Liver Imaging Reporting And Data System Research Articles

Congestive Hepatopathy: Pathophysiology, Workup, and Imaging Findings with Pathologic Correlation.

Liver congestion is increasingly encountered in clinical practice and presents diagnostic pitfalls of which radiologists must be aware. The complex altered hemodynamics associated with liver congestion leads to diffuse parenchymal changes and the development of benign and malignant nodules. Distinguishing commonly encountered benign hypervascular lesions, such as focal nodular hyperplasia (FNH)-like nodules, from hepatocellular carcinoma (HCC) can be challenging due to overlapping imaging features. FNH-like lesions enhance during the hepatic arterial phase and remain isoenhancing relative to the background liver parenchyma but infrequently appear to wash out at delayed phase imaging, similar to what might be seen with HCC. Heterogeneity, presence of an enhancing capsule, washout during the portal venous phase, intermediate signal intensity at T2-weighted imaging, restricted diffusion, and lack of uptake at hepatobiliary phase imaging point toward the diagnosis of HCC, although these features are not sensitive individually. It is important to emphasize that the Liver Imaging Reporting and Data System (LI-RADS) algorithm cannot be applied in congested livers since major LI-RADS features lack specificity in distinguishing HCC from benign hypervascular lesions in this population. Also, the morphologic changes and increased liver stiffness caused by congestion make the imaging diagnosis of cirrhosis difficult. The authors discuss the complex liver macro- and microhemodynamics underlying liver congestion; propose a more inclusive approach to and conceptualization of liver congestion; describe the pathophysiology of liver congestion, hepatocellular injury, and the development of benign and malignant nodules; review the imaging findings and mimics of liver congestion and hypervascular lesions; and present a diagnostic algorithm for approaching hypervascular liver lesions. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Using GPT-4 for LI-RADS feature extraction and categorization with multilingual free-text reports.

The Liver Imaging Reporting and Data System (LI-RADS) offers a standardized approach for imaging hepatocellular carcinoma. However, the diverse styles and structures of radiology reports complicate automatic data extraction. Large language models hold the potential for structured data extraction from free-text reports. Our objective was to evaluate the performance of Generative Pre-trained Transformer (GPT)-4 in extracting LI-RADS features and categories from free-text liver magnetic resonance imaging (MRI) reports. Three radiologists generated 160 fictitious free-text liver MRI reports written in Korean and English, simulating real-world practice. Of these, 20 were used for prompt engineering, and 140 formed the internal test cohort. Seventy-two genuine reports, authored by 17 radiologists were collected and de-identified for the external test cohort. LI-RADS features were extracted using GPT-4, with a Python script calculating categories. Accuracies in each test cohort were compared. On the external test, the accuracy for the extraction of major LI-RADS features, which encompass size, nonrim arterial phase hyperenhancement, nonperipheral 'washout', enhancing 'capsule' and threshold growth, ranged from .92 to .99. For the rest of the LI-RADS features, the accuracy ranged from .86 to .97. For the LI-RADS category, the model showed an accuracy of .85 (95% CI: .76, .93). GPT-4 shows promise in extracting LI-RADS features, yet further refinement of its prompting strategy and advancements in its neural network architecture are crucial for reliable use in processing complex real-world MRI reports.

Diagnostic Model for Proliferative HCC Using LI-RADS: Assessing Therapeutic Outcomes in Hepatectomy and TKI-ICI Combination.

