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

BACKGROUND: Proliferative hepatocellular carcinoma (HCC), aggressive with poor prognosis, and lacks reliable MRI diagnosis. PURPOSE: To develop a diagnostic model for proliferative HCC using liver imaging reporting and data system (LI-RADS) and assess its prognostic value. STUDY TYPE: Retrospective. POPULATION: 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). FIELD STRENGTH/SEQUENCE: 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. ASSESSMENT: 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. STATISTICAL TESTS: 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. RESULTS: 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). DATA CONCLUSION: The MR-based model could pre-treatment identify proliferative HCC and assist in prognosis evaluation. TECHNICAL EFFICACY: Stage 2.

Value of threshold growth as a major diagnostic feature of hepatocellular carcinoma in LI-RADS.

BACKGROUND & AIMS: The Liver Reporting and Data System (LI-RADS) version 2018 simplified the definition of threshold growth to '≥50% size increase in a mass in ≤6 months'. However, the diagnostic value of threshold growth for hepatocellular carcinoma (HCC) remained unclear. We evaluated the value of threshold growth, as defined by LI-RADS v2018, in diagnosing HCCs. METHODS: Patients who underwent preoperative gadoxetate disodium-enhanced MRI because of the presence of LI-RADS category 2, 3, or 4 rather than category 5 on prior CT/MRI between January 2017 and December 2020 were retrospectively evaluated. Pathologic or clinical diagnoses were used as reference standards. Imaging features were evaluated by three readers according to LI-RADS v2018. The frequency and diagnostic odds ratio of threshold growth were calculated. The diagnostic performance of LI-RADS category 5 was separately evaluated when threshold growth was and was not considered a major feature, and results were compared using generalized estimation equations. Subgroups of patients who underwent CT/MRI during the previous 3-6 months were analyzed. RESULTS: Analysis of 340 observations in 243 patients found that the frequency of threshold growth was 18.8% and it gradually increased over time. Threshold growth was significantly associated with HCC (diagnostic odds ratio 5.2; 95% CI 2.1-12.7; p <0.001). Use of threshold growth as a major feature significantly increased sensitivity in both the overall (66.4% vs. 57.3%, p <0.001) and subgroup (73.4% vs. 58.2%, p <0.001) cohorts, but had no effect on specificity in either the overall (97.5% vs. 98.3%, p = 0.319) or subgroup (95.9% vs. 98.0%, p = 0.323) cohorts. CONCLUSION: The revised threshold growth of LI-RADS v2018 was significantly associated with HCC. Use of threshold growth as a major diagnostic feature of HCC can improve the sensitivity of LI-RADS v2018. IMPACT AND IMPLICATIONS: We found that the revised threshold growth in the Liver Imaging Reporting and Data System version 2018 (LI-RADS v2018) was a significant predictor of hepatocellular carcinoma (HCC). The use of threshold growth as a major imaging feature of HCC significantly increased the sensitivity of LI-RADS v2018, especially small HCCs (≤3.0 cm), compared with its non-use. Because these small HCCs are eligible for curative treatments, the additional detection of small HCCs is clinically meaningful.

Steatotic hepatocellular carcinoma: Association of MRI findings to underlying liver disease and clinicopathological characteristics.

BACKGROUND & AIMS: Fatty change is commonly observed in hepatocellular carcinoma (HCC); however, the characteristics of steatotic and steatohepatitic HCCs are not well understood. METHODS: This retrospective study included patients with HCCs who underwent resection between January 2014 and December 2019 to evaluate clinicopathological and magnetic resonance imaging features. Tumours were categorized as magnetic resonance imaging-steatotic, pathology-steatotic and steatohepatitic HCCs and were defined as HCCs with ≥50% steatosis on in-and-oppose phase images, ≥34% tumour cells with lipid droplets and ≥50% tumour areas with steatohepatitic features on light microscopy respectively. RESULTS: Of 465 HCCs, 38 (8%), 23 (5%) and 15 (3%) were diagnosed as magnetic resonance imaging-steatotic, pathology-steatotic and steatohepatitic HCCs respectively. These HCC variants were less likely to be associated with hepatitis B virus infections than with type 2 diabetes mellitus, metabolic syndrome, non-tumour liver steatosis and steatohepatitis. Moreover, microvascular invasion was less likely to be associated with them than either tumour size or differentiation. Type 2 diabetes and non-tumour steatosis were independent risk factors for magnetic resonance imaging-steatotic HCCs. Pathology-steatotic HCCs and steatohepatitic HCCs were significantly associated with magnetic resonance imaging-steatotic HCCs. A targetoid appearance in the transitional or hepatobiliary phase was also more prevalent in steatohepatitic-HCCs than in non-steatohepatitic-HCCs. When magnetic resonance imaging-steatotic HCCs were combined with one or more ancillary features, the sensitivity and specificity were 60% and 97% respectively. CONCLUSION: Underlying fatty liver disease and metabolic syndrome are strongly associated with both steatotic and steatohepatitic HCCs. Clinicoradiological characteristics help identify steatohepatitic HCC with high specificity.

Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study.

OBJECTIVES: To develop a deep learning (DL) method that can determine the Liver Imaging Reporting and Data System (LI-RADS) grading of high-risk liver lesions and distinguish hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT. METHODS: This retrospective study included 1049 patients with 1082 lesions from two independent hospitals that were pathologically confirmed as HCC or non-HCC. All patients underwent a four-phase CT imaging protocol. All lesions were graded (LR 4/5/M) by radiologists and divided into an internal (n = 886) and external cohort (n = 196) based on the examination date. In the internal cohort, Swin-Transformer based on different CT protocols were trained and tested for their ability to LI-RADS grading and distinguish HCC from non-HCC, and then validated in the external cohort. We further developed a combined model with the optimal protocol and clinical information for distinguishing HCC from non-HCC. RESULTS: In the test and external validation cohorts, the three-phase protocol without pre-contrast showed κ values of 0.6094 and 0.4845 for LI-RADS grading, and its accuracy was 0.8371 and 0.8061, while the accuracy of the radiologist was 0.8596 and 0.8622, respectively. The AUCs in distinguishing HCC from non-HCC were 0.865 and 0.715 in the test and external validation cohorts, while those of the combined model were 0.887 and 0.808. CONCLUSION: The Swin-Transformer based on three-phase CT protocol without pre-contrast could feasibly simplify LI-RADS grading and distinguish HCC from non-HCC. Furthermore, the DL model have the potential in accurately distinguishing HCC from non-HCC using imaging and highly characteristic clinical data as inputs. CLINICAL RELEVANCE STATEMENT: The application of deep learning model for multiphase CT has proven to improve the clinical applicability of the Liver Imaging Reporting and Data System and provide support to optimize the management of patients with liver diseases. KEY POINTS: • Deep learning (DL) simplifies LI-RADS grading and helps distinguish hepatocellular carcinoma (HCC) from non-HCC. • The Swin-Transformer based on the three-phase CT protocol without pre-contrast outperformed other CT protocols. • The Swin-Transformer provide help in distinguishing HCC from non-HCC by using CT and characteristic clinical information as inputs.

Differences between CEUS LI-RADS and CECT LI-RADS in the diagnosis of focal liver lesions in patients at risk for HCC.

OBJECTIVES: To compare the inter-modality consistency and diagnostic performances of the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) and contrast-enhanced computed tomography (CECT) LI-RADS in patients at risk for hepatocellular carcinoma (HCC), so as to help clinicians to select a more appropriate modality to follow the focal liver lesions (FLLs). METHODS: This retrospective study included untreated 277 FLLs from 247 patients who underwent both CEUS and CECT within 1 month. The ultrasound contrast medium used was SonoVue. FLL categories were independently assigned by two ultrasound physicians and two radiologists using CEUS LI-RADS v2017 and CECT LI-RADS v2018, respectively. The diagnostic performances of CEUS and CECT LI-RADS were evaluated using sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Cohen's Kappa was employed to evaluate the concordance of the LI-RADS category. RESULTS: The inter-modality consistency for CEUS and CECT LI-RADS was 0.31 (p < 0.001). HCC was more frequently observed in CECT LR-3 and LR-4 hepatic lesions than in CEUS (7.3% vs. 19.5%, p < 0.001). The specificity and PPV of CEUS and CECT LR-5 for the diagnosis of HCC were 89.5%, 95.0%, and 82.5%, 94.4%, respectively. The sensitivity of CEUS LR-5 + LR-M for the diagnosis of hepatic malignancies was higher than that of CECT (93.7% vs. 82.7%, p < 0.001). The specificity and PPV of CEUS LR-M for the diagnosis of non-HCC malignancies were lower than those of CECT (59.7% vs. 95.5%, p < 0.001; 23.4% vs. 70.3%, p < 0.001). CONCLUSIONS: The inter-modality consistency between the CEUS and CECT LI-RADS categories is fair. CEUS LI-RADS was more sensitive than CECT LI-RADS in terms of identifying hepatic malignancies, but weaker in terms of separating HCC from non-HCC malignancies.

