Comparison of the accuracy of AASLD and LI-RADS criteria for the non-invasive diagnosis of HCC smaller than 3 cm

Risk of Bias in Liver Imaging Reporting and Data System Studies Using QUADAS-2

Purpose: Use a tailored version of the Quality Assessment of Diagnostic Accuracy Studies tool to evaluate risk of bias and applicability across LIRADS related publications. Method: A tailored QUADAS-2 tool was created through consensus approach to assess risk of bias and applicability across 37 LI-RADS related publications. Studies were selected from 2017 to 2022 using the assistance of experienced hospital librarians to search for studies evaluating the diagnostic accuracy of CT, MRI, or contrast-enhanced ultrasound for HCC using LI-RADS through multiple different databases. QUADAS-2 assessments were performed in duplicate and independently by 2 authors with experience using the QUADAS-2 tool. Disagreements were resolved with a third expert reviewer. Consensus QUADAS-2 assessments were tabulated for each domain. Results: Using the tailored QUADAS-2 tool, 31 of the 37 included LI-RADS studies were assessed as high risk of bias, and 9 out of 37 studies demonstrated concerns for applicability. Patient selection (21 out of 37 studies) and flow/timing (24 out of 37 studies) domains demonstrated the highest risk of bias. 6 out of 37 studies in the index domain demonstrated high risk of bias. 2 out of 37 studies showed high risk of bias in the reference standard domain. Conclusion: A significant proportion of LI-RADS research is at risk of bias with concerns for applicability. Identifying risk of bias in such research is essential to recognize limitations of a study that may affect the validity of the results. Areas for improvement in LI-RADS research include reducing selection bias, avoiding inappropriate exclusions, and decreasing verification bias.

Is concurrent LR-5 associated with a higher rate of hepatocellular carcinoma in LR-3 or LR-4 observations? An individual participant data meta-analysis

BACKGROUND

The Liver Imaging Reporting and Data System (LI-RADS) does not consider factors extrinsic to the observation of interest, such as concurrent LR-5 observations.

PURPOSE

To evaluate whether the presence of a concurrent LR-5 observation is associated with a difference in the probability that LR-3 or LR-4 observations represent hepatocellular carcinoma (HCC) through an individual participant data (IPD) meta-analysis.

METHODS

Multiple databases were searched from 1/2014 to 2/2023 for studies evaluating the diagnostic accuracy of CT/MRI for HCC using LI-RADS v2014/2017/2018. The search strategy, study selection, and data collection process can be found at https://osf.io/rpg8x . Using a generalized linear mixed model (GLMM), IPD were pooled across studies and modeled simultaneously with a one-stage meta-analysis approach to estimate positive predictive value (PPV) of LR-3 and LR-4 observations without and with concurrent LR-5 for the diagnosis of HCC. Risk of bias was assessed using a composite reference standard and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

RESULTS

Twenty-nine studies comprising 2591 observations in 1456 patients (mean age 59 years, 1083 [74%] male) were included. 587/1960 (29.9%) LR-3 observations in 1009 patients had concurrent LR-5. The PPV for LR-3 observations with concurrent LR-5 was not significantly different from the PPV without LR-5 (45.4% vs 37.1%, p = 0.63). 264/631 (41.8%) LR-4 observations in 447 patients had concurrent LR-5. The PPV for LR-4 observations with concurrent LR-5 was not significantly different from LR-4 observations without concurrent LR-5 (88.6% vs 69.5%, p = 0.08). A sensitivity analysis for low-risk of bias studies (n = 9) did not differ from the primary analysis.

CONCLUSION

The presence of concurrent LR-5 was not significantly associated with differences in PPV for HCC in LR-3 or LR-4 observations, supporting the current LI-RADS paradigm, wherein the presence of synchronous LR-5 may not alter the categorization of LR-3 and LR-4 observations.

Modified LR-5 criteria based on gadoxetic acid can improve the sensitivity in the diagnosis of hepatocellular carcinoma

BACKGROUND

Currently, only tumors classified as LR-5 are considered definitive hepatocellular carcinoma (HCC), and no further pathologic confirmation is required to initiate therapy. Previous studies have shown that the sensitivity of LR-5 is modest, and lesions enhanced by gadoxetic acid (Gd-EOB-DTPA) may exhibit lower sensitivity than those enhanced by Gd-DTPA.

AIM

To identify malignant ancillary features (AFs) that can independently and significantly predict HCC in Liver Imaging Reporting and Data System version 2018, and to develop modified LR-5 criteria to improve diagnostic performance on Gd-EOB-DTPA - enhanced magnetic resonance imaging.

METHODS

Imaging data from patients with HCC risk factors who underwent abdominal Gd-EOB-DTPA - enhanced magnetic resonance imaging were collected. Univariate and multivariate logistic regression analyses were performed to determine AFs that could independently and significantly predict HCC. The modified LR-5 criteria involved reclassifying LR-4/LR-3 lesions based on major features combined with independently significant AFs for HCC, or by substituting threshold growth with significant AFs. McNemar's test was used to compare the diagnostic performance of the modified LR-5 criteria.

RESULTS

A total of 244 lesions from 216 patients were included. Transitional phase hypointensity, mild - moderate T2 hyperintensity, and fat in mass (more than adjacent liver) were identified as significant independent predictors of HCC. Using the modified LR-5 criteria (e.g., LR-5-M1: LR-4 + transitional phase hypointensity; LR-5-M4: LR-5 by transitional phase hypointensity instead of threshold growth; LR-5-M5: LR-5 by mild - moderate T2 hyperintensity instead of threshold growth; LR-5-M8: LR-3/LR-4 + any two features of transitional phase hypointensity/mild - moderate T2 hyperintensity/fat in mass), sensitivities were significantly increased (88.5%-89.1%) compared to the standard LR-5 (60.6%; all P values < 0.05), while specificities (84.8%-89.9%) remained largely unchanged (93.7%; all P values > 0.05). The LR-5-M8 criterion achieved the highest sensitivity.

CONCLUSION

Mild - moderate T2 hyperintensity, transitional phase hypointensity, and fat in mass are independent and significant predictors of HCC malignant AFs. The modified LR-5 criteria can improve sensitivity without significantly reducing specificity.

LI-RADS for Diagnosing Hepatocellular Carcinoma in Patients with Noncirrhotic Chronic Hepatitis C

Background The Liver Imaging Reporting and Data System (LI-RADS) criteria have not been validated for patients with noncirrhotic chronic hepatitis C (CHC), who are at a greater risk for hepatocellular carcinoma (HCC) than the general population. Purpose To evaluate the diagnostic performance of LI-RADS category 5 (LR-5, indicating definite HCC) observations for HCC using CT and MRI in patients with noncirrhotic CHC and to compare these findings with those in patients with cirrhotic CHC. Materials and Methods This retrospective study included patients without cirrhosis with CHC with focal hepatic nodules of 1 cm or greater on dynamic CT or MRI scans who underwent pathologic confirmation at two university hospitals from August 2002 to February 2022. This group served as the test dataset. The primary outcome was the diagnostic performance of LR-5 for HCC using CT and MRI. When LI-RADS categorization differed between CT and MRI, the MRI-based classification was used as the definitive category. Results were validated using a dataset of patients with CHC from two additional hospitals based on the clinical composite reference standard. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Results The test dataset comprised 458 patients (mean age, 64 years ± 9 [SD]; 350 male; 219 without cirrhosis, 239 with cirrhosis). For noncirrhotic livers, the LR-5 criteria achieved an AUC, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.90 (95% CI: 0.86, 0.93), 85.1% (95% CI: 80.6, 89.7), 82.4% (95% CI: 77.0, 87.8), 97.6% (95% CI: 93.0, 100.0), 99.4% (95% CI: 98.2, 100.0), and 54.7% (95% CI: 43.4, 65.9), respectively. The AUC for LR-5 observations in diagnosing HCC was higher in the noncirrhotic liver group compared with the cirrhotic liver group (AUC, 0.90 [95% CI: 0.86, 0.93] vs 0.79 [95% CI: 0.74, 0.84]; P = .002). The diagnostic performance of the LR-5 criteria for diagnosing HCC was also excellent in patients with noncirrhotic CHC in the validation dataset, which included 155 lesions from 103 patients (mean age, 68 years ± 12; 146 male). The AUC, accuracy, sensitivity, specificity, PPV, and NPV in the validation dataset were 0.91 (95% CI: 0.84, 0.97), 96.1% (95% CI: 93.1, 99.2), 82.9% (95% CI: 70.4, 95.3), 100%, 100%, and 95.2% (95% CI: 91.5, 99.0), respectively. Conclusion The diagnostic performance of LR-5 for HCC in patients with noncirrhotic CHC was comparable to that in patients with cirrhosis across various clinical settings. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Schöllnast in this issue.

Opportunistic Detection of Hepatocellular Carcinoma Using Noncontrast CT and Deep Learning Artificial Intelligence

OBJECTIVE

Hepatocellular carcinoma (HCC) poses a heavy global disease burden; early diagnosis is critical to improve outcomes. Opportunistic screening-the use of imaging data acquired for other clinical indications for disease detection-as well as the role of noncontrast CT have been poorly investigated in the context of HCC. We aimed to develop an artificial intelligence algorithm for efficient and accurate HCC detection using solely noncontrast CTs.

METHODS

A 3-D convolutional block attention module (CABM) model was developed and trained on noncontrast multiphasic CT scans. HCC was diagnosed following American Association for the Study of Liver Disease guidelines and confirmed via 12-month clinical composite reference standard. CT observations were reviewed by radiologists; observations in at-risk patients were annotated via the Liver Imaging Reporting and Data System. Internal validation, independent external testing, and sensitivity analyses were performed to evaluate model performance and generalizability.