Proliferative hepatocellular carcinoma (HCC), aggressive with poor prognosis, and lacks reliable MRI diagnosis. To develop a diagnostic model for proliferative HCC using liver imaging reporting and data system (LI-RADS) and assess its prognostic value. Retrospective. 241 HCC patients underwent hepatectomy (90 proliferative HCCs: 151 nonproliferative HCCs), divided into the training (N = 167) and validation (N = 74) sets. 57 HCC patients received combination therapy with tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). 3.0 T, T1- and T2-weighted, diffusion-weighted, in- and out-phase, T1 high resolution isotropic volume excitation and dynamic gadoxetic acid-enhanced imaging. LI-RADS v2018 and other MRI features (intratumoral artery, substantial hypoenhancing component, hepatobiliary phase peritumoral hypointensity, and irregular tumor margin) were assessed. A diagnostic model for proliferative HCC was established, stratifying patients into high- and low-risk groups. Follow-up occurred every 3-6 months, and recurrence-free survival (RFS), progression-free survival (PFS) and overall survival (OS) in different groups were compared. Fisher's test or chi-square test, t-test or Mann-Whitney test, logistic regression, Harrell's concordance index (C-index), Kaplan-Meier curves, and Cox proportional hazards. Significance level: P < 0.05. The diagnostic model, incorporating corona enhancement, rim arterial phase hyperenhancement, infiltrative appearance, intratumoral artery, and substantial hypoenhancing component, achieved a C-index of 0.823 (training set) and 0.804 (validation set). Median follow-up was 32.5 months (interquartile range [IQR], 25.1 months) for postsurgery patients, and 16.8 months (IQR: 13.2 months) for combination-treated patients. 99 patients experienced recurrence, and 30 demonstrated tumor nonresponse. Differences were significant in RFS and OS rates between high-risk and low-risk groups post-surgery (40.3% vs. 65.8%, 62.3% vs. 90.1%, at 5 years). In combination-treated patients, PFS rates differed significantly (80.6% vs. 7.7% at 2 years). The MR-based model could pre-treatment identify proliferative HCC and assist in prognosis evaluation. Stage 2.

Prognostic performance of MRI LI-RADS version 2018 features and clinical-pathological factors in alpha-fetoprotein-negative hepatocellular carcinoma.

To evaluate the role of the magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) version 2018 features and clinical-pathological factors for predicting the prognosis of alpha-fetoprotein (AFP)-negative (≤ 20ng/ml) hepatocellular carcinoma (HCC) patients, and to compare with other traditional staging systems. We retrospectively enrolled 169 patients with AFP-negative HCC who received preoperative MRI and hepatectomy between January 2015 and August 2020 (derivation dataset:validation dataset = 118:51). A prognostic model was constructed using the risk factors identified via Cox regression analysis. Predictive performance and discrimination capability were evaluated and compared with those of two traditional staging systems. Six risk factors, namely the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade, were associated with recurrence-free survival. The prognostic model constructed using these factors achieved C-index of 0.705 and 0.674 in the derivation and validation datasets, respectively. Furthermore, the model performed better in predicting patient prognosis than traditional staging systems. The model effectively stratified patients with AFP-negative HCC into high- and low-risk groups with significantly different outcomes (p < 0.05). A prognostic model integrating the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade may serve as a valuable tool for refining risk stratification in patients with AFP-negative HCC.

Inter-reader Agreement for CT/MRI LI-RADS Category M Imaging Features: A Systematic Review and Meta-analysis.

To systematically evaluate inter-reader agreement in the assessment of individual Liver Imaging Reporting and Data System (LI-RADS) category M (LR-M) imaging features in computed tomography/magnetic resonance imaging (CT/MRI) LI-RADS v2018, and to explore the causes of poor agreement in LR-M assignment. Original studies reporting inter-reader agreement for LR-M features on multiphasic CT or MRI were identified using the MEDLINE, EMBASE, and Cochrane databases. The pooled kappa coefficient (κ) was calculated using the DerSimonian-Laird random-effects model. Heterogeneity was assessed using Cochran's Q test and I2 statistics. Subgroup meta-regression analyses were conducted to explore the study heterogeneity. In total, 24 eligible studies with 5,163 hepatic observations were included. The pooled κ values were 0.72 (95% confidence interval, 0.65-0.78) for rim arterial phase hyperenhancement, 0.52 (0.39-0.65) for peripheral washout, 0.60 (0.50-0.70) for delayed central enhancement, 0.68 (0.57-0.78) for targetoid restriction, 0.74 (0.65-0.83) for targetoid transitional phase/hepatobiliary phase appearance, 0.64 (0.49-0.78) for infiltrative appearance, 0.49 (0.30-0.68) for marked diffusion restriction, and 0.61 (0.48-0.73) for necrosis or severe ischemia. Substantial study heterogeneity was observed for all LR-M features (Cochran's Q test: p < 0.01; I2 ≥ 89.2%). Studies with a mean observation size of <3 cm, those performed using 1.5-T MRI, and those with multiple image readers, were significantly associated with poor agreement of LR-M features. The agreement for peripheral washout and marked diffusion restriction was limited. The LI-RADS should focus on improving the agreement of LR-M features.