Efficacy of Multiple Modified Methods of Criteria for LR-M Liver Nodules of Different Sizes: Clinical Practice and Discussion in CEUS LI-RADS Version 2017.

OBJECTIVES: To assess the diagnostic performance of the modified LR-M method of CEUS LI-RADS version 2017 with nodules of different sizes. METHODS: This retrospective study included consecutive patients with high risk for HCC who underwent CEUS between 2019 and 2021, demonstrating an LR-M observed using CEUS LI-RADS version 2017. Four modified LR-M methods were used to evaluate nodules of different sizes. The diagnostic performances of the four modified LR-M methods were assessed in LR-M nodules of different sizes by the area under the receiver operating characteristic curve (AUC). RESULTS: The 261 patients with LR-M observations included 166 HCCs and 95 non-HCCs. A total of 133 nodules were <30 mm and defined as group A, 78 nodules were 30-50 mm in size and defined as group B, and 50 nodules were >50 mm and defined as group C. The AUCs between criterion I, II, III, and IV were not significantly different in all LR-M nodules. The AUCs of the ROC curves between criterion I, II, III, and IV were not significantly different in group A. However, the AUC of criterion IV was significantly higher than that of criterion I and III in group B, and the AUCs of criterion I and criterion III were both not significant in group B; the AUC of criterion IV was not significant in group C. CONCLUSIONS: The modified LR-M method could moderate the detection effectiveness in differentiating HCC from other lesions. According to tumor size, the selection of appropriate modified LR-M diagnostic criteria could effectively improve the diagnostic performance of LR-M.

Nodules Identified on Surveillance Ultrasound for HCC: CEUS or MRI as the Initial Test?

OBJECTIVES: Following positive surveillance ultrasound (US), magnetic resonance imaging (MRI) is recommended for further characterization. We propose contrast-enhanced ultrasound (CEUS) shows equivalent efficacy. METHODS: This prospective institutional review board approved study recruited 195 consecutive at-risk patients with a positive surveillance US. All had CEUS and MRI. Biopsy (n = 44) and follow-up are gold standard. MRI and CEUS results are classified according to liver imaging reporting and data system (LI-RADS) and patient outcome. RESULTS: As an US-based modality, CEUS is superior in confirming findings from surveillance US, correlation in 189/195 (97%) on CEUS compared to 153/195 (79%) on MRI. Within these negative MRI examinations, there are 2 hepatocellular carcinoma (HCC) and 1 cholangiocarcinoma (iCCA) diagnosed on CEUS and proven by biopsy. From 195 patients, there are 71 malignant diagnoses from all sources, including 58 LR-5 (45 on MRI and 54 on CEUS) and 13 others, including HCC outside of LR-5 category, and LR-M with biopsy proven iCCA (3 on MRI and 6 on CEUS). CEUS and MRI show concordant results in the majority of patients (146/195, 75%), including 57/146 malignant and 89/146 benign diagnoses. There are 41/57 concordant LR-5 and 6/57 concordant LR-M. When CEUS and MRI are discordant, CEUS upgraded 20 (10 biopsy-proven) from MRI LR-3/4 to CEUS LR-5 or LR-M by showing washout (WO) that MRI failed to show. Additionally, CEUS characterized time and intensity of WO and diagnosed 13/20 LR-5 by showing late and weak WO and 7 LR-M by showing fast and marked WO. CEUS is 81% sensitive and 92% specific in diagnosing malignancy. MRI is 64% sensitive and 93% specific. CONCLUSIONS: CEUS performance is at least equivalent if not superior to MRI for initial evaluation of lesions from surveillance US.

[Performance of Contrast-enhanced Ultrasound Liver Imaging Reporting and Data System LR-5 in the Diagnosis of Hepatocellular Carcinoma:A Meta-analysis].