RESULTS

In all, 2,223 patients were included. The CBAM model achieved an area under the receiver operating curve (AUC) of 0.807 (95% confidence interval [CI] 0.772-0.841) on the internal validation cohort, comparable to radiological interpretation at 0.851 (95% CI 0.820-0.882). Among at-risk patients, cases with definite HCC outcomes, indeterminate scans, and scans with small lesions < 2 cm in size, the model attained AUCs of 0.769 (95% CI 0.721-0.817), 0.815 (95% CI 0.778-0.853), 0.769 (95% CI 0.704-0.834), and 0.773 (95% CI 0.692-0.854). On external testing cohort with 584 patients, the CBAM model achieved an AUC of 0.789 (95% CI 0.750-0.827).

DISCUSSION

The CBAM model achieved a diagnostic accuracy comparable to radiological interpretation during internal validation. Artificial intelligence analysis of noncontrast CTs has a potential role in HCC opportunistic screening.

Application value of different imaging methods in the early diagnosis of small hepatocellular carcinoma: a network meta-analysis

Objective

To determine the diagnostic value of ultrasound, multi-phase enhanced computed tomography, and magnetic resonance imaging of small hepatocellular carcinoma.

Methods

Experimental studies on diagnosing small hepatocellular carcinoma in four databases: PubMed, Cochrane Library, Web of Science, and Embase, were comprehensively searched from October 2007 to October 2024. Relevant diagnostic accuracy data were extracted and a Bayesian model that combined direct and indirect evidence was used for analysis.

Results

16 original studies were included and data from 2,447 patients were collated to assess the diagnostic value of 10 different methods. The methodological quality of the included studies was good and there was no obvious publication bias. The pooled DOR of all diagnostic methods was 19.61, which was statistically significant (I2 = 76.0%, P < 0.01, 95% CI:13.30 - 28.92). Normal US + CEUS + ultrasonic elastic imaging had the highest specificity (92.9), accuracy (93.6), and positive predictive value (94.4). Unenhanced MRI + Contrast-enhanced MRI had the highest sensitivity (96.6) and negative predictive value (96.6), but specificity (12.5) and positive predictive value (34.4) were extremely poor. Contrast-enhanced MRI had the highest diagnostic value in individual imaging methods (sensitivity: 66, specificity: 55.5, accuracy: 67.9, positive predictive value: 64.4, negative predictive value: 66.5). There was significant inconsistency and high heterogeneity in this study.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024507883.

Application of a deep learning algorithm for the diagnosis of HCC

Background & Aims

Hepatocellular carcinoma (HCC) is characterized by a high mortality rate. The Liver Imaging Reporting and Data System (LI-RADS) results in a considerable number of indeterminate observations, rendering an accurate diagnosis difficult.

Methods

We developed four deep learning models for diagnosing HCC on computed tomography (CT) via a training-validation-testing approach. Thin-slice triphasic CT liver images and relevant clinical information were collected and processed for deep learning. HCC was diagnosed and verified via a 12-month clinical composite reference standard. CT observations among at-risk patients were annotated using LI-RADS. Diagnostic performance was assessed by internal validation and independent external testing. We conducted sensitivity analyses of different subgroups, deep learning explainability evaluation, and misclassification analysis.

Results

From 2,832 patients and 4,305 CT observations, the best-performing model was Spatio-Temporal 3D Convolution Network (ST3DCN), achieving area under receiver-operating-characteristic curves (AUCs) of 0.919 (95% CI, 0.903-0.935) and 0.901 (95% CI, 0.879-0.924) at the observation (n = 1,077) and patient (n = 685) levels, respectively during internal validation, compared with 0.839 (95% CI, 0.814-0.864) and 0.822 (95% CI, 0.790-0.853), respectively for standard of care radiological interpretation. The negative predictive values of ST3DCN were 0.966 (95% CI, 0.954-0.979) and 0.951 (95% CI, 0.931-0.971), respectively. The observation-level AUCs among at-risk patients, 2-5-cm observations, and singular portovenous phase analysis of ST3DCN were 0.899 (95% CI, 0.874-0.924), 0.872 (95% CI, 0.838-0.909) and 0.912 (95% CI, 0.895-0.929), respectively. In external testing (551/717 patients/observations), the AUC of ST3DCN was 0.901 (95% CI, 0.877-0.924), which was non-inferior to radiological interpretation (AUC 0.900; 95% CI, 0.877--923).

Conclusions

ST3DCN achieved strong, robust performance for accurate HCC diagnosis on CT. Thus, deep learning can expedite and improve the process of diagnosing HCC.

Impact and implications

The clinical applicability of deep learning in HCC diagnosis is potentially huge, especially considering the expected increase in the incidence and mortality of HCC worldwide. Early diagnosis through deep learning can lead to earlier definitive management, particularly for at-risk patients. The model can be broadly deployed for patients undergoing a triphasic contrast CT scan of the liver to reduce the currently high mortality rate of HCC.

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.

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.

Multiparametric MRI for characterization of the tumour microenvironment

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

Mean ADC values and arterial phase hyperintensity discriminate small (≤ 3 cm) well-differentiated hepatocellular carcinoma from dysplastic nodule

BACKGROUND/AIM

This research endeavor sought to distinguish small (≤ 3 cm) well-differentiated hepatocellular carcinoma (WD-HCC) from dysplastic nodules (DN) by employing traditional imaging features and mean apparent diffusion coefficient (mADC) values derived from diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

In this retrospective analysis, we assessed a cohort of ninety patients with confirmed dysplastic nodules (DNs) (n = 71) or well-differentiated hepatocellular carcinoma (WD-HCC) (n = 41) who had undergone dynamic contrast-enhanced magnetic resonance imaging between March 2018 and June 2021. Multivariable logistic regression analyses were executed to pinpoint characteristics that can effectively differentiate histologic grades. A region-of-interest (ROI) encompassing all lesion voxels was delineated on each slice containing the mass in the ADC map. Subsequently, the whole-lesion mean ADC (mADC) were computed from these delineations. A receiver operating characteristic (ROC) curve was generated to assess the discriminatory efficacy of the mADC values in distinguishing between WD-HCC and DN.

RESULTS

Among the histopathological types from benign to malignant, mADC showed a significant decrease (P < 0.001). The mADCs were effective in distinguishing WD-HCC from DN [AUC, 0.903 (95% CI 0.849-0.958)]. The best cutoffs for the Youden index were 0.0012 mm2/s for mADC, with moderate sensitivity (70.7%) and high specificity (94.4%). MRI features including hyperintensity at arterial phase (odds ratio, 21.2; P = 0.009), mADC < 0.0012 mm2/s (odds ratio, 52.2; P < 0.001) were independent predictors for WD-HCC at multivariable analysis. The AUC value of hyperintensity at arterial phase was 0.857 (95% CI 0.786-0.928). The composite diagnostic criterion of arterial hyperintensity + mADC < 0.0012 mm2/s showed good performance [AUC, 0.926 (95% CI 0.878-0.975)], displaying increased sensitivity compared to individual assessments involving arterial hyperintensity (P = 0.013), mADC < 0.0012 mm2/s (P = 0.004), or LR-5 (P < 0.001), with similar specificity compared to LR-5 (P = 0.193).

CONCLUSION

DN and WD-HCC displayed contrasting diffusion characteristics, attainable to distinguish with satisfactory accuracy. The utilization of arterial phase hyperintensity and mADC < 0.0012 on MRI facilitated the differentiation of WD-HCC from DN.

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.

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.

Diagnostic performance of MRI for residual or recurrent hepatocellular carcinoma after locoregional treatment according to contrast agent type: a systematic review and meta‑analysis

PURPOSE

The ideal contrast agent for imaging patients with hepatocellular carcinoma (HCC) following locoregional therapies (LRT) remains uncertain. We conducted a meta-analysis to assess the diagnostic performance of magnetic resonance imaging with extracellular contrast agent (ECA-MRI) and hepatobiliary agent (EOB-MRI) in detecting residual or recurrence HCC following LRT.

METHODS

Original studies comparing the diagnostic performance of ECA-MRI and EOB-MRI were systematically identified through comprehensive searches in PubMed, EMBASE, Cochrane Library and Web of Science databases. The pooled sensitivity and specificity of ECA-MRI and EOB-MRI were calculated using a bivariate-random-effects model. Subgroup-analyses were conducted to compare the diagnostic performance of ECA-MRI and EOB-MRI according to different variables. Meta-regression analysis was employed to explore potential sources of study heterogeneity.

RESULTS

A total of 15 eligible studies encompassing 803 patients and 1018 lesions were included. Comparative analysis revealed no significant difference between ECA-MRI and EOB-MRI in the overall pooled sensitivity (87% vs. 79%) and specificity (92% vs. 96%) for the detection of residual or recurrent HCC after LRT (P = 0.41), with comparable areas under the HSROC of 0.95 and 0.92. Subgroup analyses indicated no significant diagnostic performance differences between ECA-MRI and EOB-MRI according to study design, type of LRT, most common etiology of liver disease, baseline lesion size, time of post-treated examination and MRI field strength (All P > 0.05).

CONCLUSION

ECA-MRI exhibited overall comparable diagnostic performance to EOB-MRI in assessing residual or recurrent HCC after LRT.

Radiological diagnosis of hepatocellular carcinoma does not preclude biopsy before treatment

Background & Aims

The diagnosis of hepatocellular carcinoma (HCC) in patients with cirrhosis relies on non-invasive criteria based on international guidelines. The advent of systemic therapies warrants reconsideration of the role of biopsy specimens in the diagnosis of HCC. Accordingly, we investigated the diagnostic performance of the LI-RADS 2018 and the AASLD 2011 criteria.

Methods

Consecutive patients with cirrhosis who underwent a biopsy for suspected HCC between 2015 and 2020 were included. The available imaging studies (computed tomography and/or magnetic resonance imaging) were blindly reviewed by two independent radiologists. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were assessed for LI-RADS, AASLD, and biopsies.

