LI-RADS Major Features on MRI for Diagnosing Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Node-RADS: a systematic review and meta-analysis of diagnostic performance, category-wise malignancy rates, and inter-observer reliability

OBJECTIVE

To perform a systematic review and meta-analysis to estimate diagnostic performance, category-wise malignancy rates, and inter-observer reliability of Node Reporting and Data System 1.0 (Node-RADS).

METHODS

Five electronic databases were systematically searched for primary studies on the use of Node-RADS to report the possibility of cancer involvement of lymph nodes on CT and MRI from January 1, 2021, until April 15, 2024. The study quality was assessed by modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Quality Appraisal of Diagnostic Reliability (QAREL) tools. The diagnostic accuracy was estimated with bivariate random-effects model, while the pooled category-wise malignancy rates were obtained with random-effects model.

RESULTS

Six Node-RADS-CT studies and three Node-RADS-MRI studies covering nine types of cancer were included. The study quality was mainly damaged by inappropriate index test and unknown timing according to QUADAS-2, and unclear blindness during the rating process according to QAREL. The area under hierarchical summary receiver operating characteristic curve (95% conventional interval) was 0.92 (0.89-0.94) for Node-RADS ≥ 3 as positive and 0.91 (0.88-0.93) for Node-RADS ≥ 4 as positive, respectively. The pooled malignancy rates (95% CIs) of Node-RADS 1 to 5 were 4% (0-10%), 31% (9-58%), 55% (34-75%), 89% (73-99%), and 100% (97-100%), respectively. The inter-observer reliability of five studies was interpreted as fair to substantial.

CONCLUSION

Node-RADS presented a promising diagnostic performance with an increasing probability of malignancy along higher category. However, the evidence for inter-observer reliability of Node-RADS is insufficient, and may hinder its implementation in clinical practice for lymph node assessment.

KEY POINTS

Question Node-RADS is designed for structured reporting of the possibility of cancer involvement of lymph nodes, but the evidence supporting its application has not been summarized. Findings Node-RADS presented diagnostic performance with AUC of 0.92, and malignancy rates for categories 1-5 ranged from 4% to 100%, while the inter-observer reliability was unclear. Clinical relevance Node-RADS is a useful tool for structured reporting of the possibility of cancer involvement of lymph nodes with high diagnostic performance and appropriate malignancy rate for each category, but unclear inter-observer reliability may hinder its implementation in clinical practice.

Combining CEUS and CT/MRI LI-RADS major imaging features: diagnostic accuracy for classification of indeterminate liver observations in patients at risk for HCC

PURPOSE

To determine the diagnostic accuracy of combining CEUS and CT/MRI LI-RADS major imaging features for the improved categorization of liver observations indeterminate on both CT/MRI and CEUS.

MATERIALS AND METHODS

A retrospective analysis using a database from a prospective study conducted at 11 centers in North America and Europe from 2018 to 2022 included a total of 109 participants at risk for HCC who had liver observations with indeterminate characterization (LR3, LR-4, and LR-M) on both CEUS and CT/MRI. The individual CEUS and CT/MRI LI-RADS major features were extracted from the original study and analyzed in various combinations. Reference standards included biopsy, explant histology, and follow-up CT/MRI. The diagnostic performance of the combinations of LI-RADS major features for definitive diagnosis of HCC was calculated. A reverse, stepwise logistical regression sub-analysis was also performed.

RESULTS

This study included 114 observations indeterminate on both CT/MRI and CEUS. These observations were categorized as LR-3 (n = 37), LR-4 (n = 41), and LR-M (n = 36) on CT/MRI and LR-3 (n = 48), LR-4 (n = 36), LR-M (n = 29), and LR-TIV (n = 1) on CEUS. Of them, 43.0% (49/114) were confirmed as HCC, 37.3% (43/114) non-malignant, and 19.3% (22/114) non-hepatocellular malignancies. The highest diagnostic accuracy among the combinations of imaging features was achieved in CT/MRI LR-3 observations, where the combination of CEUS arterial phase hyper-enhancement (APHE) + CT/MRI APHE had 96.7% specificity, 75.0% positive predictive value (PPV), and 86.5% accuracy for HCC.

CONCLUSION

The combination of LI-RADS major features on CT/MRI and CEUS showed higher specificity, PPV, and accuracy compared to individual modalities' assessments, particularly for CT/MRI LR-3 observations.

