias.

Results

The pooled sensitivity was estimated at 0.80 (95% CI 0.76-0.84). According to different types of Liver Imaging Reporting and Data Systems (LI-RADS), the highest pooled sensitivity was in version 2018 (0.83 (95% CI 0.79-0.87) (I2: 80.6%, P of chi 2 test for heterogeneity <0.001 and T2: 0.001). The pooled specificity was estimated as 0.89 (95% CI 0.87-0.92). According to different types of LI-RADS, the highest pooled specificity was in version 2014 (93.0 (95% CI 89.0-96.0) (I2: 81.7%, P of chi 2 test for heterogeneity <0.001 and T2: 0.001).

Conclusion

LI-RADS can assist radiologists in achieving the required sensitivity and specificity in high-risk patients suspected to have HCC. Therefore, this strategy can serve as an appropriate tool for identifying HCC.

Adherence to LI-RADS and EASL high-risk population criteria: A systematic review

BACKGROUND AND AIMS

The Liver Imaging Reporting and Data System (LI-RADS) and European Association for the Study of the Liver (EASL) diagnostic criteria for noninvasive diagnosis of HCC can only be applied to patients at a high risk of HCC. This systematic review assesses adherence to the LI-RADS and EASL high-risk population criteria in published studies.

APPROACH AND RESULTS

PubMed was searched for original research, published between January 2012 and December 2021, reporting LI-RADS and EASL diagnostic criteria on contrast-enhanced ultrasound, CT, or MRI. The algorithm version, publication year, risk status, and etiologies of chronic liver disease were recorded for each study. Adherence to high-risk population criteria was evaluated as optimal (unequivocal adherence), suboptimal (equivocal), or inadequate (clear violation). A total of 219 original studies were included, with 215 that used the LI-RADS criteria, 4 EASL only, and 15 that evaluated both LI-RADS and EASL criteria. Optimal, suboptimal, or inadequate adherence to high-risk population criteria was observed in 111/215 (51.6%), 86/215 (40.0%), and 18/215 (8.4%) LI-RADS and 6/19 (31.6%), 5/19 (26.3%), and 8/19 (42.1%) EASL studies ( p < 0.001) regardless of the imaging modality. Adherence to high-risk population criteria significantly improved according to the CT/MRI LI-RADS versions (optimal in v2018 in 64.5% of studies; v2017, 45.8%; v2014, 24.4%; v2013.1, 33.3%; p < 0.001) and the publication year (2020-2021, 62.5%; 2018-2019, 33.9%; 2014-2017, 39.3% of all LI-RADS studies; p = 0.002). No significant differences in adherence to high-risk population criteria were observed in the versions of contrast-enhanced ultrasound LI-RADS ( p = 0.388) or EASL ( p = 0.293).

CONCLUSION

Adherence to high-risk population criteria was optimal or suboptimal in about 90% and 60% of LI-RADS and EASL studies, respectively.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Non-invasive diagnosis strategy of hepatocellular carcinoma in low-risk population

Aims

With prevalence of hepatocellular carcinoma (HCC) in low-risk population (LRP), establishing a non-invasive diagnostic strategy becomes increasingly urgent to spare unnecessary biopsies in this population. The purposes of this study were to find characterisics of HCC and to establish a proper non-invasive method to diagnose HCC in LRP.

Methods

A total of 681 patients in LRP (defined as the population without cirrhosis, chronic HBV infection or HCC history) were collected from 2 institutions. The images of computed tomography (CT) and magnetic resonance imaging (MRI) were manually analysed. We divided the patients into the training cohort (n = 324) and the internal validating cohort (n = 139) by admission time in the first institution. The cohort in the second institution was viewed as the external validation (n = 218). A multivariate logistic regression model incorporating both imaging and clinical independent risk predictors was developed. C-statistics was used to evaluate the diagnostic performance.

Results

Besides the major imaging features of HCC (non-rim enhancement, washout and enhancing capsule), tumor necrosis or severe ischemia (TNSI) on imaging and two clinical characteristics (gender and alpha fetoprotein) were also independently associated with HCC diagnosis (all P < 0.01). A clinical model (including 3 major features, TNSI, gender and AFP) was built to diagnose HCC and achieved good diagnostic performance (area under curve values were 0.954 in the training cohort, 0.931 in the internal validation cohort and 0.902 in the external cohort).

Conclusions

The clinical model in this study developed a satisfied non-invasive diagnostic performance for HCC in LRP.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09812-w.