Results

In total, 167 patients underwent both available biopsy and imaging. Of the 137 relevant biopsies, 114 patients had HCC (83.2%), 12 (9%) had non-HCC malignant lesions, and 11 (8%) had benign nodules. The PPV and NPV of the biopsies were 100% and 62%, respectively; 30 biopsies were non-contributive. The PPV and NPV of the LI-RADS categories were 89% and 32.8% for LR-5 and 85.5% and 54.5% for LR-4 + 5 + TIV, respectively. The PPV and NPV of the 2011 AASLD criteria were 93.2% and 35.6%, respectively. The interobserver kappa (k = 0.380) for the LR-5 categories was reasonable. Of 100 LR-5 nodules, 11 were misclassified, in particular one case was a colorectal metastasis, and two cases were cholangiocarcinomas, of which nine were identified through biopsy, whereas six were correctly classified according to LI-RADS (LR-M or LR-TIV). Fifty percent of macrotrabecular HCC and 48.4% of poorly differentiated HCC (Edmonson 3 and 4) were not classified as LR-5.

Conclusions

LI-RADS 2018 did not outperform the AASLD 2011 score as a non-invasive diagnosis of HCC. Tumor biopsy allowed restoration of an accurate diagnosis in 11% of LR-5 cases. A combined radiological and histological diagnosis should be considered mandatory for good treatment assessment.

Impact and Implications

Although biopsy is not required for hepatocellular carcinoma diagnosis when the LI-RADS criteria are met according to current guidelines, our study underscores the limits of radiology and the need for biopsy when hepatocellular carcinoma is suspected. Histological findings could change therapeutics of liver tumors even if only for a small proportion of patients. Histological proof of the type of cancer is a standard in oncology.

Liver Imaging Reporting and Data System (LI-RADS) v2018: differential diagnostic value of ADC values for benign and malignant nodules with moderate probability (LR-3)

Objective

To evaluate the usefulness of the apparent diffusion coefficient (ADC) in differentiating between benign and malignant LR-3 lesions classified by Liver Imaging Reporting and Data System 2018 (LI-RADS v2018).

Methods

Retrospectively analyzed 88 patients with liver nodules confirmed by pathology and classified as LR-3 by LI-RADS. All patients underwent preoperative contrast-enhanced MR examination, and the following patient-related imaging features were collected: tumor size,nonrim APHE, nonperipheral "washout", enhancing "capsule", mild-moderate T2 hyperintensity, fat in mass, restricted diffusion, and nodule-in-nodule architecture. We performed ROC analysis and calculated the sensitivity and specificity.

Results

A total of 122 lesions were found in 88 patients, with 68 benign and 54 malignant lesions. The mean ADC value for malignant and benign lesions were 1.01 ± 0.15 × 103 mm2/s and 1.41 ± 0.31 × 103 mm2/s, respectively. The ADC value of malignant lesions was significantly lower than that of benign lesions, p < 0.0001. Compared with other imaging features, ADC values had the highest AUC (AUC = 0.909), with a sensitivity of 92.6% and a specificity of 74.1% for the differentiation of benign and malignant lesions.

Conclusions

ADC values are useful for differentiating between benign and malignant liver nodules in LR-3 classification, it improves the sensitivity of LI-RADS in the diagnosis of HCC while maintaining high specificity, and we recommend including ADC values in the standard interpretation of LI-RADSv2018.

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.

Hepatocellular carcinoma in patients cured of chronic hepatitis C: Minimal steatosis

BACKGROUND

Successful treatment of hepatitis C reduces liver inflammation and fibrosis; however, patients remain at risk of developing hepatocellular carcinoma (HCC).

AIMS

To identify risk factors for new-onset HCC in patients cured of hepatitis C.

METHODS

Imaging, histological, and clinical data on patients whose first HCC was diagnosed >12 months of post-SVR were analyzed. Histology of 20 nontumor tissues was analyzed in a blinded manner using the Knodel/Ishak/HAI system for necroinflammation and fibrosis/cirrhosis stage and the Brunt system for steatosis/steatohepatitis. Factors associated with post-SVR HCC were identified by comparison with HALT-C participants who did not develop post-SVR HCC.

RESULTS

Hepatocellular carcinoma was diagnosed in 54 patients (45 M/9F), a median of 6 years of post-SVR [interquartile range (IQR) =1.4-10y] at a median age of 61 years (IQR, 59-67). Approximately one-third lacked cirrhosis, and only 11% had steatosis on imaging. The majority (60%) had no steatosis/steatohepatitis in histopathology. The median HAI score was 3 (1.25-4), indicating mild necroinflammation. In a multivariable logistic regression model, post-SVR HCC was positively associated with non-Caucasian race (p = 0.03), smoking (p = 0.03), age > 60 years at HCC diagnosis (p = 0.03), albumin<3.5 g/dL (p = 0.02), AST/ALT>1 (p = 0.05), and platelets <100 × 103 cells/μL (p < 0.001). Alpha fetoprotein ≥4.75 ng/mL had 90% specificity and 71% sensitivity for HCC occurrence. Noncirrhotic patients had larger tumors (p = 0.002) and a higher prevalence of vascular invasion (p = 0.016) than cirrhotic patients.

CONCLUSIONS

One-third of patients with post-SVR HCC did not have liver cirrhosis; most had no steatosis/steatohepatitis. Hepatocellular carcinomas were more advanced in noncirrhotic patients. Results support AFP as a promising marker of post-SVR HCC risk.

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.

A new classification and regression tree algorithm: Improved diagnostic sensitivity for HCC ≤ 3.0 cm using gadoxetate disodium-enhanced MRI

PURPOSE

To develop and validate an effective algorithm, based on classification and regression tree (CART) analysis and LI-RADS features, for diagnosing HCC ≤ 3.0 cm with gadoxetate disodium‑enhanced MRI (Gd-EOB-MRI).

METHOD

We retrospectively included 299 and 90 high-risk patients with hepatic lesions ≤ 3.0 cm that underwent Gd-EOB-MRI from January 2018 to February 2021 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Through binary and multivariate regression analyses of LI-RADS features in the development cohort, we developed an algorithm using CART analysis, which comprised the targeted appearance and independently significant imaging features. On per-lesion basis, we compared the diagnostic performances of our algorithm, two previously reported CART algorithms, and LI-RADS LR-5 in development and validation cohorts.

RESULTS

Our CART algorithm, presenting as a decision tree, included targetoid appearance, HBP hypointensity, nonrim arterial phase hyperenhancement (APHE), and transitional phase hypointensity plus mild-moderate T2 hyperintensity. For definite HCC diagnosis, the overall sensitivity of our algorithm (development cohort 93.2%, validation cohort 92.5%; P < 0.006) was significantly higher than those of Jiang's algorithm modified LR-5 (defined as targetoid appearance, nonperipheral washout, restricted diffusion, and nonrim APHE) and LI-RADS LR-5, with the comparable specificity (development cohort: 84.3%, validation cohort: 86.7%; P ≥ 0.006). Our algorithm, providing the highest balanced accuracy (development cohort: 91.2%, validation cohort: 91.6%), outperformed other criteria for identifying HCCs from non-HCC lesions.

CONCLUSIONS

In high-risk patients, our CART algorithm developed with LI-RADS features showed promise for the early diagnosis of HCC ≤ 3.0 cm with Gd-EOB-MRI.

Percentages of Hepatocellular Carcinoma in LI-RADS Categories with CT and MRI: A Systematic Review and Meta-Analysis

Background The Liver Imaging Reporting and Data System (LI-RADS) CT and MRI algorithm applies equally to CT, MRI with extracellular contrast agents (ECA-MRI), and MRI with gadoxetate (Gx-MRI). Purpose To estimate pooled percentages of hepatocellular carcinoma (HCC) and overall malignancy for each LI-RADS category with CT and MRI. Materials and Methods MEDLINE and EMBASE databases were searched for research articles (January 2014-April 2021) reporting the percentages of observations in each LI-RADS category with use of versions 2014, 2017, or 2018. Study design, population characteristics, imaging modality, reference standard, and numbers of HCC and non-HCC malignancies in each category were recorded. A random-effects model evaluated the pooled percentage of HCC and overall malignancy for each category. Results There were 49 studies with 9620 patients and a total 11 562 observations, comprising 7921 HCCs, 1132 non-HCC malignancies, and 2509 benign entities. No HCC or non-HCC malignancies were reported with any modality in the LR-1 category. The pooled percentages of HCC for CT, ECA-MRI, and Gx-MRI, respectively, were 10%, 6%, and 1% for LR-2 (P = .16); 48%, 31%, and 38% for LR-3 (P = .42); 76%, 64%, and 77% for LR-4 (P = .62); 96%, 95%, and 96% for LR-5 (P = .76); 88%, 76%, and 78% for LR-5V or LR-TIV (tumor in vein) (P = .42); and 20%, 30%, and 35% for LR-M (P = .32). Most LR-M (93%-100%) and LR-5V or LR-TIV (99%-100%) observations were malignant, regardless of modality. Conclusion There was no difference in percentages of hepatocellular carcinoma and overall malignancy between CT, MRI with extracellular contrast agents, and MRI with gadoxetate for any Liver Imaging Reporting and Data System categories. © RSNA, 2023 Supplemental material is available for this article See also the editorial by Ronot in this issue.

Combination of radiofrequency ablation and percutaneous ethanol injection versus radiofrequency ablation alone for hepatocellular carcinoma: a systematic review and meta-analysis

INTRODUCTION

Radiofrequency ablation and percutaneous ethanol injection are important treatment modalities for hepatocellular carcinoma patients; Whether a combination treatment yields, additional benefit still remains controversial.