Do Risk Factors for HCC Impact the Association of CT/MRI LIRADS Major Features With HCC? An Individual Participant Data Meta-Analysis

Background: Guidelines suggest the Liver Imaging Reporting and Data System (LI-RADS) may not be applicable for some populations at risk for hepatocellular carcinoma (HCC). However, data assessing the association of HCC risk factors with LI-RADS major features are lacking. Purpose: To evaluate whether the association between HCC risk factors and each CT/MRI LI-RADS major feature differs among individuals at-risk for HCC. Methods: Databases (MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scopus) were searched from 2014 to 2022. Individual participant data (IPD) were extracted from studies evaluating HCC diagnosis using CT/MRI LI-RADS and reporting HCC risk factors. IPD from studies were pooled and modelled with one-stage meta-regressions. Interactions were assessed between major features and HCC risk factors, including age, sex, cirrhosis, chronic hepatitis B virus (HBV), and study location. A mixed effects model that included the major features, as well as separate models that included interactions between each risk factor and each major feature, were fit. Differences in interactions across levels of each risk factor were calculated using adjusted odds-ratios (ORs), 95% confidence-intervals (CI), and z-tests. Risk of bias was assessed using QUADAS-2. (Protocol: https://osf.io/tdv7j/). Results: Across 23 studies (2958 patients and 3553 observations), the associations between LI-RADS major features and HCC were consistent across several HCC risk factors (P-value range: .09-.99). A sensitivity analysis among the 4 studies with a low risk of bias did not differ from the primary analysis. Conclusion: The association between CT/MRI LI-RADS major features and HCC risk factors do not significantly differ in individuals at-risk for HCC. These findings suggest that CT/MR LI-RADS should be applied to all patients considered at risk by LI-RADS without modification or exclusions, regardless of the presence or absence of the risk factors evaluated in this study.

ESR Essentials: diagnosis of hepatocellular carcinoma-practice recommendations by ESGAR

Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy and a leading cause of cancer related death worldwide. Current guidelines for the noninvasive diagnosis of HCC are provided by the European Association for the Study of the Liver (EASL), the American Association for the Study of Liver Diseases (AASLD) which endorsed the Liver Imaging Reporting and Data System (LI-RADS) algorithm, the Korean Liver Cancer Association-National Cancer Center (KLCA-NCC), and the Asian-Pacific Association for the Study of the Liver (APASL). These allow the diagnosis of HCC in high-risk patients in the presence of typical imaging features on contrast-enhanced CT, MRI, or contrast-enhanced ultrasound. Size, non-rim arterial phase hyperenhancement, non-peripheral washout, enhancing capsule, and growth are major imaging features and they should be combined for the diagnosis of HCC. This article provides concise and relevant practice recommendations aimed at general radiologist audience, summarizing the best practice and informing on the essential imaging criteria for the diagnosis of HCC, while also discussing the high-risk population criteria, imaging modalities, and imaging features according to the current guidelines. KEY POINTS: • Noninvasive diagnosis of hepatocellular carcinoma (HCC) can be provided only in patients at high risk. • Contrast-enhanced CT or MRI are the first-line imaging exams for the diagnosis of HCC. • Major imaging features should be combined to provide the diagnosis of definitive HCC.

Liver Imaging Reporting and Data System version 2018 category 5 for diagnosing hepatocellular carcinoma: an updated meta-analysis

OBJECTIVE

We performed an updated meta-analysis to determine the diagnostic performance of Liver Imaging Reporting and Data System (LI-RADS, LR) 5 category for hepatocellular carcinoma (HCC) using LI-RADS version 2018 (v2018), and to evaluate differences by imaging modalities and type of MRI contrast material.

METHODS

The MEDLINE and Embase databases were searched for studies reporting the performance of LR-5 using v2018 for diagnosing HCC. A bivariate random-effects model was used to calculate the pooled per-observation sensitivity and specificity. Subgroup analysis was performed based on imaging modalities and type of MRI contrast material.

RESULTS

Forty-eight studies qualified for the meta-analysis, comprising 9031 patients, 10,547 observations, and 7216 HCCs. The pooled per-observation sensitivity and specificity of LR-5 for diagnosing HCC were 66% (95% CI, 61-70%) and 91% (95% CI, 89-93%), respectively. In the subgroup analysis, MRI with extracellular agent (ECA-MRI) showed significantly higher pooled sensitivity (77% [95% CI, 70-82%]) than CT (66% [95% CI, 58-73%]; p = 0.023) or MRI with gadoxetate (Gx-MRI) (65% [95% CI, 60-70%]; p = 0.001), but there was no significant difference between ECA-MRI and MRI with gadobenate (gadobenate-MRI) (73% [95% CI, 61-82%]; p = 0.495). Pooled specificities were 88% (95% CI, 80-93%) for CT, 92% (95% CI, 86-95%) for ECA-MRI, 93% (95% CI, 91-95%) for Gx-MRI, and 91% (95% CI, 84-95%) for gadobenate-MRI without significant differences (p = 0.084-0.803).

CONCLUSIONS

LI-RADS v2018 LR-5 provides high specificity for HCC diagnosis regardless of modality or contrast material, while ECA-MRI showed higher sensitivity than CT or Gx-MRI.