LI-RADS Version 2018 Targetoid Appearances on Gadoxetic Acid-Enhanced MRI: Interobserver Agreement and Diagnostic Performance for the Differentiation of HCC and Non-HCC Malignancy

Background: In LI-RADS version 2018, observations showing at least one of five targetoid appearances on different sequences or postcontrast phases are assigned LR-M, indicating likely non-hepatocellular carcinoma (HCC) malignancy. Objective: To evaluate interobserver agreement of the LI-RADS targetoid appearances among a large number of radiologists of varying experiences, and the targetoid appearances' diagnostic performance for differentiating HCC from non-HCC malignancy. Methods: This retrospective study included 100 patients (76 men, 24 women; mean age, 58±9 years) at high-risk for HCC who underwent gadoxetic acid-enhanced MRI within 30 days before hepatic tumor resection [25 randomly selected patients with non-HCC malignancy (13 intrahepatic cholangiocarcinoma, 12 combined HCC-cholangiocarcinoma); 75 matched patients with HCC]. Eight radiologists [four more-experienced (8-15 years); four less-experienced (1-5 years)] from seven different institutions independently assessed observations for the five targetoid appearances and LI-RADS categorization. Interobserver agreement and diagnostic performance for non-HCC malignancy were evaluated. Results: Interobserver agreement was poor for peripheral washout (κ=0.20); moderate for targetoid transitional-phase or hepatobiliary-phase appearance (κ=0.33), delayed central enhancement (κ=0.37), and targetoid restriction (κ=0.43); and substantial for rim arterial-phase hyperenhancement (κ=0.61). Agreement was fair for at least one targetoid appearance (κ=0.36) and moderate for at least two, three, or four targetoid appearances (κ=0.43-0.51). Agreement for individual targetoid appearances was not significantly different between more-experienced and less-experienced readers other than for targetoid restriction (κ=0.63 vs 0.43; p=.001). Agreement for at least one targetoid appearance was fair among more-experienced (κ=0.29) and less-experienced (κ=0.37) reviewers. Agreement for at least two, three, or four targetoid appearances was moderate-to-substantial among more-experienced reviewers (κ=0.45-0.63) and moderate among less-experienced reviewers (κ=0.42-0.56). Existing LR-M criteria of at least one targetoid appearance had median accuracy for non-HCC malignancy of 62%, sensitivity of 84%, and specificity of 54%. For all reviewers, accuracy was highest when requiring at least three (median accuracy 79%, sensitivity 68%, specificity 82%) or four (median accuracy 80%, sensitivity 54%, specificity 88%) targetoid appearances. Conclusion: Targetoid appearances and LR-M categorization exhitibted considerable interobserver variation for both more- and less-experienced reviewers. Clinical Impact: Requirement for multiple targetoid appearances for LR-M categorization improved interobserver agreement and diagnostic accuracy for non-HCC malignancy.

Non-invasive imaging criteria for the diagnosis of hepatocellular carcinoma in non-cirrhotic patients with chronic hepatitis B

Background & Aims

Criteria defined by the European Association for the Study of the Liver (EASL) and Liver Imaging Reporting and Data System (LI-RADS) enable hepatocellular carcinoma (HCC) diagnosis based on imaging in cirrhosis. Non-cirrhotic patients require biopsy given the lower pre-test probability of HCC. The objective of our study was to assess the performance of EASL and LI-RADS criteria for the diagnosis of HCC in non-cirrhotic patients with chronic HBV infection.

Methods

This was a cross-sectional study performed at a referral center. We included all patients with HBV without cirrhosis with focal liver lesions who underwent contrast-enhanced CT or MRI at our clinic between 2005-2018. Studies were reviewed by 2 radiologists blinded to the diagnosis.

Results

We included 280 patients, median age was 56.8 (IQR 48.2-65.45) years and 223 (80%) were male. In 191 (79%) cases the lesion was found as a result of screening. Cirrhosis was excluded based on pathology in 252 (90%) cases. We assessed 338 nodules: 257 (76%) HCC, 40 (12%) non-HCC malignant lesions, and 41 (12%) benign lesions. EASL criteria and LR-5/LR-tumor-in-vein (TIV) categories had a 100% agreement in categorizing lesions as HCC, and 226 nodules (67%) were classified as HCCs. The sensitivity, specificity, positive predictive value, and negative predictive value were 82.1 (76.9-86.6), 81.5 (71.3-89.2), 93.4 (89.3-96.2), and 58.9 (49.2-68.1), respectively. When the pre-test probability of HCC is >70%, estimated as a PAGE-B score above 9, and EASL or LR-5/LR-TIV criteria are met, post-test probability would be >90%.

Conclusions

EASL criteria and LR-5/LR-TIV categories show a positive predictive value in patients with HBV without cirrhosis that is comparable to that seen in patients with cirrhosis. These criteria can be used when the pre-test probability of HCC is >70%.