METHODS

A systematic review and meta-analysis was concluded. Randomized controlled trials published before January 1, 2022, from PubMed, EMBASE, Scopus, and CNKI were searched. Studies were excluded when patients received different ablative treatment or had serious liver dysfunction. The risk of bias assessment was evaluated using the Cochrane Collaboration's tool.

RESULTS

Ten studies, encompassing 854 patients, with histologically proven HCC were finally analyzed. The results demonstrated that patients who received RFA-PEI had slightly improvements in 1-year overall survival (OS) [risk ratio (RR): 1.11; 95% confidence interval (CI): 1.03, 1.19, I2 = 10%], 2-year OS (RR: 1.25; 95% CI: 1.12, 1.40, I2 = 0%), 3-year OS (RR: 1.42; 95% CI: 1.11, 1.83, I2 = 38%), 1-year local recurrence-free (LRF) proportion (RR: 1.2; 95% CI: 1.01, 1.42, I2 = 61%), and complete tumor necrosis (CTN) (RR: 1.32; 95% CI: 1.14, 1.53, I2 = 45%). Nevertheless, common complications, such as fever, were found to be significant (RR: 1.78, 95% CI: 1.13, 2.80).

CONCLUSION

Despite RFA-PEI appearing to be superior for HCC patients with a compensated liver in terms of OS, current evidence contained moderate to significant heterogeneity, and it was difficult to draw a definite conclusion regarding the therapeutic management in terms of LRF and CTN.

HCC or Something Else? Frequency of Various Benign and Malignant Etiologies in Cirrhotic Patients with Newly Detected Focal Liver Lesions in Relation to Different Clinical and Sonographic Parameters

Background and Aims: To investigate the frequency of different benign and malignant focal liver lesions (FLLs) in relation to clinical and sonographic features among patients with liver cirrhosis (LC) and newly detected FLLs. Methods: This study was a retrospective analysis of 225 cirrhotic patients with newly detected FLLs who underwent hepatic ultrasound (US) examinations at our university hospital from 2011 to 2022. The diagnosis of FLLs was based on histology and/or consensus radiological criteria, in accordance with the current diagnostic guidelines. The FLLs were classified into benign (bFLLs) or malignant (mFLLs) lesions and the latter group was subclassified into HCC and non-HCC mFLLs. The frequency, clinical parameters, and sonographic features of the different groups were examined and compared. Results: Of the 225 FLLs, 154 (68.4%) were mFLLs and 71 (31.6%) bFLLs. HCC was the most frequent subcategory of FLLs (132; 58.7%). There were (22; 9.8%) non-HCC mFLLs with 11 (4.9%) metastases and 11 (4.9%) non-HCC primary liver tumors. Regenerative nodules (RNs) were the most frequent form of bFLLs (25; 11.1%), followed by simple cysts (22; 9.8%) and hemangiomas (14; 6.2%). The other bFLLs (10; 14.1%) were fat deposition/sparing (5), hematomas (2), abscesses (2), and echinococcal cysts (1). The distribution of bFLLs and HCC and non-HCC mFLLs varied significantly according to the clinical scenarios. HCC mFLLs were more frequent in males (p = 0.001), in those with no history of active non-hepatic primary malignant disease (NHPMD) (p < 0.001), in those with a hepatitis B or C etiology of LC (p = 0.002), when located in the right lobe (p = 0.008), and when portal vein thrombosis was present (p = 0.03). Conclusion: In cirrhotic patients with newly detected FLLs, the non-HCC etiology was more frequently diagnosed in lesions that were located in the left lobe, in females, and in patients with a history of active NHPMD. Thus, the lower frequency of HCC in the abovementioned groups demonstrated that a cautious implementation of the current consensus radiological criteria would be required for these groups, particularly in patients with an active NHPMD, given the fact that the consensus criteria were not validated in these populations. A more active diagnostic approach may ultimately be needed for these patients. Large prospective studies are needed to validate these findings.

The diagnostic performance of contrast-enhanced CT versus extracellular contrast agent-enhanced MRI in detecting hepatocellular carcinoma: direct comparison and a meta-analysis

To compare the diagnostic value of contrast-enhanced computed tomography (CT) with extracellular contrast agent-enhanced magnetic resonance imaging (ECA-MRI) for the detection of hepatocellular carcinoma (HCC). Pubmed, Embase, Web of Science and Cochrane Library were searched (1/5/2021) for studies comparing contrast-enhanced CT with ECA-MRI in patients suspected of HCC. Studies without head-to-head comparison were excluded. The pooled sensitivity, specificity and summary area under the curve (sAUC) of contrast-enhanced CT and ECA-MRI in detecting HCC was calculated based on bivariate random effects model. Heterogeneity test included threshold effect analysis and meta-regression. Subgroup analyses were conducted according to lesion size (< 20 mm or ≥ 20 mm). Overall, 10 articles containing 1333 patients were deemed suitable for inclusion in this meta-analysis. ECA-MRI displayed increased sensitivity to contrast-enhanced CT in detecting HCC (0.77 vs. 0.63, P < 0.01). The difference in specificity between ECA-MRI and contrast-enhanced CT was not statistically significant (0.93 vs. 0.94, P = 0.25). ECA-MRI yielded higher diagnostic accuracy (sAUCs = 0.88 vs. 0.80, P < 0.01). In the subgroup analysis with a lesion size < 20 mm, ECA-MRI allowed significant gains of accuracy compared to contrast-enhanced CT (0.79 vs. 0.72, P = 0.02). ECA-MRI also outperformed contrast-enhanced CT in patients with lesion size ≥ 20 mm (sAUCs = 0.96 vs. 0.93, P = 0.04). ECA-MRI provided higher sensitivity and accuracy than contrast-enhanced CT in detecting HCC, especially lesions size < 20 mm.

MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018

Differentiating hepatocellular carcinoma (HCC) from other primary liver malignancies in the Liver Imaging Reporting and Data System (LI-RADS) M (LR-M) tumours noninvasively is critical for patient treatment options, but visual evaluation based on medical images is a very challenging task. This study aimed to evaluate whether magnetic resonance imaging (MRI) models based on radiomics features could further improve the ability to classify LR-M tumour subtypes. A total of 102 liver tumours were defined as LR-M by two radiologists based on LI-RADS and were confirmed to be HCC (n = 31) and non-HCC (n = 71) by surgery. A radiomics signature was constructed based on reproducible features using the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression algorithms with tenfold cross-validation. Logistic regression modelling was applied to establish different models based on T2-weighted imaging (T2WI), arterial phase (AP), portal vein phase (PVP), and combined models. These models were verified independently in the validation cohort. The area under the curve (AUC) of the models based on T2WI, AP, PVP, T2WI + AP, T2WI + PVP, AP + PVP, and T2WI + AP + PVP were 0.768, 0.838, 0.778, 0.880, 0.818, 0.832, and 0.884, respectively. The combined model based on T2WI + AP + PVP showed the best performance in the training cohort and validation cohort. The discrimination efficiency of each radiomics model was significantly better than that of junior radiologists’ visual assessment (p < 0.05; Delong). Therefore, the MRI-based radiomics models had a good ability to discriminate between HCC and non-HCC in LR-M tumours, providing more options to improve the accuracy of LI-RADS classification.

Risk Stratification and Distribution of Hepatocellular Carcinomas in CEUS and CT/MRI LI-RADS: A Meta-Analysis

Background

CEUS LI-RADS and CT/MRI LI-RADS have been used in clinical practice for several years. However, there is a lack of evidence-based study to compare the proportion of hepatocellular carcinomas (HCCs) in each category and the distribution of HCCs of these two categorization systems.

Purpose

The purpose of this study was to compare the proportion of HCCs between corresponding CEUS LI-RADS and CT/MRI LI-RADS categories and the distribution of HCCs and non-HCC malignancies in each category.

Methods

We searched PubMed, Embase, and Cochrane Central databases from January 2014 to December 2021. The proportion of HCCs and non-HCC malignancies and the corresponding sensitivity, specificity, accuracy, diagnostic odds ratio (DOR), and area under the curve (AUC) of the LR-5 and LR-M categories were determined using a random-effect model.

Results

A total of 43 studies were included. The proportion of HCCs in CEUS LR-5 was 96%, and that in CECT/MRI LR-5 was 95% (p > 0.05). The proportion of non-HCC malignancy in CEUS LR-M was lower than that of CT/MRI LR-M (35% vs. 58%, p = 0.01). The sensitivity, specificity, and accuracy of CEUS LR-5 for HCCs were 73%, 92%, and 78%, respectively, and of CT/MRI LR-5 for HCCs, 69%, 92%, and 76%, respectively.

Conclusion

With the upshift of the LI-RADS category, the proportion of HCCs increased. CEUS LR-3 has a lower risk of HCCs than CT/MRI LR-3. CEUS LR-5 and CT/MRI LR-5 have a similar diagnostic performance for HCCs. CEUS LR-M has a higher proportion of HCCs and a lower proportion of non-HCC malignancies compared with CT/MRI LR-M.

Added value of enhanced CT on LR-3 and LR-4 observation of Gd-EOB-DTPA MRI for the diagnosis of HCC: are CT and MR washout features interchangeable?

OBJECTIVE

To characterize the use of portal venous or delayed phase CT as an alternative to estimate washout for the non-invasive diagnosis of hepatocellular carcinoma (HCC) on gadoxetic acid-enhanced MRI in combination with other features.

METHODS

This retrospective study included 226 observations (n = 162 patients) at high risk for HCC imaged with gadoxetic acid-enhanced MRI and enhanced liver CT between March 2015 and March 2018. Two radiologists independently evaluated two sets of images and assigned the final Liver Imaging Reporting and Data System (LI-RADS) categories by consensus using gadoxetic acid-enhanced MRI. LR-1, LR-2, LR-5, and LR-M were excluded from the study.The observations were divided using different criteria for washout: hypointensity on the portal venous phase (PVP) at MRI (criteria 1), hypointensity on PVP at MRI and/or hypoattenuation on the PVP/delayed phase at dynamic CT (criteria 2), and hypointensity on the PVP and/or hepatobiliary phase at MRI (criteria 3). The sensitivity, specificity, and accuracy for the diagnosis of HCC were analyzed for each criterion.