CLINICAL RELEVANCE STATEMENT

Refinement of the criteria for improving sensitivity while maintaining high specificity of LR-5 for HCC diagnosis may be an essential future direction.

KEY POINTS

• The pooled per-observation sensitivity and specificity of LR-5 for diagnosing HCC using LI-RADSv2018 were 66% and 91%, respectively. • ECA-MRI showed higher sensitivity than CT (77% vs 66%, p = 0.023) or Gx-MRI (77% vs 65%, p = 0.001). • LI-RADS v2018 LR-5 provides high specificity (88-93%) for HCC diagnosis regardless of modality or contrast material type.

Liver imaging reporting and data system diagnostic performance in hepatocellular carcinoma when modifying the definition of "washout" on gadoxetic acid-enhanced magnetic resonance imaging

BACKGROUND AND STUDY AIMS

The sensitivity of the Liver Imaging Reporting and Data System (LI-RADS) in the diagnosis of hepatocellular carcinoma (HCC) on gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) was suboptimal. This study evaluated the LI-RADS diagnostic performance in HCC when modifying the definition of washout using the transition phase (TP) or hepatobiliary phase (HBP) hypointensity on EOB-MRI.

PATIENTS AND METHODS

This retrospective study included patients at high risk of HCC who underwent EOB-MRI from June 2016 to June 2021. Three modified LI-RADS (mLI-RADS) algorithms were formulated according to different definitions of washout as follows: (a) portal venous phase (PVP) or TP hypointensity, (b) PVP or HBP hypointensity, and (c) PVP or TP or HBP hypointensity. Diagnostic performance, including sensitivity, specificity, and accuracy, was compared between mLI-RADS and LI-RADS v2018 using McNemar's test.

RESULTS

A total of 379 patients with 426 pathologically confirmed hepatic observations (250 HCCs, 88 nonHCC malignancies, and 88 benign lesions) were included in our study. The sensitivity rates of mLI-RADS a-c (80.0 %, 80.8 %, and 80.8 %) were all higher than that of LI-RADS v2018 (74.4 %) (all p < 0.05). The specificity rates of mLI-RADS a-c (86.9 %, 85.8 %, and 85.8 %) were all slightly lower than that of LI-RADS v2018 (88.6 %), although no statistically significant difference was noted (all p > 0.05). The accuracies of the three mLI-RADS algorithms were the same and were all higher than that of LI-RADS v2018 (82.9 % vs. 80.3 %, all p < 0.05).

CONCLUSION

When the definition of washout appearance was extended to TP or HBP hypointensity on EOB-MRI, the diagnostic sensitivity of LI-RADS for HCC improved without decreasing specificity.

LI-RADS CT and MRI Ancillary Feature Association with Hepatocellular Carcinoma and Malignancy: An Individual Participant Data Meta-Analysis

Background The independent contribution of each Liver Imaging Reporting and Data System (LI-RADS) CT or MRI ancillary feature (AF) has not been established. Purpose To evaluate the association of LI-RADS AFs with hepatocellular carcinoma (HCC) and malignancy while adjusting for LI-RADS major features through an individual participant data (IPD) meta-analysis. Materials and Methods Medline, Embase, Cochrane Central Register of Controlled Trials, and Scopus were searched from January 2014 to January 2022 for studies evaluating the diagnostic accuracy of CT and MRI for HCC using LI-RADS version 2014, 2017, or 2018. Using a one-step approach, IPD across studies were pooled. Adjusted odds ratios (ORs) and 95% CIs were derived from multivariable logistic regression models of each AF combined with major features except threshold growth (excluded because of infrequent reporting). Liver observation clustering was addressed at the study and participant levels through random intercepts. Risk of bias was assessed using a composite reference standard and Quality Assessment of Diagnostic Accuracy Studies 2. Results Twenty studies comprising 3091 observations (2456 adult participants; mean age, 59 years ± 11 [SD]; 1849 [75.3%] men) were included. In total, 89% (eight of nine) of AFs favoring malignancy were associated with malignancy and/or HCC, 80% (four of five) of AFs favoring HCC were associated with HCC, and 57% (four of seven) of AFs favoring benignity were negatively associated with HCC and/or malignancy. Nonenhancing capsule (OR = 3.50 [95% CI: 1.53, 8.01]) had the strongest association with HCC. Diffusion restriction (OR = 14.45 [95% CI: 9.82, 21.27]) and mild-moderate T2 hyperintensity (OR = 10.18 [95% CI: 7.17, 14.44]) had the strongest association with malignancy. The strongest negative associations with HCC were parallels blood pool enhancement (OR = 0.07 [95% CI: 0.01, 0.49]) and marked T2 hyperintensity (OR = 0.18 [95% CI: 0.07, 0.45]). Seventeen studies (85%) had a high risk of bias. Conclusion Most LI-RADS AFs were independently associated with HCC, malignancy, or benignity as intended when adjusting for major features. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Crivellaro in this issue.