Lay summary

Current guidelines recommend performing a biopsy to confirm the diagnosis of presumed hepatocellular carcinoma (HCC) in patients without cirrhosis. We showed that specific imaging criteria had a 100% agreement for categorizing lesions as HCC, with a positive predictive value of 93.4%. These imaging criteria could be used to diagnose HCC in HBV patients without cirrhosis with a pre-test probability of HCC of ≥70%, avoiding the need for a liver biopsy.

•

Imaging criteria defined by the EASL and LI-RADS enable the diagnosis of HCC without biopsy in patients with cirrhosis.

•

A biopsy is recommended in all patients without cirrhosis.

•

Imaging criteria had a good performance in patients with HBV infection without cirrhosis when pre-test probability was >70%.

•

HCC may be diagnosed based solely on imaging criteria in patients with HBV subject to HCC screening (i.e. PAGE-B score >9).

Magnetic Resonance Imaging of Nonhepatocellular Malignancies in Chronic Liver Disease

Hepatocellular carcinoma (HCC) is the most common liver malignancy associated with chronic liver disease. Nonhepatocellular malignancies may also arise in the setting of chronic liver disease. The imaging diagnosis of non-HCC malignancies may be challenging. Non-HCC malignancies in patients with chronic liver disease most commonly include intrahepatic cholangiocarcinoma and combined hepatocellular-cholangiocarcinoma, and less commonly hepatic lymphomas and metastases. On MR imaging, non-HCC malignancies often demonstrate a targetoid appearance, manifesting as rim arterial phase hyperenhancement, peripheral washout, central delayed enhancement, and peripheral restricted diffusion. When applying the Liver Imaging Reporting and Data System algorithm, observations with targetoid appearance are categorized as LR-M.

Up-to-Date Role of CT/MRI LI-RADS in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of mortality worldwide and a major healthcare burden in most societies. Computed tomography (CT) and magnetic resonance imaging (MRI) play a pivotal role in the medical care of patients with or at risk for hepatocellular carcinoma (HCC). When stringent imaging criteria are fulfilled, CT and MRI allow for diagnosis, staging, and assessment of response to treatment, without the need for invasive workup, and can inform clinical decision making. Owing to the central role of these imaging modalities in HCC management, standardization is essential to facilitate proper imaging technique, accurate interpretation, and clear communication among all stakeholders in both the clinical practice and research settings. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system that provides standardization across the continuum of HCC imaging, including ordinal probabilistic approach for reporting that directs individualized management. This review discusses the up-to-date role of CT and MRI in HCC imaging from the LI-RADS perspective. It also provides a glimpse into the future by discussing how advances in knowledge and technology are likely to enrich the LI-RADS approach.

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

OBJECTIVE. The purpose of this article was to assess the reliability and accuracy of hepatic periportal space widening and other qualitative imaging features for the prediction of hepatic fibrosis. MATERIALS AND METHODS. This single-center retrospective study identified consecutive patients who had undergone liver MR elastography. Two abdominal radiologists independently reviewed anatomic images, assessing multiple qualitative features of chronic liver disease (CLD) including periportal space widening. Each reader also measured the periportal space at the main portal vein (MPV) and right portal vein (RPV). Interrater reliability analysis was then performed. Sensitivity and specificity were determined for the detection of any hepatic fibrosis (stage I or higher) and of advanced fibrosis (stage III or higher) using stiffness on MR elastography as the reference standard. RESULTS. Of 229 subjects, 157 (69%) had fibrosis and 78 (34%) had advanced fibrosis. Agreement for periportal space widening was moderate (κ = 0.47), and agreement for remaining features was moderate to substantial (κ = 0.42-0.80). Agreement for the periportal space at the MPV was moderate (ICC, 0.55), and agreement for the periportal space at the RPV was near perfect (ICC, 0.83). Periportal space widening had the highest sensitivity (83.0%) for any fibrosis, with limited specificity (61.3%). Surface nodularity had the highest specificity (94.4%) for any fibrosis, with limited sensitivity (51.6%). Periportal space widening plus one or more additional imaging feature of CLD or the presence of surface nodularity alone had sensitivity of 72.6% and specificity of 76.1%. A periportal space at the MPV greater than 9.5 mm had substantial agreement with qualitative periportal space widening (κ = 0.74). CONCLUSION. Periportal space widening has a high sensitivity for hepatic fibrosis, with moderate specificity when combined with additional anatomic features of CLD.