Objective To evaluate the performance of contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) LR-5 in the diagnosis of hepatocellular carcinoma (HCC). Methods The clinical research reports with the application of CEUS LI-RADS in the diagnosis of HCC were collected from PubMed,Embase,Cochrane Library,CNKI,and Wanfang Data from inception to November 14,2021.Two researchers respectively screened the literature and extracted relevant information.The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) was used to evaluate the quality of all the included articles.RevMan 5.4,Meta disc 1.4,and Stata 16.0 were employed to analyze the diagnostic performance of LR-5 for HCC in high-risk patients. Results Twenty original studies were included,involving a total of 6131 lesions,of which 5142 were HCC.The results of meta-analysis showed that the LR-5 in CEUS LI-RADS for diagnosing HCC in the high-risk population had the overall sensitivity of 0.72 (95%CI=0.66-0.77),the overall specificity of 0.93 (95%CI=0.87-0.96),the overall positive likelihood ratio of 9.89 (95%CI=5.31-18.41),the overall negative likelihood ratio of 0.30 (95%CI=0.25-0.37),and the area under the summary receiver operating characteristic curve of 0.88 (95%CI=0.85-0.91).There was heterogeneity among the included studies (I2=95.31,P<0.001).The funnel plot indicated the existence of publication bias (P=0.04). Conclusion The CEUS LI-RADS can effectively diagnose HCC in high-risk patients based on the LR-5 criteria.

Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity.

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is widely accepted as a reliable diagnostic scheme for hepatocellular carcinoma (HCC) in at-risk patients. However, its application is hampered by substantial complexity and suboptimal diagnostic sensitivity. PURPOSE: To propose data-driven modifications to the LI-RADS version 2018 (v2018) major feature system (rLI-RADS) on gadoxetate disodium (EOB)-enhanced magnetic resonance imaging (MRI) to improve sensitivity and simplicity while maintaining high positive predictive value (PPV) for detecting HCC. STUDY TYPE: Retrospective. POPULATION: Two hundred and twenty-four consecutive at-risk patients (training dataset: 169, independent testing dataset: 55) with 742 LR-3 to LR-5 liver observations (HCC: N = 498 [67%]) were analyzed from a prospective observational registry collected between July 2015 and September 2018. FIELD STRENGTH/SEQUENCE: 3.0 T/T2-weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar and three-dimensional (3D) T1-weighted gradient echo sequences. ASSESSMENT: All images were evaluated by three independent abdominal radiologists who were blinded to all clinical, pathological, and follow-up information. Composite reference standards of either histopathology or imaging follow-up were used. STATISTICAL TESTS: In the training dataset, LI-RADS v2018 major features were used to develop rLI-RADS based on their associated PPV for HCC. In an independent testing set, diagnostic performances of LI-RADS v2018 and rLI-RADS were computed using a generalized estimating equation model and compared with McNemar's test. A P value <0.05 was considered statistically significant. RESULTS: The median (interquartile range) size of liver observations was 13 mm (7-27 mm). The diagnostic table for rLI-RADS encompassed 9 cells, as opposed to 16 cells for LI-RADS v2018. In the testing set, compared to LI-RADS v2018, rLI-RADS category 5 demonstrated a significantly superior sensitivity (76% vs. 61%) while maintaining comparably high PPV (92.5% vs. 94.1%, P = 0.126). DATA CONCLUSION: Compared with LI-RADS v2018, rLI-RADS demonstrated improved simplicity and significantly superior diagnostic sensitivity for HCC in at-risk patients. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study.