LI-RADS version 2018 treatment response algorithm on extracellular contrast-enhanced MRI in patients treated with transarterial chemoembolization for hepatocellular carcinoma: diagnostic performance and the added value of ancillary features.

The Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Algorithm (TRA) (LI-RADS TRA) is used for assessing response of HCC to locoregional therapy (LRT), however, the value of ancillary features (AFs) for TACE-treated HCCs has not been extensively investigated on extracellular agent MRI (ECA-MRI). To evaluate the diagnostic performance of LI-RADS v2018 TRA on ECA-MRI for HCC treated with transarterial chemoembolization (TACE) and the value of ancillary features. This retrospective study included patients who underwent TACE for HCC and then followed by hepatic surgery between January 2019 and June 2023 with both pre- and post-TACE contrast-enhanced MRI available. Two radiologists independently evaluated the post-treated lesions on MRI using LI-RADS treatment response (TR) (LR-TR) algorithm and modified LR-TR (mLR-TR) algorithm in which ancillary features (restricted diffusion and intermediate T2-weighted hyperintensity) were added, respectively. Lesions were categorized as complete pathologic necrosis (100%, CPN) and non-complete pathologic necrosis (< 100%, non-CPN) on the basis of surgical pathology. The diagnostic performance in predicting viable and non-viable tumors based on LR-TR and mLR-TR algorithms was compared using the McNemar test. Interreader agreement was calculated by using Cohen's weighted and unweighted κ. A total of 61 patients [mean age 59years ± 10 (standard deviation); 47 men] with 79 lesions (57 pathologically viable) were included. For non-CPN prediction, the sensitivity, specificity of LR-TR viable and mLR-TR viable category were 75% (43 of 57), 82% (18 of 22) and 88% (50 of 57), 77% (17 of 22), respectively, the sensitivity of mLR-TR was significantly higher than that of LR-TR (P = 0.016) without difference in specificity (P = 1.000). Interreader agreement for LR-TR and mLR-TR category was moderate (k = 0.50, 95% confidence interval 0.33, 0.67, k = 0.42, 95% confidence interval 0.20, 0.63). The sensitivity of both LR-TR and mLR-TR algorithms in predicting viable tumors between conventional TACE (cTACE) and drug-eluting beads TACE (DEB-TACE) did not have significant difference (cTACE: 76%, 89% vs. DEB-TACE: 73%, 82%). On ECA-MRI, applying ancillary features to LI-RADS v2018 TRA can improve the sensitivity in predicting pathologic tumor viability in patients treated with TACE for hepatocellular carcinoma with no significant difference in specificity.

Compared with SonoVue® LR-5, Sonazoid® modified LR-5 has better diagnostic sensitivity for hepatocellular carcinoma: a systematic review and meta-analysis.

The contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) is a standardized system for reporting liver nodules in patients at risk of developing hepatocellular carcinoma (HCC) and is only recommended for pure blood pool agents such as SonoVue®. A modified LI-RADS was proposed for Sonazoid®, a Kupffer cell-specific contrast agent. This meta-analysis was conducted to compare the diagnostic efficiency of the CEUS LI-RADS for SonoVue® and the modified LI-RADS for Sonazoid®. The PubMed, Medline, Web of Science, Embase, and Cochrane Library databases were systematically searched to retrieve studies on the diagnostic efficiency of the CEUS LI-RADS algorithms in diagnosing HCC using SonoVue® and/or Sonazoid® from January 2016 to June 2023. Histopathology or imaging follow-up served as the reference standards. Only articles published in English on retrospective or prospective studies with full reports were included in the meta-analysis. A bivariate random-effects model was used. Data pooling, meta-regression, and sensitivity analysis were performed for the meta-analysis. Deeks' funnel plot asymmetry test was used to evaluate publication bias, and the QUADAS-2 tool was used to assess the methodological quality of eligible studies. In total, 26 studies comprising 8,495 patients with 9,244 lesions were included in the meta-analysis. The pooled data results for SonoVue® LI-RADS category 5 (LR-5) and Sonazoid® modified LR-5 were as follows: pooled sensitivity: 0.68 [95%confidence interval (CI): 0.64-0.73, I2=89.20%; P<0.01] and 0.82 (95% CI: 0.74-0.87, I2=85.39%; P<0.01) (P<0.05); pooled specificity: 0.93 (95% CI: 0.90-0.96, I2=86.52%; P<0.01) and 0.86 (95% CI: 0.79-0.91, I2=59.91%; P=0.01) (P<0.05); pooled area under the curve (AUC): 0.86 (95% CI: 0.82-0.89) and 0.91 (95% CI: 0.88-0.93) (P<0.05), respectively. The meta-regression analysis revealed that the study design, subject enrollment method, and reference standard contributed to the heterogeneity of SonoVue® LR-5, and the number of lesions was a source of heterogeneity for Sonazoid® modified LR-5. The diagnostic performance of the LI-RADS category M (LR-M) algorithms of SonoVue® and Sonazoid® was comparable. The Sonazoid® modified LR-5 algorithm had a higher diagnostic sensitivity, lower specificity, and higher AUC than SonoVue® LR-5.

