Liver Imaging Reporting And Data System Research Articles

CT/MRI LI-RADS 2024 Update: Treatment Response Assessment.

With the rising incidence of hepatocellular carcinoma, there has been increasing use of local-regional therapy (LRT) to downstage or bridge to transplant, for definitive treatment, and for palliation. The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Assessment (TRA) algorithm provides guidance for step-by-step tumor assessment after LRT and standardized reporting. Current evidence suggests that the algorithm performs well in the assessment of tumor response to arterial embolic and loco-ablative therapies and fair when assessing response to radiation-based therapies, with limited data to validate the latter. Both evidence-based and expert-based refinements of the algorithm are needed to improve its diagnostic accuracy after varying types of LRT. This review provides an overview of the challenges and limitations of the LI-RADS TRA algorithm version 2017 and discusses the refinements introduced in the updated 2024 LI-RADS algorithm for CT/MRI.

Prognostic Implications of MRI-assessed Intratumoral Fat in Hepatocellular Carcinoma: An Asian and European Cohort Study.

Background The clinicopathologic-radiologic and prognostic characteristics of intratumoral fat in hepatocellular carcinoma (HCC) are critical for personalized treatment but remain understudied. Purpose To investigate the clinicopathologic-radiologic associations and prognostic implications of MRI-assessed intratumoral fat in HCCs. Materials and Methods This retrospective cohort study included consecutive adult patients who underwent resection for solitary HCCs and preoperative contrast-enhanced MRI from two tertiary-care hospitals in East Asia (March 2011 to December 2021) and Western Europe (September 2012 to December 2019). MRI scans were independently evaluated by three radiologists at each hospital. Based on Liver Imaging Reporting and Data System (LI-RADS) version 2018, intratumoral fat was defined as "fat in mass more than adjacent liver," and the homogeneous subtype was defined as intratumoral fat "in absence of mosaic and nodule-in-nodule architecture." Recurrence-free survival (RFS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared using the log-rank test. Cox regression analyses were conducted to identify factors associated with RFS and OS. Results A total of 933 patients were included in the Asian (n = 736; median age, 53 years [IQR, 45-62 years]; 626 male) and European (n = 207; median age, 64 years [IQR, 55-70 years]; 161 male) cohorts. MRI-assessed intratumoral fat was detected in 30% (215 of 726) and 31% (64 of 207) of patients in the Asian and European cohorts, respectively (P = .72). In both cohorts, the steatohepatitic subtype, nonperipheral washout, enhancing capsule, and mosaic architecture were more frequent in tumors with intratumoral fat (P value range, <.001 to .04). Intratumoral fat in general was not associated with RFS or OS in either cohort (P value range, .48-.97). However, in the Asian cohort, homogeneous intratumoral fat was associated with longer RFS (hazard ratio [HR], 0.60; P = .009) and OS (HR, 0.33; P = .008) in multivariable Cox regression analyses. Conclusion MRI-assessed intratumoral fat was more frequent in steatohepatitic HCCs and associated with nonperipheral washout, enhancing capsule, and mosaic architecture. Although intratumoral fat was generally nonprognostic, homogeneous intratumoral fat was associated with longer RFS and OS in the Asian cohort. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Harmath in this issue.

Inter-Reader Agreement for Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System Major Features and Final Categorization: A Subanalysis From a Prospective Multicenter Study.

Contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) is used to definitively diagnose hepatocellular carcinoma (HCC) in patients at risk. However, the user variability associated with CEUS LI-RADS has not been validated in North American and European patients. This study aims to evaluate the inter-reader agreements of CEUS LI-RADS features for, and final categorization of, HCC in patients at risk. This retrospective multicenter clinical study used the database of a previous prospective multinational study, evaluating the accuracy of CEUS LI-RADS for HCC diagnosis in patients at risk. All cases were first evaluated by a site physician performing/supervising the CEUS examination. Randomly selected cases were re-evaluated by a blinded central reader. Final diagnosis was confirmed with the reference standard, which was a composite of imaging tests and histology. Cohen's kappa test was used to evaluate inter-reader agreement. This study included 150 liver nodules and 58.0% (87/150) were confirmed as HCC, 4.7% (7/150) non-HCC malignancies, 22.7% (34/150) had no confirmed final diagnosis, and 14.7% (22/150) were nonmalignant. Inter-reader agreements were substantial for CEUS LI-RADS categorization (κ = 0.61; 95% confidence interval [CI]: 0.51-0.71) and major features assessment (ranged κ = 0.64-0.78), LR-5 (κ = 0.65; 95% CI: 0.52-0.77), and LR-M (κ = 0.67; 95% CI: 0.44-0.90), while for LR-1 and LR-2 categorization was almost perfect (κ = 0.85; 95% CI: 0.65-1.00). Our study reported a substantial inter-reader agreement for overall CEUS LI-RADS categorization, especially for LR-5 and LR-M, and major imaging features of HCC, further confirming CEUS LI-RADS as a valuable and reliable tool for diagnosis of HCC.