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is widely used for diagnosing hepatocellular carcinoma (HCC), however, with unsatisfactory sensitivity, complex ancillary features, and inadequate integration with gadoxetate disodium (EOB)-enhanced MRI. PURPOSE: To modify LI-RADS (mLI-RADS) on EOB-MRI. STUDY TYPE: Secondary analysis of a prospective observational study. POPULATION: Between July 2015 and September 2018, 224 consecutive high-risk patients (median age, 51 years; range, 26-83; 180 men; training/testing sets: 169/55 patients) with 742 (median size, 13 mm; interquartile range, 7-27; 498 HCCs) LR-3/4/5 observations. FIELD STRENGTH/SEQUENCE: 3.0 T T2 -weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar, and 3D T1 -weighted gradient echo sequences. ASSESSMENT: Three radiologists (with 5, 5, and 10 years of experience in liver MR imaging, respectively) blinded to the reference standard (histopathology or imaging follow-up) reviewed all MR images independently. In the training set, the optimal LI-RADS version 2018 (v2018) features selected by Random Forest analysis were used to develop mLI-RADS via decision tree analysis. STATISTICAL TESTS: In an independent testing set, diagnostic performances of mLI-RADS, LI-RADS v2018, and the Korean Liver Cancer Association (KLCA) guidelines were computed using a generalized estimating equation model and compared with McNemar's test. A two-tailed P < 0.05 was statistically significant. RESULTS: Five features (nonperipheral "washout," restricted diffusion, nonrim arterial phase hyperenhancement [APHE], mild-moderate T2 hyperintensity, and transitional phase hypointensity) constituted mLI-RADS, and mLR-5 was nonperipheral washout coupled with either nonrim APHE or restricted diffusion. In the testing set, mLI-RADS was significantly more sensitive (72%) and accurate (80%) than LI-RADS v2018 (sensitivity, 61%; accuracy 74%; both P < 0.001) and the KLCA guidelines (sensitivity, 64%; accuracy 74%; both P < 0.001), without sacrificing positive predictive value (mLI-RADS, 94%; LI-RADS v2018, 94%; KLCA guidelines, 92%). DATA CONCLUSION: In high-risk patients, the EOB-MRI-based mLI-RADS was simpler and more sensitive for HCC than LI-RADS v2018 while maintaining high positive predictive value. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Prognostic implications of CT/MRI LI-RADS in hepatocellular carcinoma: State of the art and future directions.

Hepatocellular carcinoma (HCC) is the fourth most lethal malignancy with an increasing incidence worldwide. Management of HCC has followed several clinical staging systems that rely on tumour morphologic characteristics and clinical variables. However, these algorithms are unlikely to profile the full landscape of tumour aggressiveness and allow accurate prognosis stratification. Noninvasive imaging biomarkers on computed tomography (CT) or magnetic resonance imaging (MRI) exhibit a promising prospect to refine the prognostication of HCC. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, techniques, interpretation, reporting and data collection of liver imaging. At present, it has been widely accepted as an effective diagnostic system for HCC in at-risk patients. Emerging data have provided new insights into the potential of CT/MRI LI-RADS in HCC prognostication, which may help refine the prognostic paradigm of HCC that promises to direct individualized management and improve patient outcomes. Therefore, this review aims to summarize several prognostic imaging features at CT/MRI for patients with HCC; the available evidence regarding the use of LI-RDAS for evaluation of tumour biology and clinical outcomes, pitfalls of current literature, and future directions for LI-RADS in the management of HCC.

What proportion of LI-RADS 5 observations reported in clinical practice do not meet LI-RADS 5 criteria?

OBJECTIVES: Liver Imaging Reporting and Data System (LI-RADS, LR) category 5 (definite hepatocellular carcinoma [HCC]) is assigned based on combinations of major imaging features (MFs): size, arterial-phase hyperenhancement (APHE), washout (WO), enhancing capsule, and threshold growth. The criteria were simplified in v2018 compared to v2017. The goal of this study is to assess the proportion of LR-5 observations reported in clinical practice with LI-RADS v2017 or v2018 that did not meet LR-5 criteria based on reported MFs. METHODS: All MR and CT reports using a standardized LI-RADS template between April 2017 and September 2020 were identified retrospectively. For each reported LR-5 observation, size, MFs, and LI-RADS version (v2017 or v2018) were extracted. Reported MFs were used to determine whether LR-5 criteria were met using the applied version of LI-RADS. The data was summarized descriptively. RESULTS: Three hundred eight observations in 234 patients (67.6% male, mean age 66.2 years) were reported as LR-5, including 136 (44.2%) with v2017 and 172 (55.8%) with v2018. 8/136 (6%) v2017 LR-5 observations and 6/172 (3%) v2018 LR-5 observations did not meet LR-5 criteria. Of 8 incorrectly categorized v2017 observations, 3 (43%) lacked APHE, 1 (14%) was a 16-mm new observation with APHE only, and 4 (43%) were 10-19 mm with APHE and WO. Of the 6 incorrectly categorized v2018 observations, 5 (83%) lacked APHE and 1 (17%) was < 10 mm. CONCLUSIONS: Depending on the LI-RADS version, 3-6% of LR-5 observations reported in clinical practice do not meet LR-5 criteria based on reported MFs. Key Points • Depending on the LI-RADS version, 3-6% of LR-5 observations in clinical practice do not meet LR-5 criteria based on reported major imaging features. • Assigning LR-5 category to observations without nonrim arterial-phase hyperenhancement was the most common error.