Comparison of Four Diagnostic Guidelines for Hepatocellular Carcinoma Using Gadoxetic Acid-enhanced Liver MRI.

Background Noninvasive diagnostic guidelines for hepatocellular carcinoma (HCC) vary across different global geographic areas, especially regarding criteria about gadoxetic acid-enhanced MRI. Purpose To compare the diagnostic performance of four different international HCC diagnosis guidelines and readers' judgment in diagnosing HCC using gadoxetic acid-enhanced MRI in patients at high risk for HCC. Materials and Methods This retrospective study included patients who had not undergone treatment, were at risk for HCC, and who underwent gadoxetic acid-enhanced MRI from January 2015 to June 2018 from 11 tertiary hospitals in South Korea. Four radiologists independently reviewed focal liver lesions (FLLs) according to four guidelines: American Association for the Study of Liver Diseases (AASLD)/Liver Imaging Reporting and Data System (LI-RADS), Korean Liver Cancer Association-National Cancer Center (KLCA-NCC), European Association for the Study of the Liver (EASL), and Asian Pacific Association for the Study of the Liver (APASL). Reader judgment (HCC or not HCC) was also recorded. Malignant FLLs were confirmed at pathology, and histologic and clinical follow-up data were used for benign FLLs. The guidelines' diagnostic performance was compared using generalized estimating equations. Additionally, the diagnostic odds ratio was assessed. Results A total of 2445 FLLs (median size, 27.4 mm) were analyzed in 2237 patients (mean age, 59 years ± 11 [SD]; 1666 male patients); 69.3% (1694 of 2445) were HCCs. KLCA-NCC showed the highest accuracy (80.0%; 95% CI: 78.7, 81.2; P = .001), with high sensitivity in Eastern guidelines (APASL, 89.1% [95% CI: 87.8, 90.3]; KLCA-NCC, 78.2% [95% CI: 76.6, 79.7]) and high specificity in Western guidelines (AASLD/LI-RADS, 89.6% [95% CI: 87.8, 91.2]; EASL, 88.1% [95% CI: 86.2, 89.9]) (P = .001). The diagnostic odds ratios were 20.7 (95% CI: 17.0, 25.3) for AASLD/LI-RADS, 18.9 (95% CI: 15.8, 22.6) for KLCA-NCC, 16.8 (95% CI: 13.8, 20.4) for EASL, and 8.9 (95% CI: 7.4, 10.7) for APASL. The readers' judgment demonstrated higher accuracy than that of the guidelines (accuracy, 86.0%; 95% CI: 84.9, 86.9; P = .001). Conclusion Among four different international HCC diagnosis guidelines, Eastern guidelines demonstrated higher sensitivity, whereas Western guidelines displayed higher specificity. KLCA-NCC achieved the highest accuracy, and AASLD/LI-RADS exhibited the highest diagnostic odds ratio. © RSNA, 2024 Supplemental material is available for this article.