RESULTS

Using gadoxetic acid-enhanced, 226 lesions were diagnosed as LR-3 or LR-4 by LI-RADS. Among them, 98 and 152 had "washout" at criteria 1 and 2, respectively. For the diagnosis of HCC, criteria 2 and 3 showed significantly higher sensitivities (67.3 and 92.5%, respectively) compared with criteria 1 (35.5%) (p < 0.001). The specificity of criteria 3 (13%) was significantly lower than those of criteria 1 and 2 (40.7% and 38.4%, respectively, p < 0.001). The specificities between criteria 1 and 2 were not statistically different (p = 0.427).

CONCLUSION

Although the LI-RADS lexicon does not permit the interchange of image features among various image modalities, the sensitivity of HCC diagnosis could be improved without any decrease in specificity by adding CT image washout features.

ADVANCES IN KNOWLEDGE

Although the LI-RADS lexicon does not permit the interchange of image features among various image modalities, complementary use of dynamic CT in LR-3 or LR-4 categories on the basis of gadoxetic acid-enhanced MRI may contribute to major imaging feature.

New Liver MR Imaging Hallmarks for Small Hepatocellular Carcinoma Screening and Diagnosing in High-Risk Patients

OBJECTIVE

Early detection and diagnosis of hepatocellular carcinoma (HCC) is essential for prognosis; however, the imaging hallmarks for tumor detection and diagnosis has remained the same for years despite the use of many new immerging imaging methods. This study aimed to evaluate the detection performance of hepatic nodules in high risk patients using either hepatobiliary specific contrast (HBSC) agent or extracellular contrast agent (ECA), and further to compare the diagnostic performances for hepatocellular carcinoma (HCC) using different diagnostic criteria with the histopathological results as reference standard.

METHODS

This prospective study included 247 nodules in 222 patients (mean age, 53.32 ± 10.84 years; range, 22-79 years). The detection performance and imaging features of each nodule were evaluated in all MR sequences by three experienced abdominal radiologists. The detection performance of each nodule on all MR sequences were compared and further the diagnostic performance of various diagnostic criteria were evaluated.

RESULTS

For those patients who underwent ECA-MRI, the conventional imaging hallmark of "AP + PVP and/or DP" was recommended, as 60.19% diagnostic sensitivity, 80.95% specificity and 100% lesion detection rate. Additionally, for those patients who underwent HBSC-MRI, the diagnostic criteria of "DWI + HBP" was recommended. This diagnostic criteria demonstrated, both in all tumor size and for nodules ≤2 cm, higher sensitivity (93.07 and 90.16%, all p <0.05, respectively) and slightly lower specificity (64.71 and 87.50%, all p >0.05, respectively) than that of the European Association for the Study of the Liver (EASL) criteria.

CONCLUSIONS

Different abbreviated MR protocols were recommended for patients using either ECA or HBSC. These provided imaging settings demonstrated high lesion detection rate and diagnostic performance for HCC.

Hepatocellular carcinoma surveillance, incidence, and tumor doubling times in patients cured of hepatitis C

Background

Hepatocellular carcinoma (HCC) incidence and mortality vary by race/ethnicity and both are higher in Black patients than in Whites. For HCC surveillance, all cirrhotic patients are advised to undergo lifelong twice‐annual abdominal imaging. We investigated factors associated with surveillance and HCC incidence in a diverse HCC risk group, cirrhotic patients recently cured of hepatitis C virus (HCV) infection.

Methods

In this observational cohort study, all participants (n = 357) had advanced fibrosis/cirrhosis and were cured of HCV with antiviral treatment. None had Liver Imaging Reporting and Data System (LI‐RADS) 2–5 lesions prior to HCV cure. Ultrasound, computed tomography, and/or magnetic resonance imaging were used for surveillance.

Results

At a median follow‐up of 40 months [interquartile range (IQR) = 28–48], the median percentage of time up‐to‐date with surveillance was 49% (IQR) = 30%–71%. The likelihood of receiving a first surveillance examination was not significantly associated with race/ethnicity, but was higher for patients with more advanced cirrhosis, for example, bilirubin [odds ratio (OR) = 3.8/mg/dL, p = 0.002], private insurance (OR = 3.4, p = 0.006), and women (OR = 2.3, p = 0.008). The likelihood of receiving two or three examinations was significantly lower for non‐Hispanic Blacks and Hispanics versus non‐Hispanic Whites (OR = 0.39, and OR = 0.40, respectively, p < 0.005 for both) and for patients with higher platelet counts (OR = 0.99/10,000 cells/µl, p = 0.01), but higher for patients with private insurance (OR = 2.8, p < 0.001). Incident HCC was associated with higher bilirubin (OR = 1.7, p = 0.02) and lower lymphocyte counts (OR = 0.16, p = 0.01).

Conclusions

Contrary to best practices, HCC surveillance was associated with sociodemographic factors (insurance status and race/ethnicity) among patients cured of HCV. Guideline‐concordant surveillance is needed to address healthcare disparities.

Added Value of Quantitative Apparent Diffusion Coefficients for Identifying Small Hepatocellular Carcinoma from Benign Nodule Categorized as LI-RADS 3 and 4 in Cirrhosis

BACKGROUND AND AIMS

Correct identification of small hepatocellular carcinomas (HCCs) and benign nodules in cirrhosis remains challenging, quantitative apparent diffusion coefficients (ADCs) have shown potential value in characterization of benign and malignant liver lesions. We aimed to explore the added value of ADCs in the identification of small (≤3 cm) HCCs and benign nodules categorized as Liver Imaging Reporting and Data System (LI-RADS) 3 (LR-3) and 4 (LR-4) in cirrhosis.

METHODS

Ninety-seven cirrhosis patients with 109 small nodules (70 HCCs, 39 benign nodules) of LR-3 and 4 LR-4 based on major and ancillary magnetic resonance imaging features were included. Multiparametric quantitative ADCs of the lesions, including the mean ADC (ADC_mean), minimum ADC (ADC_min), maximal ADC (ADC_max), ADC standard deviation (ADC_std), and mean ADC value ratio of lesion-to-liver parenchyma (ADC_ratio) were calculated. Regarding the joint diagnosis, a nomogram model was plotted using multivariate logistic regression analysis. The performance was assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

The ADC_mean, ADC_min, ADC_ratio, and ADC_std were significantly associated with the identification of small HCC and benign nodules (p<0.001). For the joint diagnosis, the LI-RADS category (odds ratio [OR]=12.50), ADC_min (OR=0.14), and ADC_ratio (OR=0.12) were identified as independent factors for distinguishing HCCs from benign nodules. The joint nomogram model showed good calibration and discrimination, with a C-index of 0.947. Compared with the LI-RADS category alone, this nomogram model demonstrated a significant improvement in diagnostic performance, with AUC increasing from 0.820 to 0.967 (p=0.001).

CONCLUSIONS

The addition of quantitative ADCs could improve the identification of small HCC and benign nodules categorized as LR-3 and 4 LR-4 in patients with cirrhosis.

Can modified LI-RADS increase the sensitivity of LI-RADS v2018 for the diagnosis of 10-19 mm hepatocellular carcinoma on gadoxetic acid-enhanced MRI?

PURPOSE

To evaluate whether the Liver Imaging Reporting and Data System (LI-RADS) v2018 LR-5 criteria can be modified to increase sensitivity without reducing specificity for diagnosing 10-19 mm hepatocellular carcinoma (HCC) on gadoxetic acid-enhanced magnetic resonance imaging (MRI).

METHODS

A total of 133 high-risk consecutive patients with 174 small observations (10-19 mm) detected on gadoxetic acid-enhanced MRI were retrospectively studied. LI-RADS MRI major features (MFs) and ancillary features (AFs) were reviewed by two independent radiologists in consensus. Observations were categorized using LI-RADS v2018 MFs. Independently significant AFs were identified through logistic regression analysis. Upgraded LR-5 criteria were developed by combining independently significant AFs with MFs of LR-3 or LR-4 v2018. The sensitivity and specificity of the new diagnostic criteria were compared with those of LR-5 v2018 using McNemar's test.

RESULTS

Three of the AFs favoring malignancy [mild-moderate T2 hyperintensity, transitional phase (TP) hypointensity and fat in mass] were independently significant features for diagnosing 10-19 mm HCC. The upgraded LR-5 criteria (mLI-RADS VII: LR-4 + mild-moderate T2 hyperintensity/TP hypointensity or LR-3 + fat in mass) yielded a significantly greater sensitivity than that of the LR-5 v2018 criteria (70.4% vs 55.1%; p < 0.001), whereas the specificity was not significantly different (94.7% vs 98.7%, p = 0.250).

CONCLUSIONS

Independently significant AFs may be used to upgrade an observation from LR-3/LR-4 to LR-5, which can improve the sensitivity without impairing the specificity for diagnosing 10-19 mm HCC on gadoxetic acid-enhanced MRI.