Lessons learned: strategies for implementing and the ongoing use of LI-RADS in your practice.

The establishment of the Liver Imaging Reporting and Data System (LI-RADS) in 2011 provided a comprehensive approach to standardized imaging, interpretation, and reporting of liver observations in patients diagnosed with or at risk for hepatocellular carcinoma (HCC). Each set of algorithms provides criteria pertinent to the various components of HCC management including surveillance, diagnosis, staging, and treatment response supported by a detailed lexicon of terms applicable to a wide range of liver imaging scenarios. Before its widespread adoption, the variability in the terminology of diagnostic criteria and definitions of imaging features led to significant challenges in patient management and made it difficult to replicate findings or apply them consistently. The integration of LI-RADS into the clinical setting has enhanced the efficiency and clarity of communication between radiologists, referring providers, and patients by employing a uniform language that averts miscommunications. LI-RADS has been strengthened with its integration into the American Association for Study of Liver Diseases practice guidelines. We will provide the background on the initial development of LI-RADS and reasons for development to serve as a starting point for conveying the system's benefits and evolution over the years. We will also suggest strategies for the implementation and maintenance of a LI-RADS program will be discussed.

CT/MRI technical pitfalls for diagnosis and treatment response assessment using LI-RADS and how to optimize.

Hepatocellular carcinoma (HCC), the most common primary liver cancer, is a significant global health burden. Accurate imaging is crucial for diagnosis and treatment response assessment, often eliminating the need for biopsy. The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation and reporting of liver imaging for diagnosis and treatment response assessment, categorizing observations using defined categories that are based on the probability of malignancy or post-treatment tumor viability. Optimized imaging protocols are essential for accurate visualization and characterization of liver findings by LI-RADS. Common technical pitfalls, such as suboptimal postcontrast phase timing, and MRI-specific challenges like subtraction misregistration artifacts, can significantly reduce image quality and diagnostic accuracy. The use of hepatobiliary contrast agents introduces additional challenges including arterial phase degradation and suboptimal uptake in advanced cirrhosis. This review provides radiologists with comprehensive insights into the technical aspects of liver imaging for LI-RADS. We discuss common pitfalls encountered in routine clinical practice and offer practical solutions to optimize imaging techniques. We also highlight technical advances in liver imaging, including multi-arterial MR acquisition and compressed sensing. By understanding and addressing these technical aspects, radiologists can improve accuracy and confidence in the diagnosis and treatment response assessment for hepatocellular carcinoma.

LI-RADS radiation-based treatment response algorithm for HCC: what to know and how to use it.

Locoregional treatments (LRT) continue to advance for hepatocellular carcinoma (HCC). Selective internal radiation therapy (SIRT) or transarterial radioembolization (TARE) with radioactive 90 Yttrium (Y90) microspheres is currently widely accepted, and external beam and stereotactic body radiation (EBRT/SBRT) are increasingly used as LRT1-5. Assessment of treatment response after these radiation-based therapies can be challenging, given that the adjacent liver also undergoes treatment related changes, inflammatory changes occur, and there is a variable time for response to develop. In 2017, the liver imaging reporting and data system (LI-RADS) workgroup initially developed a single algorithm for the imaging assessment of treatment response encompassing all types of locoregional therapies, the LI-RADS treatment response (LR-TR) algorithm. Recognizing that response and imaging patterns differ between radiation and non-radiation based therapies, the LR-TR working group recently updated the algorithm to reflect the unique characteristics of tumor response for therapies involving radiation. This article aims to elucidate the changes in the new version of the LI-RADS TR, with a guide for algorithm utilization and illustration of expected and unexpected findings post liver directed therapies for HCC.