Optimizing LI-RADS: ancillary features screened from LR-3/4 categories can improve the diagnosis of HCC on MRI

ObjectiveTo determine the high-efficiency ancillary features (AFs) screened from LR-3/4 lesions and the HCC/non-HCC group and the diagnostic performance of LR3/4 observations.Materials and methodsWe retrospectively analyzed a total of 460 patients (with 473 nodules) classified into LR-3-LR-5 categories, including 311 cases of hepatocellular carcinoma (HCC), 6 cases of non-HCC malignant tumors, and 156 cases of benign lesions. Two faculty abdominal radiologists with experience in hepatic imaging reviewed and recorded the major features (MFs) and AFs of the Liver Imaging Reporting and Data System (LI-RADS). The frequency of the features and diagnostic performance were calculated with a logistic regression model. After applying the above AFs to LR-3/LR-4 observations, the sensitivity and specificity for HCC were compared.ResultsThe average age of all patients was 54.24 ± 11.32 years, and the biochemical indicators ALT (P = 0.044), TBIL (P = 0.000), PLT (P = 0.004), AFP (P = 0.000) and Child‒Pugh class were significantly higher in the HCC group. MFs, mild-moderate T2 hyperintensity, restricted diffusion and AFs favoring HCC in addition to nodule-in-nodule appearance were common in the HCC group and LR-5 category. AFs screened from the HCC/non-HCC group (AF-HCC) were mild–moderate T2 hyperintensity, restricted diffusion, TP hypointensity, marked T2 hyperintensity and HBP isointensity (P = 0.005, < 0.001, = 0. 032, p < 0.001, = 0.013), and the AFs screened from LR-3/4 lesions (AF-LR) were restricted diffusion, mosaic architecture, fat in mass, marked T2 hyperintensity and HBP isointensity (P < 0.001, = 0.020, = 0.036, < 0.001, = 0.016), which were not exactly the same. After applying AF-HCC and AF-LR to LR-3 and LR-4 observations in HCC group and Non-HCC group, After the above grades changed, the diagnostic sensitivity for HCC were 84.96% using AF-HCC and 85.71% using AF-LR, the specificity were 89.26% using AF-HCC and 90.60% using AF-LR, which made a significant difference (P = 0.000). And the kappa value for the two methods of AF-HCC and AF–LR were 0.695, reaching a substantial agreement.ConclusionWhen adjusting for LR-3/LR-4 lesions, the screened AFs with high diagnostic ability can be used to optimize LI-RADS v2018; among them, AF-LR is recommended for better diagnostic capabilities.

[Multiparametric magnetic resonance imaging for hepatocellular carcinoma, part1 : Morphology and dynamic perfusion imaging in primary diagnostics and treatment monitoring].

Radiology plays akey role in the diagnosis and monitoring of hepatocellular carcinoma (HCC). Ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) are used to identify HCC lesions. Multiparametric MRI provides detailed insights into the tumor biology through the analysis of morphology, perfusion and diffusion. In this way preoperative decisions can be optimized. The guidelines recommend using contrast-enhanced MRI or ultrasound for the diagnosis of HCC. The preferred method is MRI due to its superiority in the detection of small lesions The treatment response is evaluated using modified response evaluation criteria for solid tumors (RECIST) and the European Association for the Study of the Liver (EASL) criteria. The use of multiparametric MRI in conjunction with the liver imaging reporting and data system (LI-RADS) plays overall acentral role in the precise diagnosis and monitoring of the treatment of HCC.