CT/MRI and CEUS LI-RADS Major Features Association with Hepatocellular Carcinoma: Individual Patient Data Meta-Analysis

Background The Liver Imaging Reporting and Data System (LI-RADS) assigns a risk category for hepatocellular carcinoma (HCC) to imaging observations. Establishing the contributions of major features can inform the diagnostic algorithm. Purpose To perform a systematic review and individual patient data meta-analysis to establish the probability of HCC for each LI-RADS major feature using CT/MRI and contrast-enhanced US (CEUS) LI-RADS in patients at high risk for HCC. Materials and Methods Multiple databases (MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scopus) were searched for studies from January 2014 to September 2019 that evaluated the accuracy of CT, MRI, and CEUS for HCC detection using LI-RADS (CT/MRI LI-RADS, versions 2014, 2017, and 2018; CEUS LI-RADS, versions 2016 and 2017). Data were centralized. Clustering was addressed at the study and patient levels using mixed models. Adjusted odds ratios (ORs) with 95% CIs were determined for each major feature using multivariable stepwise logistic regression. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) (PROSPERO protocol: CRD42020164486). Results A total of 32 studies were included, with 1170 CT observations, 3341 MRI observations, and 853 CEUS observations. At multivariable analysis of CT/MRI LI-RADS, all major features were associated with HCC, except threshold growth (OR, 1.6; 95% CI: 0.7, 3.6; P = .07). Nonperipheral washout (OR, 13.2; 95% CI: 9.0, 19.2; P = .01) and nonrim arterial phase hyperenhancement (APHE) (OR, 10.3; 95% CI: 6.7, 15.6; P = .01) had stronger associations with HCC than enhancing capsule (OR, 2.4; 95% CI: 1.7, 3.5; P = .03). On CEUS images, APHE (OR, 7.3; 95% CI: 4.6, 11.5; P = .01), late and mild washout (OR, 4.1; 95% CI: 2.6, 6.6; P = .01), and size of at least 20 mm (OR, 1.6; 95% CI: 1.04, 2.5; P = .04) were associated with HCC. Twenty-five studies (78%) had high risk of bias due to reporting ambiguity or study design flaws. Conclusion Most Liver Imaging Reporting and Data System major features had different independent associations with hepatocellular carcinoma; for CT/MRI, arterial phase hyperenhancement and washout had the strongest associations, whereas threshold growth had no association. © RSNA, 2021 Online supplemental material is available for this article.

Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study

We aimed to evaluate the stability of radiomic features in the liver of healthy individuals across different three-dimensional regions of interest (3D ROI) sizes in T1-weighted (T1w) and T2-weighted (T2w) images from different MR scanners. We retrospectively included 66 examinations of patients without known diseases or pathological imaging findings acquired on three MRI scanners (3 Tesla I: 25 patients, 3 Tesla II: 19 patients, 1.5 Tesla: 22 patients). 3D ROIs of different diameters (10, 20, 30 mm) were drawn on T1w GRE and T2w TSE images into the liver parenchyma (segment V–VIII). We extracted 93 radiomic features from the different ROIs and tested features for significant differences with the Mann–Whitney-U (MWU)-test. The MWU-test revealed significant differences for most second- and higher-order features, indicating a systematic difference dependent on the ROI size. The features mean, median, root mean squared (RMS), 10th percentile, and 90th percentile were not significantly different. We also assessed feature robustness to ROI size variation with overall concordance correlation coefficients (OCCCs). OCCCs across the different ROI-sizes for mean, median, and RMS were excellent (>0.90) in both sequences on all three scanners. These features, therefore, seem robust to ROI-size variation and suitable for radiomic studies of liver MRI.

EASL versus LI-RADS: Intra-individual comparison of MRI with extracellular contrast and gadoxetic acid for diagnosis of small HCC

BACKGROUND & AIMS

Liver Imaging Reporting and Data System (LI-RADS) and European Association for the Study (EASL) criteria for hepatocellular carcinoma (HCC) diagnosis have been updated in 2018. We aimed to compare the HCC diagnostic performance of LI-RADS and EASL criteria with extracellular contrast agents-MRI (ECA-MRI) and hepatobiliary agents-MRI (HBA-MRI).

METHODS

We prospectively evaluated 179 participants with cirrhosis (n = 105) or non-cirrhotic chronic hepatitis B (CHB) (n = 74) who underwent both ECA-MRI and HBA-MRI before surgery for de novo nodule(s) measuring 10-30 mm. We compared the HCC diagnostic performance of EASL and LR-5 in both MRIs.

RESULTS

In an analysis of 215 observations (175 HCCs, 17 non-HCC malignancies and 23 benign lesions) identified from cirrhotic or non-cirrhotic CHB participants, LR-5 with ECA-MRI provided the highest sensitivity (80.7%), followed by EASL with ECA-MRI (76.2%), LR-5 with HBA-MRI (67.3%) and EASL with HBA-MRI (63.0%, all P < .05). The specificities were comparable (89.4%-91.5%). When the analysis is limited to participants with pathological cirrhosis (123 observations), the sensitivity of LR-5 with ECA-MRI was similar to that of EASL with ECA-MRI (82.7% vs 80.2%, P = .156), but higher than LR-5 with HBA-MRI (65.1%) or EASL with HBA-MRI (62.8%, both P < .001), with comparable specificities (87.5%-91.7%).

CONCLUSIONS

The LR-5 with ECA-MRI yielded the highest sensitivity with a similar specificity for HCC diagnosis in cirrhosis and non-cirrhotic CHB participants, while the sensitivities of LR-5 and EASL with ECA-MRI are similar for cirrhosis participants. This indicates non-invasive diagnosis criteria can differ by contrast agents and presence of cirrhosis.

Tumor in vein (LR-TIV) and liver imaging reporting and data system (LI-RADS) v2018: diagnostic features, pitfalls, prognostic and management implications

Vascular invasion by hepatocellular carcinoma (HCC), also known as tumor in vein (TIV), indicates highly invasive tumor behavior and is also associated with poor outcome. Because a diagnosis of TIV precludes liver transplantation, knowledge of the imaging findings to differentiate between TIV and bland thrombus is key for proper patient management. Prior versions of liver imaging reporting and data system (LI-RADS) included presence of TIV as part of LR-5 criteria. However, even if HCC is the most common liver malignancy associated with TIV, other tumors can have vascular invasion and may occur in cirrhotic patients. For these reasons, in LI-RADS v2017 LR-TIV has been introduced as a new different diagnostic category. The aim of this article is to discuss the diagnostic criteria of LR-TIV according to LI-RADS v2018 and analyze potential pitfalls encountered on daily clinical practice. Indeterminate cases and how to manage them will also be discussed.

Progression Rates of LR-2 and LR-3 Observations on MRI to Higher LI-RADS Categories in Patients at High Risk for Hepatocellular Carcinoma: A Retrospective Study

Background: Reported rates of hepatocellular carcinoma (HCC) for LR-2 and LR-3 observations are generally greater than what may be expected based on clinical experience, possibly reflecting some studies' requirement for pathologic reference. Objective: To determine progression rates to higher LI-RADS categories of LR-2 and LR-3 observations in patients at high risk for HCC. Methods: This retrospective study included 91 patients (mean age 62 years; 64 men, 27 women) at high risk for HCC with clinically reported LR-2 (n=55) or LR-3 (n=36) observations on MRI and who also underwent follow-up CT or MRI after at least 12 months. A study coordinator annotated the location of a single LR-2 or LR-3 observation per patient, based on the clinical reports. Using LI-RADS v2018 criteria, two radiologists independently assigned LI-RADS categories on the follow-up examinations. Progression rates from LR-2 or LR-3 to higher categories were determined. A post hoc consensus review was performed of observations that progressed to LR-4 or LR-5. Subgroup analyses were performed with respect to presence or prior HCC (n=34) or a separate baseline LR-5 observation (n=12). Results: For LR-2 observations, progression to LR-4 was 0.0% (95% CI 0.0%-6.7%) and LR-5 was 3.6% (95% CI 0.4%-13.1%) for both readers. For LR-3 observations, progression to LR-4 was 22.2% (95% CI 9.6%-43.8%) and LR-5 was 11.1% (95% CI 3.0%-28.4%) for both readers. Fourteen observations progressed to LR-4 or LR-5 for both readers (post hoc analysis: no LR-2 to LR-4; two LR-2 to LR-5; eight LR-3 to LR-4; four LR-3 to LR-5). Progression rate from LR-3 to LR-5 was higher (p<.001) among patients with versus without a separate baseline LR-5 observation (100.0% vs 3.0%, respectively) for both readers; progression rate from LR-2 to LR-5 was not associated with a separate baseline LR-5 observation for either reader (p=.30). Progression rates were not different (p>.99) between patients with versus without prior HCC. Conclusion: Based on progression to LR-4 or LR-5, LR-2 and LR-3 observations showed lower progression rates than reported in studies incorporating pathology in determining progression. Clinical Impact: The findings refine understanding of the clinical significance of LR-2 and LR-3 observations.

Imaging Features at the Periphery: Hemodynamics, Pathophysiology, and Effect on LI-RADS Categorization

Liver lesions have different enhancement patterns at dynamic contrast-enhanced imaging. The Liver Imaging Reporting and Data System (LI-RADS) applies the enhancement kinetic of liver observations in its algorithms for imaging-based diagnosis of hepatocellular carcinoma (HCC) in at-risk populations. Therefore, careful analysis of the spatial and temporal features of these enhancement patterns is necessary to increase the accuracy of liver mass characterization. The authors focus on enhancement patterns that are found at or around the margins of liver observations-many of which are recognized and defined by LI-RADS, such as targetoid appearance, rim arterial phase hyperenhancement, peripheral washout, peripheral discontinuous nodular enhancement, enhancing capsule appearance, nonenhancing capsule appearance, corona enhancement, and periobservational arterioportal shunts-as well as peripheral and periobservational enhancement in the setting of posttreatment changes. Many of these are considered major or ancillary features of HCC, ancillary features of malignancy in general, features of non-HCC malignancy, features associated with benign entities, or features related to treatment response. Distinction between these different patterns of enhancement can help with achieving a more specific diagnosis of HCC and better assessment of response to local-regional therapy. ^©RSNA, 2021.

LI-RADS v2018: how to appropriately use ancillary features in category adjustment from intermediate probability of malignancy (LR-3) to probably HCC (LR-4) on gadoxetic acid-enhanced MRI

OBJECTIVES

To determine the appropriate use of ancillary features (AFs) in upgrading LI-RADS category 3 (LR-3) to category 4 (LR-4) for hepatic nodules on gadoxetic acid-enhanced MRI.