Machine learning model based on dynamic contrast-enhanced ultrasound assisting LI-RADS diagnosis of HCC: A multicenter diagnostic study

BackgroundTo enhance the accuracy of hepatocellular carcinoma (HCC) diagnosis using contrast-enhanced (CE) US, the American College of Radiology developed the CEUS Liver Imaging Reporting and Data System (LI-RADS). However, the system still exhibits limitations in distinguishing between HCC and non-HCC lesions. PurposeTo investigate the viability of employing machine learning methods based on quantitative parameters of contrast-enhanced ultrasound for distinguishing HCC within LR-M nodules. Materials and methodsThis retrospective analysis was conducted on pre-treatment CEUS data from liver nodule patients across multiple centers between January 2013 and June 2022. Quantitative analysis was performed using CEUS images, and the machine learning diagnostic models based on quantitative parameters were utilized for the classification diagnosis of LR-M nodules. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC) and compared with the performance of four radiologists. ResultsThe training and internal testing datasets comprised 168 patients (median age, 53 years [IQR, 18 years]), while the external testing datasets from two other centers included 110 patients (median age, 54 years [IQR, 16 years]). In the internal independent test set, the top-performing Random Forest model achieved an AUC of 0.796 (95%CI: 0.729–0.853) for diagnosing HCC. This model exhibited a sensitivity of 0.752 (95%CI: 0.750–0.755) and a specificity of 0.761 (95%CI: 0.758–0.764), outperforming junior radiologists who achieved an AUC of 0.619 (95%CI: 0.543–0.691, p < .01) with sensitivity and specificity of 0.716 (95%CI: 0.713–0.718) and 0.522 (95%CI: 0.519–0.526), respectively. ConclusionSignificant differences in contrast-enhanced ultrasound quantitative parameters are observed between HCC and non-HCC lesions. Machine learning models leveraging these parameters effectively distinguish HCC categorized as LR-M, offering a valuable adjunct for the accurate classification of liver nodules within the CEUS LI-RADS framework.

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.

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. 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. 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). Twenty-nine studies comprising 2591 observations in 1456 patients (mean age 59years, 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. 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.

Application of a deep learning algorithm for the diagnosis of HCC

Background & AimsHepatocellular carcinoma (HCC) is characterized by a high mortality rate. The Liver Imaging Reporting and Data System (LI-RADS) results in considerable proportions of indeterminate observations, rendering an accurate diagnosis difficult. MethodsWe 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. ResultsFrom 2,832 patients and 4,305 CT observations, the best-performing model was Spatio-Temporal 3D Convolution Network (ST3DCN), achieving area under curves (AUCs) of 0.919 (95%CI 0.903-0.935) and 0.901 (95%CI 0.879-0.924) at the observation (n=1077) and patient (n=685) levels respectively during internal validation, compared to 0.839 (95%CI 0.814-0.864) and 0.822 (95%CI 0.790-0.853) respectively for standard-of-care radiological interpretation. ST3DCN’s negative predictive values were 0.966 (95%CI 0.954-0.979) and 0.951 (95%CI 0.931-0.971) respectively. ST3DCN’s observation-level AUCs among at-risk patients, 2-5 cm observations and singular porto-venous phase analysis 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), ST3DCN’s AUC was 0.901 (95%CI 0.877-0.924), non-inferior to radiological interpretation (AUC 0.900, 95%CI 0.877-923). ConclusionsST3DCN achieved strong, robust performance for accurate HCC diagnosis on CT. Deep learning can expedite and improve the diagnostic process of HCC. Impact and implicationsThe clinical applicability of deep learning in HCC diagnosis is potentially huge, especially considering the expected increase in the incidence and mortality of HCC in Eastern Asia and 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 current high mortality rate of HCC.

Correlation of Needle Biopsy–Acquired Histopathologic Grade of Hepatocellular Carcinoma with Outcomes after Thermal Ablation

PurposeTo correlate preablation needle biopsy–acquired histopathologic grade of Liver Imaging Reporting and Data System (LI-RADS) 5 hepatocellular carcinoma (HCC) with postablation local tumor control rate, intrahepatic distant tumor progression–free survival, and overall survival. Materials and MethodsThis single-center, retrospective cohort study included adult patients with LI-RADS 5 HCC who underwent a preablation core needle biopsy within 3 months prior to thermal ablation from January 2015 to December 2022. Histopathologic grade from the needle biopsy was evaluated as predictor of local tumor control rate, intrahepatic distant tumor progression–free survival, and overall survival. Kaplan-Meier survival curves were compared using the Gehan generalized Wilcoxon test. ResultsThe study group comprised 133 patients (mean age, 67 years [SD ± 10]; 107 men) with LI-RADS 5 confirmed HCC, stratified into 18 poorly differentiated tumors (median follow-up, 27.7 months; interquartile range [IQR], 15.5–55.4 months) and 115 well-differentiated/moderately differentiated tumors (median follow-up, 29.2 months; IQR, 15.4–59.9 months). No difference in local tumor control rate was noted between the 2 cohorts (hazard ratio [HR], 1.16; 95% CI, 0.32–4.23; P = .898). There was significantly lower intrahepatic distant tumor progression–free survival after thermal ablation in the poorly differentiated cohort (HR, 2.54; 95% CI, 0.92–7.05; P < .001). The overall survival in the poorly differentiated cohort was also lower, although this did not reach statistical significance (HR, 1.77; 95% CI, 0.60–5.26; P = .202). ConclusionsPatients with needle biopsy–proven poorly differentiated LI-RADS 5 HCC had significantly lower intrahepatic distant tumor progression–free survival after thermal ablation compared with those with well-differentiated/moderately differentiated HCC.