A287 LEVERAGING MACHINE LEARNING TO IMPROVE THE DIAGNOSTIC ACCURACY OF ULTRASOUND SCREENING FOR HEPATOCELLULAR CARCINOMA

Abstract Background Ultrasound screening stands out as the gold standard for hepatocellular carcinoma (HCC) detection, attributed to its broad accessibility, patient-friendly, and cost-efficient nature. Nevertheless, the five-year survival rate for HCC currently rests at 32.7%, with suboptimal screening being a key contributor to this. The recent years have witnessed the rise of machine learning models, powered by artificial neural networks. Among these, convolutional neural networks (CNNs) have taken the lead in revolutionizing medical image analysis, offering unprecedented success in predictive tasks and giving us hope for a brighter future in the fight against HCC Aims To train and test a machine learning algorithm using pre-trained CNNs to improve early detection of HCC through ultrasound screening. Methods In this retrospective study, 1835 charts of patients with chronic liver disease were reviewed: 346 with histologically confirmed HCC and 1457 with ultrasounds without HCC. A diagnosis of HCC was confirmed pathologically on biopsy or surgical resection, and/or radiographically with a Liver Imaging Reporting and Data System (LI-RADS) score of five on CT and/or MRI. Patients with benign lesions were required to have at least two ultrasounds three years apart that confirmed benign characteristics. Cases were excluded if they had a prior history of treated HCC, post-transplant HCC, or HCC with Barcelona Clinic Liver Cancer (BCLC) Stage B and above. All ultrasound images were reviewed by experienced radiologists, and segmented as liver lesions (HCC versus benign) and surrounding liver. Results A total of 149 patients have been included to date, comprising 72 with benign lesions, 73 with HCC, and four with both benign and malignant lesions. 224 lesions have been segmented, consisting of 87 HCC and 137 benign lesions. Candidate networks are under development and evaluation for the classification of liver lesions. Imaging pre-processing was performed such that the liver region of interest (ROI) and the lesion ROI were standardized, with 2-channel greyscale images on two separate channels. The algorithm was constructed using a per-lesion analysis. Initial testing has achieved an area under the curve (AUC) of 73.3%, 95% CI [68.6, 77.0] with 2 repetitions of 10-fold cross-validation. Conclusions Enhancing ultrasound screening for HCC is imperative for improving patient care. Analysis of remaining cases is ongoing. Future studies will be essential, including prospective evaluation and external validation. Funding Agencies None

Association of LI-RADS and Histopathologic Features with Survival in Patients with Solitary Resected Hepatocellular Carcinoma.

Background Both Liver Imaging Reporting and Data System (LI-RADS) and histopathologic features provide prognostic information in patients with hepatocellular carcinoma (HCC), but whether LI-RADS is independently associated with survival is uncertain. Purpose To assess the association of LI-RADS categories and features with survival outcomes in patients with solitary resected HCC. Materials and Methods This retrospective study included patients with solitary resected HCC from three institutions examined with preoperative contrast-enhanced CT and/or MRI between January 2008 and December 2019. Three independent readers evaluated the LI-RADS version 2018 categories and features. Histopathologic features including World Health Organization tumor grade, microvascular and macrovascular invasion, satellite nodules, and tumor capsule were recorded. Overall survival and disease-free survival were assessed with Cox regression models. Marginal effects of nontargetoid features on survival were estimated using propensity score matching. Results A total of 360 patients (median age, 64 years [IQR, 56-70 years]; 280 male patients) were included. At CT and MRI, the LI-RADS LR-M category was associated with increased risk of recurrence (CT: hazard ratio [HR] = 1.83 [95% CI: 1.26, 2.66], P = .001; MRI: HR = 2.22 [95% CI: 1.56, 3.16], P < .001) and death (CT: HR = 2.47 [95% CI: 1.72, 3.55], P < .001; MRI: HR = 1.80 [95% CI: 1.32, 2.46], P < .001) independently of histopathologic features. The presence of at least one nontargetoid feature was associated with an increased risk of recurrence (CT: HR = 1.80 [95% CI: 1.36, 2.38], P < .001; MRI: HR = 1.93 [95% CI: 1.81, 2.06], P < .001) and death (CT: HR = 1.51 [95% CI: 1.10, 2.07], P < .010) independently of histopathologic features. In matched samples, recurrence was associated with the presence of at least one nontargetoid feature at CT (HR = 2.06 [95% CI: 1.15, 3.66]; P = .02) or MRI (HR = 1.79 [95% CI: 1.01, 3.20]; P = .048). Conclusion In patients with solitary resected HCC, LR-M category and nontargetoid features were negatively associated with survival independently of histopathologic characteristics. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kartalis and Grigoriadis in this issue.

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.

Systematic training of LI-RADS CT v2018 improves interobserver agreements and performances in LR categorization for focal liver lesions.