Threshold growth has a limited role in differentiating hepatocellular carcinoma from other focal hepatic lesions

BACKGROUND AND OBJECTIVE: The role of threshold growth, as one of the major features (MFs) of hepatocellular carcinoma (HCC) in the Liver Imaging Reporting and Data System (LI-RADS) is inconsistent. This study evaluated the LI-RADS diagnostic performance for HCC when threshold growth was removed or replaced by independently significant ancillary features (AFs). MATERIALS AND METHODS: This retrospective institutional review board-approved study included patients with a high HCC risk who underwent gadoxetic acid-enhanced MRIs. The MRI findings were consistent with pathologically proven focal hepatic observations. The pathological results were used as the gold standard reference. The sizes of the lesions with and without threshold growth were compared. Univariate and multivariate logistic regression analyses were used to confirm the independently significant AFs of HCC. In addition to the classification criteria of LI-RADS version 2018 (LI-RADS v2018), the lesions were also reclassified according to the following two schemes: scheme A, using all MFs except threshold growth, with threshold growth feature treated as an AF favouring malignancy; and scheme B, replacing the threshold growth feature with independently significant AFs and treated them as new MFs. The diagnostic performance of the above two LI-RADS schemes for HCC was calculated and compared with that of LI-RADS v2018. RESULTS: A total of 379 patients and 426 observations were included. Threshold growth was not an independent significant MF for HCC diagnosis [odds ratio (OR), 1.0; 95% confidence interval (CI), 0.6-1.8; p = 0.927]. For all three groups of observations (HCCs, non-HCC malignancies, and benign lesions), the mean size with threshold growth was smaller than that without threshold growth (all p < 0.05). The nodule-in-nodule feature was an independent significant AF (OR, 9.8; 95% CI, 1.2-79.3; p = 0.032) and was used to replace threshold growth as a new MF in scheme B. The sensitivities of schemes A and B were 74.0% and 75.6%, respectively. The specificities of schemes A and B were the same (88.6%). None of the diagnostic performance metrics for HCC (sensitivity, specificity, accuracy) of either scheme A or B was significantly different from those of LI-RADS v2018 (all p > 0.05). CONCLUSION: Threshold growth is not an independently significant MF for HCC diagnosis. The diagnostic performance of LI-RADS for HCC is not affected regardless of whether threshold growth is removed from the list of MFs or replaced with an independently significant and more HCC-specific AF, which is the nodule-in-nodule feature.

Assessment of LI-RADS efficacy in classification of hepatocellular carcinoma and benign liver nodules using DCE-MRI features and machine learning

Purpose

The current study aimed to evaluate the efficiency of dynamic contrast-enhanced (DCE) MRI visual features in classifying benign liver nodules and hepatocellular carcinoma (HCC) using a machine learning model.

Methods

115 LI-RADS3, 137 LI-RADS4, and 140 LI-RADS5 nodules were included (392 nodules from 245 patients), which were evaluated by follow-up imaging for LR-3 and pathology results for LR-4 and LR-5 nodules. Data was collected retrospectively from 3 T and 1.5 T MRI scanners. All the lesions were categorized into 124 benign and 268 HCC lesions. Visual features included tumor size, arterial-phase hyper-enhancement (APHE), washout, lesion segment, mass/mass-like, and capsule presence. Gini-importance method extracted the most important features to prevent over-fitting. Final dataset was split into training(70%), validation(10%), and test dataset(20%). The SVM model was used to train the classifying algorithm. For model validation, 5-fold cross-validation was utilized, and the test data set was used to assess the final accuracy. The area under the curve and receiver operating characteristic curves were used to assess the performance of the classifier model.

Results

For test dataset, the accuracy, sensitivity, and specificity values for classifying benign and HCC lesions were 82%,84%, and 81%, respectively. APHE, washout, tumor size, and mass/mass-like features significantly differentiated benign and HCC lesions with p-value < .001.

Conclusions

The developed classification model employing DCE-MRI features showed significant performance of visual features in classifying benign and HCC lesions. Our study also highlighted the significance of mass and mass-like features in addition to LI-RADS categorization. For future work, this study suggests developing a deep-learning algorithm for automatic lesion segmentation and feature assessment to reduce lesion categorization errors.