A modified LI-RADS: targetoid tumors with enhancing capsule can be diagnosed as HCC instead of LR-M lesions.

OBJECTIVE: To elucidate whether the presence of enhancing capsule can be applied to establish a modified Liver Imaging Reporting and Data System (LI-RADS) to differentiate hepatocellular carcinoma (HCC) from non-HCC malignancies in extracellular contrast agent (ECA)-enhanced and hepatobiliary agent (HBA)-enhanced MRI. METHODS: We enrolled 198 participants (161 men; mean age, 56.3 years) with chronic liver disease who underwent ECA-MRI and HBA-MRI before surgery for de novo hepatic nodule(s). Two reviewers assigned LI-RADS categories (v2018). We defined a "modified LR-5 category, which emphasizes enhancing capsule (mLR-5C)" over targetoid features and classifies tumors with both targetoid appearance and enhancing capsule as HCC instead of LR-M. We compared the diagnostic performance of conventional LI-RADS and modified LI-RADS criteria for both MRIs. RESULTS: A total of 258 hepatic nodules (194 HCCs, 43 benign lesions, and 21 non-HCC malignancies; median size, 19 mm) were analyzed. By conventional LI-RADS, 47 (18.2%) nodules (31 HCCs and 16 non-HCC malignancies) were categorized as LR-M. The mLR-5C criterion showed superior sensitivity (ECA-MRI, 76.6% vs. 67.0%; HBA-MRI, 60.4% vs. 56.3%; both p < 0.05) while maintaining high specificity (ECA-MRI, 93.8% vs. 98.4%; HBA-MRI, 95.3% vs. 98.4%; both p > 0.05) compared with the LR-5 criterion. Using the mLR-5C criterion, ECA-MRI exhibited higher sensitivity than HBA-MRI (76.6% vs. 60.4%, p < 0.001) and similar specificity (93.8% vs. 95.3%, p > 0.99). CONCLUSION: Our modified LI-RADS achieved superior sensitivity for diagnosing HCC, without compromising specificity compared with LR-5. ECA-MRI showed higher sensitivity in diagnosing HCC than HBA-MRI by applying the mLR-5C for LR-M lesions. KEY POINTS: • By conventional LI-RADS, 31 (16.0%) of 194 HCCs were categorized as LR-M. • Among 31 HCCs categorized as LR-M, 19 HCCs or 8 HCCs were recategorized as HCC on ECA-MRI or HBA-MRI, respectively, after applying the modified LR-5 category, which allocates targetoid lesions with enhancing capsule as mLR-5C instead of LR-M. • The mLR-5C showed superior sensitivity compared with the LR-5 in both MRIs (ECA-MRI, 76.6% vs. 67.0%; HBA-MRI, 60.4% vs. 56.3%, both p < 0.05), while maintaining high specificity (ECA-MRI, 93.8% vs. 98.4%; HBA-MRI, 95.3% vs. 98.4%; both p > 0.05).

Differentiation between combined hepatocellular carcinoma and hepatocellular carcinoma: comparison of diagnostic performance between ultrasomics-based model and CEUS LI-RADS v2017.

BACKGROUND: The imaging findings of combined hepatocellular cholangiocarcinoma (CHC) may be similar to those of hepatocellular carcinoma (HCC). CEUS LI-RADS may not perform well in distinguishing CHC from HCC. Studies have shown that radiomics has an excellent imaging analysis ability. This study aimed to establish and confirm an ultrasomics model for differentiating CHC from HCC. METHODS: Between 2004 and 2016, we retrospectively identified 53 eligible CHC patients and randomly included 106 eligible HCC patients with a ratio of HCC:CHC = 2:1, all of whom were categorized according to Contrast-Enhanced (CE) ultrasonography (US) Liver Imaging Reporting and Data System (LI-RADS) version 2017. The model based on ultrasomics features of CE US was developed in 74 HCC and 37 CHC and confirmed in 32 HCC and 16 CHC. The diagnostic performance of the LI-RADS or ultrasomics model was assessed by the area under the curve (AUC), accuracy, sensitivity and specificity. RESULTS: In the entire and validation cohorts, 67.0% and 81.3% of HCC cases were correctly assigned to LR-5 or LR-TIV contiguous with LR-5, and 73.6% and 87.5% of CHC cases were assigned to LR-M correctly. Up to 33.0% of HCC and 26.4% of CHC were misclassified by CE US LI-RADS. A total of 90.6% of HCC as well as 87.5% of CHC correctly diagnosed by the ultrasomics model in the validation cohort. The AUC, accuracy, sensitivity of the ultrasomics model were higher though without significant difference than those of CE US LI-RADS in the validation cohort. CONCLUSION: The proposed ultrasomics model showed higher ability though the difference was not significantly different for differentiating CHC from HCC, which may be helpful in clinical diagnosis.