Evaluation of combined hepatocellular-cholangiocarcinoma using CEUS LI-RADS: correlation with pathological characteristics.

To explore the factors that influence the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) classification of combined hepatocellular-cholangiocarcinoma (cHCC-ICC). Between September 2014 to July 2020, the CEUS features of 58 patients with pathologically confirmed cHCC-ICC were retrospectively evaluated and assigned according to the CEUS LI-RADS (version 2017). The pathological characteristics of nodules categorizing as different CEUS LI-RADS categories were compared. Multivariate logistic regression analysis was conducted to explore potential factors that may influence the CEUS LI-RADS classification of cHCC-ICC. According to CEUS LI-RADS, 32.8% (19/58), 63.8% (37/58), and 3.4% (2/58) were categorized as LR-5, LR-M, and LR-TIV, respectively. There was significant difference between the LR-M and LR-5 groups with regard to the pathological grade, nodule size, and HCC/ICC-component ratio of cHCC-ICC. Multivariate logistic regression analysis identified tumor size and the relative proportions of hepatocellular carcinomas (HCC) and intrahepatic cholangiocarcinomas (ICC) components as the independent influencing factors. Tumor size and the relative proportion of HCC and ICC components within the nodule had a significant impact on the CEUS LI-RADS classification of cHCC-ICC.

Training on contrast-enhanced ultrasound LI-RADS classification for resident radiologists: a retrospective comparison of performance after training

ObjectivesTo evaluate the effects and benefits of training radiology residents on contrast-enhanced ultrasound (CEUS) according to the Liver Imaging Reporting and Data System (LI-RADS).MethodsIn total, 234 patients at high risk of hepatocellular carcinoma (HCC) who underwent CEUS were enrolled, including 27 lesions in the education set and 207 lesions in the test sets (a–d). Forty-five radiology residents and 4 radiology experts involved in CEUS LI-RADS training individually reviewed the test sets before, immediately after, and 3-months after training. The consistency with kappa values of the description of CEUS features, the classification of focal liver lesions (FLLs), and the diagnostic performance were evaluated.ResultsThe level of agreement between the radiology experts and residents improved after training (all p < 0.05), while there were no significant differences between the post-training and 3-months post-training results (all p > 0.05). The sensitivity, specificity, positive predictive value, and area under the curve (AUC) based on the CEUS LI-RADS classification of the radiology experts in the diagnosis of HCC were 62.9%, 96.4%, 96.3%, and 0.796, respectively. The diagnostic performance of the radiology residents significantly improved after training (all p < 0.05). Misunderstanding of definitions and subjective interpretation of images were the main reasons for disagreement with multiple responses.ConclusionDedicated CEUS LI-RADS training improved the performance of radiology residents in diagnosing FLLs and their agreement with radiology experts on CEUS features. Images and videos to explain typical features of the training were essential to improve agreement between the radiology experts and residents.Critical relevance statementAgreement on lesion descriptors between radiology experts and residents can improve with training.Key PointsThe diagnostic performance of less experienced radiologists for diagnosing HCC could be improved by training.Images and videos to explain typical features during training were essential.Agreement on lesion descriptors between radiology experts and residents improved after training.Graphical

Non-contrast abbreviated MRI for the detection of hepatocellular carcinoma in patients with Liver Imaging Reporting and Data System LR-3 and LR-4 observations in MRI.