Individual Participant Data Meta-Analysis of LR-5 in LI-RADS Version 2018 versus Revised LI-RADS for Hepatocellular Carcinoma Diagnosis

Background A simplification of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 (v2018), revised LI-RADS (rLI-RADS), has been proposed for imaging-based diagnosis of hepatocellular carcinoma (HCC). Single-site data suggest that rLI-RADS category 5 (rLR-5) improves sensitivity while maintaining positive predictive value (PPV) of the LI-RADS v2018 category 5 (LR-5), which indicates definite HCC. Purpose To compare the diagnostic performance of LI-RADS v2018 and rLI-RADS in a multicenter data set of patients at risk for HCC by performing an individual patient data meta-analysis. Materials and Methods Multiple databases were searched for studies published from January 2014 to January 2022 that evaluated the diagnostic performance of any version of LI-RADS at CT or MRI for diagnosing HCC. An individual patient data meta-analysis method was applied to observations from the identified studies. Quality Assessment of Diagnostic Accuracy Studies version 2 was applied to determine study risk of bias. Observations were categorized according to major features and either LI-RADS v2018 or rLI-RADS assignments. Diagnostic accuracies of category 5 for each system were calculated using generalized linear mixed models and compared using the likelihood ratio test for sensitivity and the Wald test for PPV. Results Twenty-four studies, including 3840 patients and 4727 observations, were analyzed. The median observation size was 19 mm (IQR, 11-30 mm). rLR-5 showed higher sensitivity compared with LR-5 (70.6% [95% CI: 60.7, 78.9] vs 61.3% [95% CI: 45.9, 74.7]; P < .001), with similar PPV (90.7% vs 92.3%; P = .55). In studies with low risk of bias (n = 4; 1031 observations), rLR-5 also achieved a higher sensitivity than LR-5 (72.3% [95% CI: 63.9, 80.1] vs 66.9% [95% CI: 58.2, 74.5]; P = .02), with similar PPV (83.1% vs 88.7%; P = .47). Conclusion rLR-5 achieved a higher sensitivity for identifying HCC than LR-5 while maintaining a comparable PPV at 90% or more, matching the results presented in the original rLI-RADS study. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sirlin and Chernyak in this issue.

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics

ABSTRACT

With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

Inter-observer agreement and accuracy of LI-RADS v2018 for differentiating tumor in vein from bland thrombus using gadoxetic acid-enhanced magnetic resonance imaging

PURPOSE

To assess inter-observer agreement and accuracy of LI-RADS v2018 for differentiating tumor in vein (TIV) from bland thrombus on gadoxetic acid-enhanced magnetic resonance imaging (Gx-MRI). Secondarily, to determine whether a multi-feature model improves accuracy compared to LI-RADS.

METHODS

We retrospectively identified consecutive patients at risk for hepatocellular carcinoma with venous occlusion(s) reported on Gx-MRI. Five radiologists independently classified each occlusion as TIV or bland thrombus using the LI-RADS TIV criterion (enhancing soft tissue in vein). They also evaluated imaging features suggestive of TIV or bland thrombus. Intra-class correlation coefficient (ICC) was calculated for individual features. A multi-feature model was developed based on consensus scores of features with > 5% consensus prevalence and > 0.40 ICC. Sensitivity and specificity of the LI-RADS criterion and of the cross-validated multi-feature model were compared.

RESULTS

Ninety-eight patients with 103 venous occlusions (58 TIV, 45 bland thrombus) were included. The LI-RADS criterion provided 0.63 ICC and, depending on the reader, 0.62-0.93 sensitivity and 0.87-1.00 specificity. Five other features had > 5% consensus prevalence and > 0.40 ICC, including three LI-RADS suggestive features and two non-LI-RADS features. The optimal multi-feature model incorporated the LI-RADS criterion and one LI-RADS suggestive feature (occluded or obscured vein contiguous with malignant parenchymal mass). After cross-validation, the multi-feature model did not improve sensitivity or specificity compared to the LI-RADS criterion (P = 0.23 and 0.25, respectively).

CONCLUSION

Using Gx-MRI, the LI-RADS criterion for TIV provides substantial inter-observer agreement, variable sensitivity, and high specificity for differentiating TIV from bland thrombus. A cross-validated multi-feature model did not improve diagnostic performance.

The role of enhancing capsule and modified capsule appearances in LI-RADS for diagnosing HCC ≤ 3.0 cm on gadoxetate disodium-enhanced MRI

OBJECTIVES

To evaluate the value of using enhancing capsule (EC) or modified capsule appearance as a major feature in LI-RADS for diagnosing HCC ≤ 3.0 cm on gadoxetate disodium-enhanced MRI (Gd-EOB-MRI), and to explore the relationship between the imaging features and the histological fibrous capsule.

METHODS

This retrospective study enrolled 342 hepatic lesions ≤ 3.0 cm in 319 patients that underwent Gd-EOB-MRIs from January 2018 to March 2021. During dynamic phases and hepatobiliary phase, the modified capsule appearance added the nonenhancing capsule (NEC) (modified LI-RADS + NEC) or corona enhancement (CoE) (modified LI-RADS + CoE) to EC as an alternative capsule appearance. Inter-reader agreement of imaging features was assessed. The diagnostic performances of LI-RADS, LI-RADS with EC ignored, and two modified LI-RADS were compared, followed by Bonferroni correction. Multivariable regression analysis was performed to identify the independent features associated with the histological fibrous capsule.