METHODS

We retrospectively analyzed MRI features of solid hepatic nodules (≤ 30 mm) categorized as LR-3/4 on gadoxetic acid-enhanced MRI. In LI-RADS diagnostic table-based-LR-3 observations, logistic regression analyses were performed to identify AFs suggestive of hepatocellular carcinomas (HCCs) rather than non-malignant nodules. Using McNemar's test, the sensitivities and specificities of the final-LR-4 category for HCC diagnosis were compared according to the principles of AF application in category adjustment.

RESULTS

A total of 336 hepatic nodules (191 HCCs; 145 non-malignant) in 252 patients were evaluated. Based on major HCC features, 248 nodules (123 HCCs) were assigned as table-based-LR-3 and 88 nodules (68 HCCs) as table-based-LR-4. In table-based-LR-3 observations, mild-moderate T2 hyperintensity was identified as an independent predictor of HCC as opposed to non-malignant nodules (odds ratio = 3.01, p = 0.002). For HCC diagnosis, different criteria of final-LR-4: only table-based-LR-4, allowing category upgrade using only T2 hyperintensity, or using any AFs favoring malignancy resulted in sensitivities of 35.6% (68/191), 53.9% (103/191), and 88.5% (169/191), and specificities of 86.2% (125/145), 75.9% (110/145), and 21.4% (31/145), respectively, which differed from each other (all p < 0.001).

CONCLUSIONS

While the application of MRI AF in LI-RADS category adjustment increases the sensitivity of LR-4 category for HCC diagnosis, it is accompanied by a significant decrease in specificity. Mild-moderate T2 hyperintensity, a significant AF indicative of HCC, may be more appropriate for upgrading LR-3 to LR-4.

KEY POINTS

• When upgrading from LR-3 to LR-4 using any MRI ancillary features favoring malignancy, LR-4 sensitivity increases but specificity decreased for HCC diagnosis. • By upgrading LR-3 to LR-4 based on MRI ancillary features found to suggest HCC rather than non-malignant nodules in multivariate analysis (i.e., mild-moderate T2 hyperintensity), LR-4 demonstrated a more balanced sensitivity and specificity for HCC diagnosis (53.9% and 75.9%, respectively).

Can Risk Stratification Based on Ultrasound Elastography of Background Liver Assist CEUS LI-RADS in the Diagnosis of HCC?

OBJECTIVE

To explore whether risk stratification based on ultrasound elastography of liver background assists contrast-enhanced ultrasound liver imaging reporting and data system (CEUS LI-RADS) in diagnosing HCC.

MATERIALS AND METHODS

In total, 304 patients with focal liver lesions (FLLs) confirmed by pathology underwent CEUS and ultrasound elastography were included in this retrospective study. Patients with chronic hepatitis B (CHB, n=193) and non-CHB (n=111) were stratified by four liver stiffness measurement (LSM) thresholds. A LI-RADS category was assigned to FLLs using CEUS LI-RADS v2017. The diagnostic performance was assessed with the AUC, sensitivity, specificity, PPV, and NPV.

RESULTS

The mean background liver stiffness of HCC patients with CHB, HCC patients without CHB and non-HCC patients without CHB were 9.72 kPa, 8.23 kPa and 4.97 kPa, respectively. The AUC, sensitivity, specificity and PPV of CEUS LI-RADS for HCC in CHB patients with LSM ≥ 5.8 kPa, ≥ 6.8 kPa, ≥ 9.1 kPa, and ≥ 10.3 kPa were high, with corresponding values of 0.745 to 0.880, 94.2% to 95.3%, 81.3% to 85.7%, and 98.1% to 98.8%, respectively. Higher AUC and specificity for HCC was observed in non-CHB patients with LSM ≥ 9.1 kPa and ≥ 10.3 kPa compared to non-CHB patients with LSM ≥ 5.8 kPa and ≥ 6.8 kPa, with corresponding values of0.964/1.000 vs 0.590/0.580, and 100%/100% vs 60%/70%, respectively.

CONCLUSION

CEUS LI-RADS has a good diagnostic performance in CHB patients regardless of the background liver stiffness. Furthermore, CEUS LI-RADS can be applied for non-CHB patients with a LSM ≥ 9.1 kPa.

Tissue Diagnosis Is Associated With Worse Survival in Hepatocellular Carcinoma: A National Cancer Database Analysis

BACKGROUND

Biopsy to achieve tissue diagnosis (TD) of hepatocellular carcinoma (HCC) risks needle tract seeding. With chest wall and peritoneal recurrences reported, TD could worsen cancer outcomes. We investigated HCC outcomes after TD compared to clinical diagnosis (CD), hypothesizing that TD adversely affects overall survival (OS).

METHODS

The National Cancer Database (NCDB) Participant User File for liver cancer was reviewed, including patients with nonmetastatic HCC treated with major hepatectomy or transplantation. Clinical diagnosis patients were matched 1:1 to TD patients per propensity score. Survival was examined in the unmatched and matched cohorts.

RESULTS

Of 172 283 cases, 16 366 met inclusion criteria. Mean age was 60.8 years, 12 100 (73.9%) were male, and 48.4% of patients received hepatectomies. Clinical diagnosis occurred in 70.4% of cases, and 29.6% underwent TD. Cox regression confirmed the diagnostic method as an independent predictor of OS in addition to age, Charlson-Deyo score, grade, delay of surgery, lymphovascular invasion, nodal stage, and procedure type, favoring transplantation over hepatectomy. After propensity matching on these factors, 4251 patients were matched from each group. In the matched cohort, patients with TD had a significantly lower OS than patients with CD (median: 65.5 vs. 85.6 ± 2.7 months, P < .001). The corresponding 5-year survival was lower in the TD group (47.6% vs. 60.9% P < .001).

CONCLUSION

Hepatocellular carcinoma patients with preoperative TD had decreased OS compared to CD, which persisted after propensity matching. This study supports avoiding biopsy for HCC whenever possible.

Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm

Background

Accurate characterization of small nodules in a cirrhotic liver is challenging. We aimed to determine the additive value of MRI-based radiomics analysis to Liver Imaging Reporting and Data System version 2018 (LI-RADS v 2018) algorithm in differentiating small (≤ 3 cm) hepatocellular carcinomas (HCCs) from benign nodules in cirrhotic liver.

Methods

In this retrospective study, 150 cirrhosis patients with histopathologically confirmed small liver nodules (HCC, 112; benign nodules, 44) were evaluated from January 2013 to October 2018. Based on the LI-RADS algorithm, a LI-RADS category was assigned for each lesion. A radiomics signature was generated based on texture features extracted from T1-weighted, T2W, and apparent diffusion coefficient (ADC) images by using the least absolute shrinkage and selection operator regression model. A nomogram model was developed for the combined diagnosis. Diagnostic performance was assessed using receiver operating characteristic curve (ROC) analysis.

Results

A radiomics signature consisting of eight features was significantly associated with the differentiation of HCCs from benign nodules. Both LI-RADS algorithm (area under ROC [A_z] = 0.898) and the MRI-Based radiomics signature (A_z = 0.917) demonstrated good discrimination, and the nomogram model showed a superior classification performance (A_z = 0.975). Compared with LI-RADS alone, the combined approach significantly improved the specificity (97.7% vs 81.8%, p = 0.030) and positive predictive value (99.1% vs 92.9%, p = 0.031) and afforded comparable sensitivity (97.3% vs 93.8%, p = 0.215) and negative predictive value (93.5% vs 83.7%, p = 0.188).

Conclusions

MRI-based radiomics analysis showed additive value to the LI-RADS v 2018 algorithm for differentiating small HCCs from benign nodules in the cirrhotic liver.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01710-y.

Increasing the sensitivity of LI-RADS v2018 for diagnosis of small (10-19 mm) HCC on extracellular contrast-enhanced MRI

PURPOSE

To evaluate whether the LI-RADS v2018 LR-5 criteria can be modified to increase sensitivity without reducing specificity for diagnosing small (10-19 mm) HCC.

METHODS

167 consecutive high-risk patients with 174 small observations reported clinically on extracellular contrast-enhanced MRI from 2014 to 2018 were retrospectively studied. The best available reference standard was applied for each observation. Blinded to the reference standard, two radiologists scored LI-RADS imaging features retrospectively and assigned each observation a LI-RADS category using LI-RADS v2018 and each of four modified LI-RADS versions (mLI-RADS I to IV) with successively more expansive LR-5 criteria. Per-observation sensitivity and specificity of LR-5 for small HCC using each version were assessed. Each modified version was compared to v2018 (McNemar test).

RESULTS

The 174 observations included 135 HCC, 8 non-HCC malignancies, and 31 benign entities. Using LI-RADS v2018, LR-5 provided 70% (both readers) sensitivity and 95% (both readers) specificity for small HCC. Expanding the LR-5 criteria to include nonrim APHE plus at least one additional major feature (mLI-RADS I) or no APHE plus at least two additional major features (mLI-RADS II) significantly increased sensitivity (reader 1/reader 2: 75%/75% vs. 70%, p = 0.016/0.031; 78%/79% vs. 70%, p = 0.001/0.001) without significantly reducing specificity (reader 1/reader 2: 90%/92% vs. 95%, p = 0.500/1.000 for both). mLI-RADS III and IV further increased sensitivity (reader 1/reader 2: 80%/81% vs. 70%, p < 0.001/< 0.001; 94%/92% vs. 70, p < 0.001/< 0.001) but with trend-level (reader 1/reader 2: 85%/80% vs. 95%, p = 0.125/0.063) or significant (reader 1/reader 2: 64%/62% vs. 95%, p < 0.001/< 0.001) specificity reductions.