[Small hepatocellular carcinoma : Diagnostics according to guidelines and established in the clinical setting]. / Das kleine hepatozelluläre Karzinom : Leitliniengerechte und klinisch etablierte Diagnostik.

CLINICAL/METHODICAL ISSUE: The diagnosis of hepatocellular carcinoma (HCC)-especially the characterization of small lesions <2 cm-continues to be a radiological challenge. STANDARD RADIOLOGICAL METHODS: In the current S3 guideline on diagnosis and therapy of HCC, contrast-enhanced imaging examinations, such as contrast-enhanced ultrasonography (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI), are still the diagnostic standard. METHODOLOGICAL INNOVATIONS: HCC in the cirrhotic liver should be diagnosed by its typical contrast-enhanced pattern in the MRI. In addition, the use of quality assurance instruments such as LI-RADS (Liver Imaging Reporting and Data System) contributes to the desired consistency of findings, even with small ambiguous findings. PERFORMANCE: Many studies have shown that the LI-RADS classification reflects the likelihood of HCC and other malignant liver lesions. ACHIEVEMENTS: Guidelines and quality assurance instruments contribute to a more precise diagnosis in patients with suspected HCC. PRACTICAL RECOMMENDATIONS: A guideline-compliant diagnostic algorithm and the LI-RADS should be used across the board for accurate HCC diagnostics.

Preoperative estimation of the survival of patients with unresectable hepatocellular carcinoma achieving complete response after conventional transcatheter arterial chemoembolization: assessments of clinical and LI-RADS MR features.

PURPOSE: To identify the associations of clinical and magnetic resonance (MR) features with overall survival (OS) in patients with unresectable hepatocellular carcinoma (HCC) achieving complete response (CR) after conventional transcatheter arterial chemoembolization (TACE) and to further develop an individual nomograph to estimate the survival probability. MATERIALS AND METHODS: A total of 112 patients with unresectable HCC treated with TACE as first-line treatment were retrospectively evaluated. Potential risk factors associated with OS were identified by univariate and multivariate Cox analyses. The survival model was developed by multivariate Cox proportional hazard model. The area under the receiver operating characteristic curve was calculated to assess the performance of each marker and of the whole model. Discrimination was performed using Kaplan-Meier curves, and the survival curves were compared by the log-rank test. A nomogram derived from the survival model was established. RESULTS: Multivariate Cox analyses indicated that nonsmooth tumor margin, peritumoral enhancement, fat sparing in solid mass, and Barcelona clinic liver cancer (BCLC) stage were independent risk indicators associated with OS. The survival model showed acceptable diagnostic power, with an area under the curve (AUC) of 0.687. Kaplan-Meier curves demonstrated that the model discriminated well, as the high-risk and low-risk groups had median survival times of 21.6 months and 34.8 months, respectively (log-rank test, P = 0.01). CONCLUSIONS: Nonsmooth tumor margin, peritumoral enhancement, fat sparing in solid mass, and BCLC stage were potential biomarkers to evaluate the survival with favorable performance and discriminate HCC patients with CR under conventional TACE treatment.

Using new criteria to improve the differentiation between HCC and non-HCC malignancies: clinical practice and discussion in CEUS LI-RADS 2017.