RESULTS

The inter-reader agreement on EC (0.64) was lower than that on the NEC alternative (0.71) but better than that on CoE alternative (0.58). For HCC diagnosis, compared to LI-RADS, LI-RADS with EC ignored showed significantly lower sensitivity (72.7% vs. 67.4%, p < 0.001) with comparable specificity (89.3% vs. 90.7%, p = 1.000). Two modified LI-RADS showed slightly higher sensitivity and lower specificity than LI-RADS, without statistical significance (all p ≥ 0.006). The AUC was highest with modified LI-RADS + NEC (0.82). Both EC and NEC were significantly associated with the fibrous capsule (p < 0.05).

CONCLUSION

EC appearance improved the diagnostic sensitivity of LI-RADS for HCC ≤ 3.0 cm on Gd-EOB-MRI. Considering NEC as an alternative capsule appearance allowed for better inter-reader reliability and comparable diagnostic ability.

KEY POINTS

• Using the enhancing capsule as a major feature in LI-RADS significantly improved the sensitivity of diagnosing HCC ≤ 3.0 cm without reducing specificity on gadoxetate disodium-enhanced MRI. • Compared to the corona enhancement, the nonenhancing capsule might be a preferable alternative capsule appearance for diagnosing HCC ≤ 3.0 cm. • Capsule appearance should be considered a major feature in LI-RADS for diagnosing HCC ≤ 3.0 cm, regardless whether the capsule appears to be enhancing or nonenhancing.

LI-RADS v2018: utilizing ancillary features on gadoxetic acid-enhanced MRI to improve the diagnostic performance of small hapatocellular carcinoma (≤ 20 mm)

PURPOSE

To evaluate the role of ancillary features (AFs) of Liver Imaging Reporting and Data System (LI-RADS) in the diagnostic performance of small HCC (≤ 20 mm) on gadoxetic acid-enhanced MRI.

METHODS

A total of 154 patients with 183 hepatic observations were analysed in this retrospective study. Observations were categorized using only major features (MFs) and combined MFs and AFs. Independently significant AFs were identified through logistic regression analysis, and upgraded LR-5 criteria were developed using these as new MFs. The diagnostic performance of the modified LI-RADS (mLI-RADS) was calculated and compared with that of LI-RADS v2018 using McNemar's test.

RESULTS

Restricted diffusion, transitional and hepatobiliary phase hypointensity were independently significant AFs. The mLI-RADS a, c, e, g, h and i (upgraded LR-4 lesions that were categorized using only MFs to LR-5 using a certain or any one, two, three of the above AFs as new MFs) yielded a significantly greater sensitivity than that of the LI-RADS v2018 (68.0%, 69.1%, 69.1%, 69.1%, 69.1%, 68.0% vs. 61.9%, all p < 0.05), whereas the specificities were not significantly different (84.9%, 86.0%, 84.9%, 83.7%, 84.9%, 87.2% vs. 88.4% all p > 0.05). When independently significant AFs were used to upgrade the LR-4 nodules categorized by combined MFs and AFs (mLI-RADS b, d and f), the sensitivities were improved, but the specificities were decreased (all p < 0.05).

CONCLUSIONS

Independently significant AFs may be used to upgrade an observation from LR-4 (categorized only using MFs) to LR-5, which can improve diagnostic performance for small HCC.

Laparoscopic Microwave Ablation: Which Technologies Improve the Results

Liver resection is the best treatment for hepatocellular carcinoma (HCC) when resectable. Unfortunately, many patients with HCC cannot undergo liver resection. Percutaneous thermoablation represents a valid alternative for inoperable neoplasms and for small HCCs, but it is not always possible to accomplish it. In cases where the percutaneous approach is not feasible (not a visible lesion or in hazardous locations), laparoscopic thermoablation may be indicated. HCC diagnosis is commonly obtained from imaging modalities, such as CT and MRI, However, the interpretation of radiological images, which have a two-dimensional appearance, during the surgical procedure and in particular during laparoscopy, can be very difficult in many cases for the surgeon who has to treat the tumor in a three-dimensional environment. In recent years, more technologies have helped surgeons to improve the results after ablative treatments. The three-dimensional reconstruction of the radiological images has allowed the surgeon to assess the exact position of the tumor both before the surgery (virtual reality) and during the surgery with immersive techniques (augmented reality). Furthermore, indocyanine green (ICG) fluorescence imaging seems to be a valid tool to enhance the precision of laparoscopic thermoablation. Finally, the association with laparoscopic ultrasound with contrast media could improve the localization and characteristics of tumor lesions. This article describes the use of hepatic three-dimensional modeling, ICG fluorescence imaging and laparoscopic ultrasound examination, convenient for improving the preoperative surgical preparation for personalized laparoscopic approach.