Inter-reader reliability of CT Liver Imaging Reporting and Data System according to imaging analysis methodology: a systematic review and meta-analysis

OBJECTIVES

To establish inter-reader reliability of CT Liver Imaging Reporting and Data System (LI-RADS) and explore factors that affect it.

METHODS

MEDLINE and EMBASE databases were searched from January 2014 to March 2020 to identify original articles reporting the inter-reader reliability of CT LI-RADS. The imaging analysis methodology of each study was identified, and pooled intraclass correlation coefficient (ICC) or kappa values (κ) were calculated for lesion size, major features (arterial-phase hyperenhancement [APHE], nonperipheral washout [WO], and enhancing capsule [EC]), and LI-RADS categorization (LR) using random-effects models. Subgroup analyses of pooled κ were performed for the number of readers, average reader experience, differences in reader experience, and LI-RADS version.

RESULTS

In the 12 included studies, the pooled ICC or κ of lesion size, APHE, WO, EC, and LR were 0.99 (0.96-1.00), 0.69 (0.58-0.81), 0.67 (0.53-0.82), 0.65 (0.54-0.76), and 0.70 (0.59-0.82), respectively. The experience and number of readers varied: studies using readers with ≥ 10 years of experience showed significantly higher κ for LR (0.82 vs. 0.45, p = 0.01) than those with < 10 years of reader experience. Studies with multiple readers including inexperienced readers showed significantly lower κ for APHE (0.55 vs. 0.76, p = 0.04) and LR (0.45 vs. 0.79, p = 0.02) than those with all experienced readers.

CONCLUSIONS

CT LI-RADS showed substantial inter-reader reliability for major features and LR. Inter-reader reliability differed significantly according to average reader experience and differences in reader experience. Reported results for inter-reader reliability of CT LI-RADS should be understood with consideration of the imaging analysis methodology.

KEY POINTS

• The CT Liver Imaging Reporting and Data System (LI-RADS) provides substantial inter-reader reliability for three major features and category assignment. • The imaging analysis methodology varied across studies. • The inter-reader reliability of CT LI-RADS differed significantly according to the average reader experience and the difference in reader experience.

Targetoid appearance on T2-weighted imaging and signs of tumor vascular involvement: diagnostic value for differentiating HCC from other primary liver carcinomas

OBJECTIVES

To evaluate targetoid appearance on T2-weighted imaging and signs of tumor vascular involvement as potential new LI-RADS features for differentiating hepatocellular carcinoma (HCC) from other non-HCC primary liver carcinomas (PLCs).

METHODS

This IRB-approved, retrospective study was performed at two liver transplant centers. The final population included 375 patients with pathologically proven lesions imaged between 2007 and 2017 with contrast-enhanced CT or MRI. The cohort consisted of 165 intrahepatic cholangiocarcinomas and 74 combined hepatocellular-cholangiocarcinomas, with the addition of 136 HCCs for control. Two abdominal radiologists (R1; R2) independently reviewed the imaging studies (112 CT; 263 MRI) and recorded the presence of targetoid appearance on T2-weighted images and features of tumor vascular involvement including encasement, narrowing, tethering, occlusion, and obliteration. The sensitivity and specificity of each feature were calculated for the diagnosis of non-HCC PLCs. Cohen's kappa (k) test was used to assess inter-reader agreement.

RESULTS

The sensitivity of targetoid appearance on T2-weighted images for the diagnosis of non-HCC PLCs was 27.5% and 32.6% (R1 and R2) and the specificity was 98.2% and 97.3% (R1 and R2). Among the features of tumor vascular involvement, those providing the highest sensitivity for non-HCC PLCs were vascular encasement (R1: 34.3%; R2: 37.2%) and obliteration (R1: 25.5%; R2: 29.7%). The highest specificity for non-HCC PLCs was provided by tethering (R1: 100%; R2: 97.1%) and occlusion (R1: 99.3%; R2: 99.3%). The inter-reader agreement was moderate to substantial (k = 0.48-0.77).

CONCLUSIONS

Targetoid appearance on T2-weighted images and features of tumor vascular involvement demonstrated high specificity for non-HCC malignancy.

KEY POINTS

• Targetoid appearance on T2-weighted imaging and signs of tumor vascular involvement have high specificity (92-100%) for the diagnosis of non-HCC PLCs, regardless of the presence of liver risk factors. • In the subset of patients with risk factors for HCC, the sensitivity of signs of tumor vascular involvement decreases for both readers (1.7-20.3%), while the specificity increases reaching values higher than 94.2%. • The inter-reader agreement is substantial for targetoid appearance on T2-weighted images (k = 0.74) and moderate to substantial for signs of tumor vascular involvement (k = 0.48-0.77).

Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.