With ultrasound sensitivity limited in hepatocellular carcinoma (HCC) surveillance and few prospective studies on non-contrast abbreviated MRI (NC-AMRI), this study aimed to assess its diagnostic performance in detecting HCC. This prospective study involved cirrhotic patients with contrast-enhanced MRI (CE-MRI) Liver Imaging Reporting and Data System (LI-RADS) LR-3 and LR-4 observations detected during HCC surveillance. Patients underwent average 3 complete CE-MRI rounds at 3-6 months interval, with approximately 12-month follow-up. NC-AMRI included diffusion-weighted (DWI), T2-weighted imaging (T2WI), and T1-weighted imaging (T1WI). NC-AMRI protocol images were analysed for diagnostic performance, with subgroup analyses. CE-MRI and NC-AMRI images were independently reviewed by 2 experienced radiologists, with inter-reader agreement assessed with Kappa coefficient. The reference standard was the American Association for the Study of Liver Diseases-defined presence of arterial hypervascularity and washout during the portal-venous or delayed phases on CE-MRI. In 166 CE-MRI follow-ups of 63 patients (median age: 63 years; 60.3% male, 39.7% female), 12 patients developed HCC, with average size of 19.6 mm. The NC-AMRI (DWI + T2WI + T1WI) showed 91.7% sensitivity (95%CI, 61.5-99.8) and 91.6% specificity (95%CI, 86.0-95.4), area under receiver operating characteristic 0.92 (95%CI, 0.83-1.00). Across different Body Mass Index categories, lesion size, Child-Turcotte-Pugh classes, Albumin-Bilirubin (ALBI) grades, and Model for End-Stage Liver Disease classes, sensitivity remained consistent. However, specificity differed significantly between ALBI grade 1 and 2 (86.7% vs. 98.4%, P = .010), and between viral and non-viral cirrhosis (93.8% vs. 80.8%, P = .010). NC-AMRI proved clinically feasible, and exhibits high diagnostic performance in HCC detection. This study highlights efficacy of NC-AMRI in detecting HCC among cirrhotic patients with LR-3 and LR-4 observations, representing significant progress in HCC surveillance.

Intraindividual comparison of CT and MRI for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.

To establish and validate scoring models for predicting vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) using computed tomography (CT) and magnetic resonance imaging (MRI), and to intra-individually compare the predictive performance between the two modalities. We retrospectively included 324 patients with surgically confirmed HCC who underwent preoperative dynamic CT and MRI with extracellular contrast agent between June 2019 and August 2020. These patients were then divided into a discovery cohort (n = 227) and a validation cohort (n = 97). Imaging features and Liver Imaging Reporting and Data System (LI-RADS) categories of VETC-positive HCCs were evaluated. Logistic regression analyses were conducted on the discovery cohort to identify clinical and imaging predictors associated with VETC-positive cases. Subsequently, separate CT-based and MRI-based scoring models were developed, and their diagnostic performance was compared using generalized estimating equations. On both CT and MRI, VETC-positive HCCs exhibited a higher frequency of size > 5.0 cm, necrosis or severe ischemia, non-smooth tumor margin, targetoid appearance, intratumor artery, and heterogeneous enhancement with septations or irregular ring-like structure compared to VETC-negative HCCs (all p < 0.05). Regarding LI-RADS categories, VETC-positive HCCs were more frequently categorized as LR-M than VETC-negative cases (all p < 0.05). In the validation cohort, the CT-based model showed similar sensitivity (76.7% vs. 86.7%, p = 0.375), specificity (83.6% vs. 74.6%, p = 0.180), and area under the curve value (0.80 vs. 0.81, p = 0.910) to the MRI-based model in predicting VETC-positive HCCs. Preoperative CT and MRI demonstrated comparable performance in the identification of VETC-positive HCCs, thus displaying promising predictive capabilities. Both computed tomography and magnetic resonance imaging demonstrated promise in preoperatively identifying the vessel-encapsulating tumor cluster pattern in hepatocellular carcinoma, with no statistically significant difference between the two modalities, potentially adding additional prognostic value. Computed tomography (CT) and magnetic resonance imaging (MRI) show promise in the preoperative identification of vessels encapsulating tumor clusters-positive hepatocellular carcinoma (HCC). HCC with vessels encapsulating tumor cluster patterns were more frequently LR-M compared to those without. These CT and MRI models showed comparable ability in identifying vessels encapsulating tumor clusters-positive HCC.

The Value of CEUS LI-RADS combined with AFP in early diagnosis of hepatocellular carcinoma in low- and high-risk patients.