Diagnostic performance of CT versus MRI Liver Imaging Reporting and Data System category 5 for hepatocellular carcinoma: a systematic review and meta-analysis of comparative studies

OBJECTIVE

To compare the performance of Liver Imaging Reporting and Data System category 5 (LR-5) for diagnosing HCC between CT and MRI using comparative studies.

METHODS

The MEDLINE and EMBASE databases were searched from inception to April 21, 2021, to identify studies that directly compare the diagnostic performance of LR-5 for HCC between CT and MRI. A bivariate random-effects model was fitted to calculate the pooled per-observation sensitivity and specificity of LR-5 of each modality, and compare the pooled estimates of paired data. Subgroup analysis was performed according to the MRI contrast agent.

RESULTS

Seven studies with 1145 observations (725 HCCs) were included in the final analysis. The pooled per-observation sensitivity of LR-5 for diagnosing HCC was higher using MRI (61%; 95% confidence interval [CI], 43-76%; I² = 95%) than CT (48%; 95% CI, 31-65%; I² = 97%) (p < 0.001). The pooled per-observation specificities of LR-5 did not show statistically significant difference between CT (96%; 95% CI, 92-98%; I² = 0%) and MRI (93%; 95% CI, 88-96%; I² = 16%) (p = 0.054). In the subgroup analysis, extracellular contrast agent-enhanced MRI showed significantly higher pooled per-observation sensitivity than gadoxetic acid-enhanced MRI for diagnosing HCC (73% [95% CI, 55-85%] vs. 55% [95% CI, 39-70%]; p = 0.007), without a significant difference in specificity (93% [95% CI, 80-98%] vs. 94% [95% CI, 87-97%]; p = 0.884).

CONCLUSIONS

The LR-5 of MRI showed significantly higher pooled per-observation sensitivity than CT for diagnosing HCC. The pooled per-observation specificities of LR-5 were comparable between the two modalities.

KEY POINTS

• The pooled sensitivity of LR-5 using MRI was higher than that using CT (61% versus 48%), but the pooled specificities of LR-5 were not significantly different between CT and MRI (96% versus 93%). • Subgroup analysis according to the MRI contrast media showed a significantly higher pooled per-observation sensitivity using ECA-enhanced MRI than with EOB-enhanced MRI (73% versus 55%), and comparable specificities (93% versus 94%). • Although LI-RADS provides a common diagnostic algorithm for CT or MRI, the per-observation performance of LR-5 can be affected by the imaging modality as well as the MRI contrast agent.

A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls

Liver cancer is the sixth most detected tumor and the third leading cause of tumor death worldwide. Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with specific risk factors and a targeted population. Imaging plays a major role in the management of HCC from screening to post-therapy follow-up. In order to optimize the diagnostic-therapeutic management and using a universal report, which allows more effective communication among the multidisciplinary team, several classification systems have been proposed over time, and LI-RADS is the most utilized. Currently, LI-RADS comprises four algorithms addressing screening and surveillance, diagnosis on computed tomography (CT)/magnetic resonance imaging (MRI), diagnosis on contrast-enhanced ultrasound (CEUS) and treatment response on CT/MRI. The algorithm allows guiding the radiologist through a stepwise process of assigning a category to a liver observation, recognizing both major and ancillary features. This process allows for characterizing liver lesions and assessing treatment. In this review, we highlighted both major and ancillary features that could define HCC. The distinctive dynamic vascular pattern of arterial hyperenhancement followed by washout in the portal-venous phase is the key hallmark of HCC, with a specificity value close to 100%. However, the sensitivity value of these combined criteria is inadequate. Recent evidence has proven that liver-specific contrast could be an important tool not only in increasing sensitivity but also in diagnosis as a major criterion. Although LI-RADS emerges as an essential instrument to support the management of liver tumors, still many improvements are needed to overcome the current limitations. In particular, features that may clearly distinguish HCC from cholangiocarcinoma (CCA) and combined HCC-CCA lesions and the assessment after locoregional radiation-based therapy are still fields of research.

A gadoxetic acid-enhanced MRI-based multivariable model using LI-RADS v2018 and other imaging features for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma

PURPOSE

To identify imaging features of macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) using LI-RADS v2018 and other imaging features and to develop a gadoxetic acid-enhanced MRI (EOB-MRI)-based model for pretreatment prediction of MTM-HCC.