To retrospectively explored whether systematic training in the use of Liver Imaging Reporting and Data System (LI-RADS) v2018 on computed tomography (CT) can improve the interobserver agreements and performances in LR categorization for focal liver lesions (FLLs) among different radiologists. A total of 18 visiting radiologists and the liver multiphase CT images of 70 hepatic observations in 63 patients at high risk of HCC were included in this study. The LI-RADS v2018 training procedure included three thematic lectures, with an interval of 1 month. After each seminar, the radiologists had 1 month to adopt the algorithm into their daily work. The interobserver agreements and performances in LR categorization for FLLs among the radiologists before and after training were compared. After training, the interobserver agreements in classifying the LR categories for all radiologists were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.053). After systematic training, the areas under the curve (AUCs) for LR categorization performance for all participants were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.062). Systematic training in the use of the LI-RADS can improve the interobserver agreements and performances in LR categorization for FLLs among radiologists with different levels of experience.

Evaluation of the Time Difference Method in Identifying Hepatocellular Carcinoma in Current CEUS LR-M Category Nodules

ObjectiveTo explore a potential method for improving the diagnostic detection hepatocellular carcinoma (HCC) based on the contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) version 2017. MethodsWe retrospectively evaluated 585 liver nodules in 427 patients with HCC risk from December 2020 to March 2023. The nodules were categorized as LR-1 to LR-M based on CEUS LI-RADS version 2017 and were randomly subclassified into a developmental cohort (DC) and a validation cohort (VC) at 3:1. In the DC, the cutoff value of the time difference (∆T) for differentiating HCC from other malignancies in LR-M was calculated, which was used to reclassify nodules in the VC. The diagnostic effect on HCC detection before and after reclassification was further assessed. ResultsAccording to the current CEUS LI-RADS, 140 of 426 (32.9%) confirmed HCC nodules were misclassified as LR-M. In the DC (439 nodules), the receiver operating characteristic (ROC) curve showed that the cutoff value of ∆T (washout onset time minus contrast arrival time) recommended for HCC diagnosis was greater than 21 s. In the VC (146 nodules), 34 HCCs were correctly categorized as LR-5 according to the cutoff value and after reclassification LR-5 had higher accuracy (67.1% vs. 89.0%, p < 0.001) and sensitivity (56.0% vs. 87.2%, p < 0.001) for HCC diagnosis with high specificity (100% vs. 94.6%, p = 0.500) remained. ConclusionUsing the time difference method could identify HCC nodules misdiagnosed as LR-M and improve the diagnostic performance for current CEUS LI-RADS.

Assessing Adherence to US LI-RADS Follow-up Recommendations in Vulnerable Patients Undergoing Hepatocellular Carcinoma Surveillance.

Purpose To assess adherence to the US Liver Imaging Reporting and Data System (LI-RADS) recommendations for hepatocellular carcinoma (HCC) surveillance and associated patient-level factors in a vulnerable, diverse patient sample. Materials and Methods The radiology report database was queried retrospectively for patients who underwent US LI-RADS-based surveillance examinations at a single institution between June 1, 2020, and February 28, 2021. Initial US and follow-up liver imaging were included. Sociodemographic and clinical data were captured from electronic medical records. Adherence to radiologist recommendation was defined as imaging (US, CT, or MRI) follow-up in 5-7 months for US-1, imaging follow-up in 3-6 months for US-2, and CT or MRI follow-up in 2 months for US-3. Descriptive analysis and multivariable modeling that adjusted for age, sex, race, and time since COVID-19 pandemic onset were performed. Results Among 936 patients, the mean age was 59.1 years; 531 patients (56.7%) were male and 544 (58.1%) were Asian or Pacific Islander, 91 (9.7%) were Black, 129 (13.8%) were Hispanic, 147 (15.7%) were White, and 25 (2.7%) self-reported as other race. The overall adherence rate was 38.8% (95% CI: 35.7, 41.9). The most common liver disease etiology was hepatitis B (60.6% [657 of 936 patients]); 19.7% of patients (183 of 936) had current or past substance use disorder, and 44.8% (416 of 936) smoked. At adjusted multivariable analysis, older age (odds ratio [OR], 1.20; P = .02), male sex (OR, 1.62; P = .003), hepatology clinic attendance (OR, 3.81; P < .001), and recent prior US examination (OR, 2.44; P < .001) were associated with full adherence, while current smoking (OR, 0.39; P < .001) was negatively associated. Conclusion Adherence to HCC imaging surveillance was suboptimal, despite US LI-RADS implementation. Keywords: Liver, Ultrasound, Screening, Abdomen/GI, Cirrhosis, Metabolic Disorders, Socioeconomic Issues Supplemental material is available for this article. © RSNA, 2024.