We found that the occurrence of hepatocellular carcinoma (HCC) has increased significantly in non-cirrhotic individuals, with HCC being frequently overlooked or misdiagnosed. Contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) is known to have a high diagnostic quality in high-risk HCC patients. Therefore, we aimed to compare the detection accuracy of CEUS LI-RADS for HCC between low- and high-risk individuals, to confirm its value in low-risk patients at increased risk of HCC, but not yet included in the high-risk groups of LI-RADS. In addition, since CEUS LR-4 and LR-M categories contain a relatively high proportion of HCC, and serum alpha-fetoprotein (AFP) is the most commonly used biomarker for HCC, and the clinically valid, we attempted to further improve the early diagnostic capability of CEUS LI-RADS for HCC in the low-risk and high-risk patients by combining CEUS LR-4 and LR-M categories with AFP. We defined high-risk groups (HR)-included in the high-risk patients of LI-RADS, low-risk groups (LR)-not included in the high-risk patients of LI-RADS and enrolled 189 HCC patients with LR and HR settings in a retrospective study. All lesions were confirmed histopathologically. The CEUS LI-RADS accuracy for detecting HCC in these two patients was compared. In addition, the diagnostic algorithm in our study was proposed (for CEUS LR-4 and LR-M patients with AFP>20 ng/ml). we analyzed the ability of CEUS LI-RADS as a valid method of establishing the early diagnosis of HCC in LR and HR patients by combining LR-4 and LR-M categories with AFP. Through comparative analysis, the specificity of the CEUS LR-5 category for HCC in the HR group was 78.4%, whereas in the LR group, it was 94.2%. Meanwhile, the sensitivity (63.2% vs. 63.0%) and positive predictive value (PPV) (75.0% vs. 88.7%) did not differ between the LR and HR groups ( P = 0.990, P = 0.299). It is noteworthy that there were the high proportion of HCC in CEUS LR-4 and LR-M categories in our cases and when we combined CEUS LR-4 and LR-M categories with AFP significantly improved the sensitivity by 21.0% (84.2%) in the LR group, and by 16.0% (79.0%) in the HR group, with statistically difference in sensitivity after combination in the HR group ( P = 0.014). The CEUS LR-5 category has real meaningful utility in the diagnosis of HCC in both LR and HR patients. The early detection power of the CEUS LI-RADS category for HCC patients was further increased when the CEUS LR-4 and LR-M categories were combined with elevated AFP.

Diagnostic performance of CT/MRI LI-RADS v2018 in non-cirrhotic steatotic liver disease.

To assess the performance of computed tomography (CT)/magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) among patients with non-cirrhotic steatotic liver disease (SLD). This IRB-approved, retrospective study included 119 observations from 77 adult patients (36 women, 41 men; median 64 years) who underwent liver CT or MRI from 2010 to 2023. All patients had histopathologic evidence of SLD without cirrhosis. Three board-certified abdominal radiologists blinded to tissue diagnosis and imaging follow-up assessed observations with LI-RADS. The positive predictive value (PPV), sensitivity, specificity, accuracy, and inter-reader agreement were calculated. Seventy-five observations (63%) were benign and 44 (37%) were malignant. PPV for hepatocellular carcinoma (HCC) was 0-0% for LR-1, 0-0% for LR-2, 0-7% for LR-3, 11-20% for LR-4, 75-88% for LR-5, 0-8% for LR-M, and 50-75% for LR-TIV. For LR-5 in identifying HCC, sensitivity was 79-83%, specificity was 91-97%, and accuracy was 89-92%. For composite categories of LR-5, LR-M, or LR-TIV in identifying malignancy, sensitivity was 86-89%, specificity was 85-96%, and accuracy was 86-93%. The most common false positives for LR-5 were hepatocellular adenomas. Only 59-65% of HCCs showed non-peripheral washout at CT versus 67-83% at MRI, though nearly all had an enhancing capsule. PPV and accuracy of LR-5 for HCC did not differ by modality. Inter-reader agreement for major features ranged from 0.667 to 0.830 and was 0.766 for the final category. Despite challenges such as the lower prevalence of non-peripheral washout at CT and overlapping imaging features between HCC and hepatocellular adenomas, LI-RADS may serve as an effective tool in assessing focal liver lesions in SLD. LI-RADS in non-cirrhotic steatotic liver disease can effectively diagnose hepatocellular carcinoma and malignancy at computed tomography and magnetic resonance imaging, thereby guiding clinical management decisions and expediting patient care pathways. Performance of LI-RADS is unknown in non-cirrhotic patients with steatotic liver disease. LI-RADS 5 category showed a high pooled specificity of 91-97% for hepatocellular carcinoma. LI-RADS can non-invasively risk stratify focal liver observations in non-cirrhotic patients with steatotic liver disease.

The American College of Radiology contrast-enhanced ultrasound Liver Imaging Reporting and Data System and its modified version in diagnosing hepatocellular carcinoma via Sonazoid: a meta-analysis.