CONCLUSIONS

Expanding the v2018 LR-5 criteria to include nonrim APHE plus at least one additional major feature or no APHE plus at least two additional major features significantly increases sensitivity without significantly reducing specificity for small HCC. Confirmation is warranted in multi-center prospective studies.

LI-RADS v2018 major criteria: Do hepatocellular carcinomas in non-alcoholic steatohepatitis differ from those in virus-induced chronic liver disease on MRI?

PURPOSE

LI-RADS v2018 diagnostic system is used to diagnose hepatocellular carcinoma (HCC) in at risk patients. However, its applicability to HCC in non-alcoholic steatohepatitis (NASH) has not been specifically studied. The purpose of this study was to assess the applicability of LI-RADS v2018 diagnostic system for HCC in patients with NASH.

MATERIALS AND METHODS

The MRI examinations of 41 patients with HCC and NASH (NASH group) were reviewed and compared to those obtained in 41 patients with HCC and virus-induced chronic liver disease (Virus group). MRI examinations of the two groups were compared for imaging presentation, LI-RADS major criteria and LI-RADS categorization. Qualitative variables were compared using Fisher exact test and quantitative variables using Mann-Whitney U test Interreader agreement was assessed using kappa statistic.

RESULTS

No significant differences in qualitative and quantitative variables were observed between the two groups. Most common findings in the two groups were hyperenhancement during the arterial phase and visibility on T2-weighted images (93 % vs. 98 %, P = 0.616 and 85 % vs. 88 %, P = 1.000 for NASH group and Virus group, respectively). No differences in prevalence between the two groups were found for any major LI-RADS v2018 criterion. Interreader agreement for LI-RADS categorization was strong for the NASH group (kappa = 0.802) and moderate for the virus group (kappa = 0.720). No differences were found between the two groups for LI-RADS categories (P =  0.303).

CONCLUSIONS

The LI-RADS v2018 diagnostic algorithm can be applied in patients with NASH.

Clinical Application of Liver Imaging Reporting and Data System for Characterizing Liver Neoplasms: A Meta-Analysis

The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing liver imaging in patients at risk of developing hepatocellular carcinoma (HCC). We aimed to determine the diagnostic performance of LI-RADS category 5 (LR5) for diagnosing HCC and LI-RADS category M (LRM) for characterizing other non-HCC malignancies (OM) using contrast-enhanced ultrasound (CEUS) and computed tomography (CT)/magnetic resonance imaging (MRI). Multiple databases were searched for articles evaluating the diagnostic accuracy of CEUS LI-RADS and/or CT/MRI LI-RADS. A random-effects model was adopted to synthesize the summary estimates of the diagnostic accuracy of LR5 for diagnosing HCC and LRM for characterizing OM using CEUS and CT/MRI. The pooled sensitivity and specificity of CEUS LR5 for the diagnosis of HCC were 69% and 93%, respectively. The pooled sensitivity was 67% and the specificity, 93% of CT/MRI LR5 for HCC diagnosis. There was no significant difference between the overall diagnostic accuracy for HCC diagnosis of CEUS LR5 and that of CT/MRI LR5 in terms of diagnostic odds ratio (DOR) (p = 0.55). The sensitivity was 84% with a specificity of 90% in the CEUS LRM for characterizing OM, while the sensitivity and specificity of CT/MRI LRM for characterizing OM was 63% and 95%. The DOR of CEUS LRM for characterizing OM was higher than that of CT/MRI LRM without significant difference (50.59 vs. 36.06, p = 0.34). This meta-analysis indicated that CEUS LI-RADS is qualified to characterize HCC and OM and may provide complementary information on liver nodules to CT/MRI LI-RADS.

MRI LI-RADS Version 2018: Impact of and Reduction in Ancillary Features

OBJECTIVE. The purpose of this study is to determine the impact of LI-RADS ancillary features on MRI and to ascertain whether the number of ancillary features can be reduced without compromising LI-RADS accuracy. MATERIALS AND METHODS. A total of 222 liver observations in 81 consecutive patients were identified on MRI between August 2013 and December 2018. The presence or absence of major and ancillary features was used to determine the LI-RADS category for LR-1 to LR-5 observations. Final diagnosis was established on the basis of pathologic findings or one of several composite clinical reference standards. Diagnostic accuracy was compared with and without ancillary features by use of the z test of proportions. Decision tree analysis and machine learning-based feature pruning were used to identify noncontributory ancillary features for LI-RADS categorization. Interobserver agreement with and without ancillary features was measured using the Krippendorff alpha coefficient, and comparisons were made using bootstrapping. A p < .05 was considered statistically significant. RESULTS. Application of ancillary features resulted in a change in the LI-RADS category of seven hepatocellular carcinomas (HCCs), with the category of six of seven (86%) HCCs upgraded; 51 benign observations also had a change in LI-RADS category, with the category of 33 (65%) of these observations downgraded. When ancillary features were applied, the percentage of HCCs in each LI-RADS category did not differ significantly compared with major features alone (p = .06-.49). Decision tree analysis and the machine learning model identified five ancillary features as noncontributory: corona enhancement, nodule-in-nodule, mosaic architecture, blood products in mass, and fat in a mass, more than in adjacent liver. Interobserver agreement was high with and without application of ancillary features; however, it was significantly higher without ancillary features (p < .001). CONCLUSION. Although ancillary features are an important component of LI-RADS, their impact may be small. Several ancillary features likely can be removed from LI-RADS without compromising diagnostic performance.

Magnetic Resonance Imaging May Be Able to Identify the Origin of Neuroendocrine Tumor Liver Metastases

OBJECTIVES

The aim of the study was to discriminate hepatic metastases from pancreatic neuroendocrine tumors (pNET) and hepatic metastases from midgut neuroendocrine tumors (mNET) with magnetic resonance imaging (MRI).

METHODS

MRI examinations of 24 patients with hepatic metastases from pNET were quantitatively and qualitatively assessed by 2 blinded readers and compared to those obtained in 23 patients with hepatic metastases from mNET. Inter-reader agreement was calculated with kappa and intraclass correlation coefficient (ICC). Sensitivity, specificity, and accuracy of each variable for the diagnosis of hepatic metastasis from pNET were calculated. Associations between variables and primary tumor (i.e., pNET vs. mNET) were assessed by univariate and multivariate analyses. A nomogram was developed and validated using an external cohort of 20 patients with pNET and 20 patients with mNET.

RESULTS

Interobserver agreement was strong to perfect (k = 0.893-1) for qualitative criteria and excellent for quantitative variables (ICC: 0.9817-0.9996). At univariate analysis, homogeneity on T1-weighted images was the most discriminating variable for the diagnosis of pNET (OR: 6.417; p = 0.013) with greatest sensitivity (88%; 21/24; 95% CI: 68-97%). At multivariate analysis, tumor homogeneity on T1-weighted images (p = 0.007; OR: 17.607; 95% CI: 2.179-142.295) and target sign on diffusion-weighted images (p = 0.007; OR: 19.869; 95% CI: 2.305-171.276) were independently associated with pNET. Nomogram yielded a corrected AUC of 0.894 (95% CI: 0.796-0.992) for the diagnosis of pNET in the training cohort and 0.805 (95% CI: 0.662-0.948) in the validation cohort.

CONCLUSIONS

MRI provides qualitative features that can help discriminate between hepatic metastases from pNET and those from mNET.

MRI Ancillary Features for LI-RADS Category 3 and 4 Observations: Improved Categorization to Indicate the Risk of Hepatic Malignancy

OBJECTIVE. The purpose of this study was to investigate whether ancillary features can help stratify malignancy risk in Liver Imaging Reporting and Data System (LI-RADS) category 3 (LR-3) and 4 (LR-4) observations. MATERIALS AND METHODS. This retrospective longitudinal study included 106 LR-3 or LR-4 observations on gadolinium-enhanced MRI obtained from January 2014 to December 2015 in 80 patients who were treatment naïve and at risk (mean age, 58.0 ± 10.7 [SD] years; 60 men). The presence of major and ancillary features, the category determined using only major features, and the final category adjusted by the application of ancillary features were retrospectively analyzed. MRI features were compared using generalized estimating equations, and cumulative incidence curves for malignancy were compared using log-rank tests with a resampling extension. RESULTS. At 6-month follow-up, the cumulative incidence of observations initially categorized as LR-4, observations upgraded to LR-4, observations initially categorized as LR-3, and observations downgraded to LR-3 were 62.5%, 29.7%, 6.2%, and 0%, respectively. The cumulative incidence of malignancy did not differ between observations categorized by major feature as LR-3 and LR-4 (p = 0.12), but was higher in final observations categorized as LR-4 than in those categorized as LR-3 (p < 0.001). Among observations categorized by major feature as LR-3, the cumulative incidence of malignancy was higher in observations upgraded to LR-4 than in observations that were initially graded as LR-3 (p = 0.03), which showed differences in the frequency of restricted diffusion and mild-to-moderate T2-weighted hyperintensity (p < 0.001 for both). CONCLUSION. Final categories determined with ancillary features, instead of categories determined by major features only, can help indicate malignancy risk in LR-3 and LR-4 observations on MRI.

Imaging Diagnosis of Hepatocellular Carcinoma: The Liver Imaging Reporting and Data System, Why and How?

The Liver Imaging Reporting and Data System (LI-RADS) provides standardized lexicon, technique, interpretation, and reporting of liver imaging in patients at risk for hepatocellular carcinoma (HCC). When applied to at-risk populations, LI-RADS achieves higher than 95% positive predictive value for the noninvasive diagnosis of HCC on computed tomography (CT), MRI and contrast-enhanced ultrasound (CEUS). This article focuses on similarities and differences between the CT/MRI diagnostic algorithm (CT/MRI LI-RADS) and the CEUS diagnostic algorithm (CEUS LI-RADS) to inform health care professionals for efficient and appropriate clinical decisions through the management of patients at risk.