Locoregional Therapies for Hepatocellular Carcinoma prior to Liver Transplant: Comparative Pathologic Necrosis, Radiologic Response, and Recurrence

PurposeTo compare pathologic tumor necrosis rates after locoregional therapies (LRTs) for hepatocellular carcinoma (HCC) prior to liver transplantation and evaluate radiologic-pathologic correlation along with posttransplant HCC recurrence. Materials and MethodsConsecutive patients with solitary HCC bridged or downstaged with LRT from 2010 to 2022 were included. LRTs were transarterial chemoembolization (TACE), radioembolization (yttrium-90 [90Y]), ablation, and stereotactic body radiotherapy (SBRT). Upfront combination therapy options were TACE/ablation and TACE/SBRT. Subsequent therapy crossover due to local recurrence was allowed. Posttreatment imaging closest to the time of transplant, explant histopathologic necrosis, and tumor recurrence after transplant were reviewed. ResultsSeventy-three patients met inclusion criteria, of whom 5 (7%) required downstaging. 90Y alone (n = 36) and multimodal therapy (pooled upfront combination and crossover therapy, n = 23) resulted in significantly greater pathologic necrosis compared with TACE alone (n = 14; P = .01). High dose 90Y radiation segmentectomy (≥190 Gy; n = 27) and TACE/ablation (n = 7) showed highest rates of complete pathologic necrosis (CPN)—63% (n = 17) and 71% (n = 5), respectively. Patients with CPN had a mean lesion size of 2.5 cm, compared with 3.2 cm without CPN (P = .04), irrespective of LRT modality. HCC recurrence was more common in patients without CPN (16%, 6/37) than in those with CPN (3%, 1/36; P = .11). Using Liver Imaging Reporting and Data System (LI-RADS), a nonviable imaging response was 75% sensitive and 57% specific for CPN. ConclusionsRadiation segmentectomy and multimodal therapy significantly improved CPN rates compared with TACE alone. A LI-RADS treatment response of nonviable did not confidently predict CPN.

Complementary Role of CEUS and CT/MR LI-RADS for Diagnosis of Recurrent HCC.

We retrospectively compared the diagnostic performance of contrast-enhanced ultrasonography (CEUS) and contrast-enhanced computer tomography-magnetic resonance imaging (CT/MRI) for recurrent hepatocellular carcinoma (HCC) after curative treatment. After curative treatment with 421 ultrasound (US) detected lesions, 303 HCC patients underwent both CEUS and CT/MRI. Each lesion was assigned a Liver Imaging Reporting and Data System (LI-RADS) category according to CEUS and CT/MRI LI-RADS. Receiver-operating characteristic (ROC) curves were computed to determine the optimal diagnosis algorithms for CEUS, CT and MRI. The diagnostic accuracy, sensitivity, specificity, and area under the curve (AUC) were compared between CEUS and CT/MRI. Among the 421 lesions, 218 were diagnosed as recurrent HCC, whereas 203 lesions were diagnosed as benign. In recurrent HCC, CEUS detected more arterial hyperenhancement (APHE) and washout than CT and more APHE than MRI. CEUS yielded better diagnostic performance than CT (AUC: 0.981 vs. 0.958) (p = 0.024) comparable diagnostic performance to MRI (AUC: 0.952 vs. 0.933) (p > 0.05) when using their optimal diagnostic criteria. CEUS missed 12 recurrent HCCs, CT missed one, and MRI missed none. The detection rate of recurrent HCC on CEUS (94.8%, 218/230) was lower than that on CT/MRI (99.6%, 259/260) (p = 0.001). Lesions located on the US blind spots and visualization score C would hinder the ability of CEUS to detect recurrent HCC. CEUS demonstrated excellent diagnostic performance but an inferior detection rate for recurrent HCC. CEUS and CT/MRI played a complementary role in the detection and characterization of recurrent HCC.

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

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

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

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