The American College of Radiology (ACR) developed the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) for pure blood contrast agents, but Sonazoid was not included. Modifications to LI-RADS have been proposed for Sonazoid. The purpose of this meta-analysis was to identify and compare the diagnostic efficacy of the two LI-RADS algorithms of Sonazoid. We searched the PubMed, MEDLINE, Web of Science, Embase, and Cochrane Library databases from databases inception to August 31, 2023, to find original studies on the ACR LI-RADS and/or modified LI-RADS algorithm with Sonazoid used as the contrast agent in patients with high-risk hepatocellular carcinoma (HCC). A bivariate random-effects model was used. Data pooling, meta-regression, and sensitivity analysis were performed for meta-analysis. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to assess the methodological quality, and the Deeks funnel plot asymmetry test was used to evaluate the publication bias. A meta-analysis of 10 studies with 1,611 observations was conducted. The pooled data for ACR LI-RADS category 5 (LR-5) and modified LR-5 were respectively as follows: pooled sensitivity, 0.70 [95% confidence interval (CI): 0.64-0.75] and 0.81 (95% CI: 0.76-0.86) (P<0.05); pooled specificity, 0.90 (95% CI: 0.82-0.94) and 0.87 (95% CI: 0.81-0.91) (P>0.05); and pooled area under the summary receiver operating characteristic curve, 0.84 and 0.91. The diagnostic performance of LI-RADS category M (LR-M) of the two algorithms was comparable. Study heterogeneity was observed. The results indicated that modified LR-5 algorithm demonstrated improved diagnostic sensitivity compared with the ACR LR-5 algorithm of Sonazoid, with differences observed between the different versions. Further research is needed to validate and explore the optimal diagnostic criteria for HCC using Sonazoid. Before the database search was conducted, this study was registered on PROSPERO (International Prospective Register of Systematic Reviews; CRD42023455220).

Simplified liver imaging reporting and data system for the diagnosis of hepatocellular carcinoma on gadoxetic acid-enhanced magnetic resonance imaging.

The liver imaging reporting and data system (LI-RADS) diagnostic table has 15 cells and is too complex. The diagnostic performance of LI-RADS for hepatocellular carcinoma (HCC) is not satisfactory on gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI). To evaluate the ability of the simplified LI-RADS (sLI-RADS) to diagnose HCC on EOB-MRI. A total of 331 patients with 356 hepatic observations were retrospectively analysed. The diagnostic performance of sLI-RADS A-D using a single threshold was evaluated and compared with LI-RADS v2018 to determine the optimal sLI-RADS. The algorithms of sLI-RADS A-D are as follows: The single threshold for sLI-RADS A and B was 10 mm, that is, classified observations ≥ 10mm using an algorithm of 10-19 mm observations (sLI-RADS A) and ≥ 20 mm observations (sLI-RADS B) in the diagnosis table of LI-RADS v2018, respectively, while the classification algorithm remained unchanged for observations < 10 mm; the single threshold for sLI-RADS C and D was 20 mm, that is, for < 20 mm observations, the algorithms for < 10 mm observations (sLI-RADS C)and 10-19 mm observations (sLI-RADS D) were used, respectively, while the algorithm remained unchanged for observations ≥ 20 mm. With hepatobiliary phase (HBP) hypointensity as a major feature (MF), the final sLI-RADS (F-sLI-RADS) was formed according to the optimal sLI-RADS, and its diagnostic performance was evaluated. The times needed to classify the observations according to F-sLI-RADS and LI-RADS v2018 were compared. The optimal sLI-RADS was sLI-RADS D (with a single threshold of 20 mm), because its sensitivity was greater than that of LI-RADS v2018 (89.8% vs 87.0%, P = 0.031), and its specificity was not lower (89.4% vs 90.1%, P > 0.999). With HBP hypointensity as an MF, the sensitivity of F-sLI-RADS was greater than that of LI-RADS v2018 (93.0% vs 87.0%, P < 0.001) and sLI-RADS D (93.0% vs 89.8%, P = 0.016), without a lower specificity (86.5% vs 90.1%, P = 0.062; 86.5% vs 89.4%, P = 0.125). Compared with that of LI-RADS v2018, the time to classify lesions according to F-sLI-RADS was shorter (51 ± 21 s vs 73 ± 24 s, P < 0.001). The use of sLI-RADS with HBP hypointensity as an MF may improve the sensitivity of HCC diagnosis and reduce lesion classification time.