MATERIALS AND METHODS

A total of 93 patients with pathologically proven HCC (39 MTM-HCC and 54 non-MTM-HCC) were retrospectively evaluated with EOB-MRI at 3 T. Imaging analysis according to LI-RADS v2018 was evaluated by two readers. Univariate and multivariate analyses were performed to determine independent predictors for MTM-HCC. Different logistic regression models were built based on MRI features, including model A (enhancing capsule, blood products in mass and ascites), model B (enhancing capsule and ascites), model C (blood products in mass and ascites), and model D (blood products in mass and enhancing capsule). Diagnostic performance was assessed by receiver operating characteristic (ROC) curves.

RESULTS

After multivariate analysis, absence of enhancing capsule (odds ratio = 0.102, p = 0.010), absence of blood products in mass (odds ratio = 0.073, p = 0.030), and with ascites (odds ratio = 55.677, p = 0.028) were identified as independent differential factors for the presence of MTM-HCC. Model A yielded a sensitivity, specificity, and AUC of 35.90% (21.20,52.80), 94.44% (84.60, 98.80), and 0.731 (0.629, 0.818). Model A achieved a comparable AUC than model D (0.731 vs. 0.699, p = 0.333), but a higher AUC than model B (0.731 vs. 0.644, p = 0.048) and model C (0.731 vs. 0.650, p = 0.005).

CONCLUSION

The EOB-MRI-based model is promising for noninvasively predicting MTM-HCC and may assist clinicians in pretreatment decisions.

The added value of artificial intelligence to LI-RADS categorization: A systematic review

PURPOSE

The objective of this systematic review was to critically assess the available literature on deep learning (DL) and radiomics applied to the Liver Imaging Reporting and Data System (LI-RADS) in terms of 1) automatic LI-RADS classification of liver nodules; 2) the contribution of DL and radiomics to human evaluation in the classification of liver nodules following LI-RADS protocol.

MATERIALS AND METHODS

A literature search was conducted to identify original research studies published up to April 2021. The inclusion criteria were: English language, focus on computed tomography (CT) and/or magnetic resonance (MR) with specified number of patients and lesions, adoption of LI-RADS classification for the detected hepatic lesions, and application of AI in the classification of liver nodules. Review articles, conference papers, editorials and commentaries, animal studies or studies with absence of AI and/or LI-RADS were excluded. After screening 221 articles, 11 studies were included in this review.

RESULTS

All the included studies proved that DL and radiomics have high performances in liver nodules classification, sometimes similar or better than human evaluation. The best performances of DL was an AUC of 0.95 on MR and the best performance of radiomics was AUC of 0.98 either on CT and MR, while the lower ones were respectively AUC of 0.63 either on CT and MR for DL and AUC of 0.70 on CT for radiomics.

CONCLUSION

DL and radiomics could be a useful tool in assisting radiologists in the diagnosis and classification of liver nodules according to LI-RADS.

Enhancing capsule in hepatocellular carcinoma: intra-individual comparison between CT and MRI with extracellular contrast agent

PURPOSE

The purpose of this study was to compare the value of contrast-enhanced computed tomography (CT) to that of magnetic resonance imaging obtained with extracellular contrast agent (ECA-MRI) for the diagnosis of a tumor capsule in hepatocellular carcinoma (HCC) using histopathologic findings as the standard of reference.

MATERIALS AND METHODS

This retrospective study included patients with pathologically-proven resected HCCs with available preoperative contrast-enhanced CT and ECA-MRI examinations. Two blinded radiologists independently reviewed contrast-enhanced CT and ECA-MRI examinations to assess the presence of an enhancing capsule. The histopathological analysis of resected specimens was used as reference for the diagnosis of a tumor capsule. The sensitivity and specificity of CT and ECA-MRI for the diagnosis of a tumor capsule were determined, and an intra-individual comparison of imaging modalities was performed using McNemar test. Inter-reader agreement was assessed using Kappa test.

RESULTS

The study population included 199 patients (157 men, 42 women; mean age: 61.3 ± 13.0 [SD] years) with 210 HCCs (mean size 56.7 ± 43.7 [SD] mm). A tumor capsule was present in 157/210 (74.8%) HCCs at histopathologic analysis. Capsule enhancement was more frequently visualized on ECA-MRI (R1, 68.6%; R2, 71.9%) than on CT (R1, 44.3%, P < 0.001; R2, 47.6%, P < 0.001). The sensitivity of ECA-MRI was better for the diagnosis of histopathological tumor capsule (R1, 76.4%; R2, 79.6%; P < 0.001), while CT had a greater specificity (R1, 84.9%; R2, 83.0%; P < 0.001). Inter-reader agreement was moderate both on CT (kappa = 0.55; 95% confidence interval [CI]: 0.43-0.66) and ECA-MRI (kappa = 0.57; 95% CI: 0.45-0.70).

CONCLUSION

Capsule enhancement was more frequently visualized on ECA-MRI than on CT. The sensitivity of ECA-MRI was greater than that of CT, but the specificity of CT was better than that of ECA-MRI.