LI-RADS Category Can Be a Post-Surgical Prognostic Factor for Intrahepatic Cholangiocarcinoma in Patients with Liver Cirrhosis or Chronic Hepatitis B

&lt;b&gt;&lt;i&gt;Introduction:&lt;/i&gt;&lt;/b&gt; The Liver Imaging Reporting and Data System (LI-RADS) categorization has been proposed as a potential prognostic indicator for primary liver neoplasms in patients with liver cirrhosis or chronic hepatitis B. This multicenter study aimed to determine whether LI-RADS categorization can offer additional post-surgical prognostic value for intrahepatic cholangiocarcinoma (ICCA) when used in conjunction with the American Joint Committee on Cancer (AJCC) guidelines. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; Patients with high risk for hepatocellular carcinoma, surgically confirmed ICCAs, and available preoperative MRI were enrolled. LI-RADS categorization of ICCAs was performed using MRI features, and multivariate analyses were conducted incorporating LI-RADS category, AJCC staging, and clinicopathologic factors to evaluate their predictive value for postoperative recurrence-free survival (RFS) and overall survival (OS). In patients with early recurrence (&amp;lt;2 years), the percentages of AJCC stage I and LR-M or LR tumor-in-vein (TIV) were calculated, respectively. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; Among the 166 ICCAs analyzed, 13.3% (22/166) were classified as LR-4/5, 77.7% (129/166) as LR-M, and 9.0% (15/166) as LR TIV. Classifications according to the 8th AJCC guidelines for patients with available post-surgical pathologic data and follow-up imaging were 40.6% (63/155) stage I tumors, 23.9% (37/155) stage II, and 35.5% (55/155) stage III. Multivariate analysis revealed that LI-RADS category (LR-M or LR-TIV) was a significant factor for predicting both RFS (hazard ratio [HR] = 2.86, &lt;i&gt;p&lt;/i&gt; = 0.02) and OS (HR = 3.18, &lt;i&gt;p&lt;/i&gt; = 0.03). Additionally, AJCC staging (II or III) was a significant factor for RFS (HR = 3.90, &lt;i&gt;p&lt;/i&gt; &amp;lt; 0.001) and OS (HR = 3.29, &lt;i&gt;p&lt;/i&gt; &amp;lt; 0.001), male sex was a significant factor for RFS (HR = 1.89, &lt;i&gt;p&lt;/i&gt; = 0.006) and OS (HR = 2.23, &lt;i&gt;p&lt;/i&gt; = 0.002), and positive resection margin was a significant factor for OS (HR = 1.91, &lt;i&gt;p&lt;/i&gt; = 0.03). Among the 80 patients with early recurrence, 97.5% displayed LR-M or LR-TIV features, while 11.3% were AJCC stage I patients. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; The MRI-based preoperative LI-RADS categorization of ICCA provides additional post-surgical prognostic value beyond the AJCC guidelines, with significant implications for both RFS and OS.

Clinical impact of CEUS on non-characterizable observations and observations with intermediate probability of malignancy on CT/MRI in patients at risk for HCC

BackgroundHepatocellular carcinoma (HCC) is a unique cancer allowing tumor diagnosis with identification of definitive patterns of enhancement on contrast-enhanced imaging, avoiding invasive biopsy. However, it is still unclear to what extent Contrast-Enhanced Ultrasound (CEUS) is a clinically useful additional step when Computed tomography (CT) or Magnetic resonance imaging (MRI) are inconclusive.MethodsA prospective international multicenter validation study for CEUS Liver Imaging Reporting and Data System (LI-RADS) was conducted between January 2018 and August 2021. 646 patients at risk for HCC with focal liver lesions were enrolled. CEUS was performed using an intravenous ultrasound contrast agent within 4 weeks of CT/MRI. Liver nodules were categorized based on LI-RADS (LR) criteria. Histology or one-year follow-up CT/MRI imaging results were used as the reference standard. The diagnostic performance of CEUS was evaluated for inconclusive CT/MRI scan in two scenarios for which the AASLD recommends repeat imaging or imaging follow-up: observations deemed non-characterizable (LR-NC) or with indeterminate probability of malignancy (LR-3).Results75 observations on CT or MRI were categorized as LR-3 (n = 54) or LR-NC (n = 21) CEUS recategorization of such observations into a different LR category (namely, into one among LR-1, LR-2, LR-5, LR-M, or LR-TIV) resulted in management recommendation changes in 33.3% (25/75) and in all but one (96.0%, 24/25) observation, the new management recommendations were correct.ConclusionCEUS LI-RADS resulted in management recommendations change in substantial number of liver observations with initial indeterminate CT/MRI characterization, identifying both non-malignant lesions and HCC, potentially accelerating the diagnostic process and alleviating the need for biopsy or follow-up imaging.ClinicalTrials.gov number, NCT03318380.