PURPOSE: Using contrast-enhanced ultrasound (CEUS) to evaluate the diagnostic performance of liver imaging reporting and data system (LI-RADS) version 2017 and to explore potential ways to improve the efficacy. METHODS: A total of 315 nodules were classified as LR-1 to LR-5, LR-M, and LR-TIV. New criteria were applied by adjusting the early washout onset (< 45 s) and the time of marked washout (within 3 min). Two subgroups of the LR-M nodules were recategorized as LR-5, respectively. The diagnostic performance was evaluated by calculating the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: By adjusting early washout onset to < 45 s, the LR-5 as a standard for diagnosing HCC had an improved sensitivity (74.1% vs. 56.1%, P < 0.001) without significant change in PPV (93.3% vs. 96.1%, P = 0.267), but the specificity was decreased (48.3% vs. 78.5%, P = 0.018). The LR-M as a standard for the diagnosis of non-HCC malignancies had an increase in specificity (89.2% vs. 66.2%, P < 0.001) but a decrease in sensitivity (31.5% vs. 68.4%, P = 0.023). After reclassification according to the time of marked washout, the sensitivity of the LR-5 increased (80% vs. 56.1%, P < 0.001) without a change in PPV (94.9% vs. 96.1%, P = 0.626) and specificity (80% vs. 78.5%, P = 0.879). For reclassified LR-M nodules, the specificity increased (87.5% versus 66.2%, P < 0.001) with a non-significant decrease in sensitivity (47.3% vs. 68.4%, P = 0.189). CONCLUSIONS: The CEUS LI-RADS showed good confidence in diagnosing HCC while tended to misdiagnose HCC as non-HCC malignancies. Adjusting the marked washout time within 3 min would reduce the possibility of this misdiagnosis.

Hepatic Steatosis Has No Effect in Diagnosis Accuracy of LI-RADS v2018 Categorization of Hepatocellular Carcinoma in MR Imaging.

BACKGROUND: In clinical practice, hepatocellular carcinoma (HCC) is widely diagnosed by using MRI, however, whether the imaging features are affected by hepatic steatosis (HS) is still unknown. PURPOSE: To investigate and compare the differences in HCC related imaging features between with- and without-HS groups, and to further determine whether HS affects the diagnosis accuracy of Liver Imaging Reporting and Data System (LI-RADS) v2018 of HCC in MRI. STUDY TYPE: Prospective. SUBJECTS: One hundred and seventy-one patients (mean age, 52 ± 11 years; range, 26-83 years) including 137 men and 34 women. FIELD STRENGTH/SEQUENCE: 3.0 T, gradient echo (GRE). ASSESSMENT: Subjects were classified as HS and non-HS groups according to MRI-proton density fat-fraction (PDFF). HS was defined as MRI-PDFF >5.6%. Three radiologists accessed HCC features and assigned LI-RADS categories in MRI independently based on LI-RADS v2018. Frequencies of HCC major features and LR categorization assignment between the two groups as well as interobserver agreement between the two radiologists were assessed. STATISTICAL TESTS: Unpaired t-test, Chi-square test, Fisher's exact test, kappa statistic, intraclass correlation coefficient (ICC). A two-sided P value <0.05 was considered as statistically significant. RESULTS: Major features including arterial hyperenhancement (APHE), enhancing "capsule" and nonperipheral "washout" observed between HS and non-HS groups were not significantly different (78.95% vs.78.62%, P = 0.866; 57.89% vs.52.98%, P = 0.483; and 75% vs.81.46%, P = 0.257, respectively), and the assessment of observation size showed a borderline difference (P = 0.059). No significant difference in LR-5 assignment between the two groups (69.74% vs. 72.85% for reader 1, P = 0.641; 71.05% vs. 72.19% for reader 2, P = 0.877). Interobserver agreement between the two radiologists showed almost perfect in LR-5 assignment (κ = 0.869) and size observation (ICC = 0.997). DATA CONCLUSION: The diagnosis of HCC based on LI-RADS v2018 in MRI is of comparable performance regardless of HS, in which there is no significant difference in either the major imaging features or LR categorization. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2.

Interreader Agreement of Liver Imaging Reporting and Data System on MRI: A Systematic Review and Meta-Analysis.

BACKGROUND: Use of the Liver Imaging Reporting and Data System (LI-RADS) is increasing, but the reported results for interreader agreement seem quite variable. PURPOSE: To systematically determine the interreader agreement of LI-RADS on magnetic resonance imaging (MRI) and to determine the sources of heterogeneity between the reported results. STUDY TYPE: Systematic review and meta-analysis. SUBJECTS: Fifteen original articles with 2968 lesions. FIELD STRENGTH: 1.5T and 3.0T. ASSESSMENT: Two reviewers independently performed the data extraction. The reviewers identified and reviewed the original articles reporting the interreader agreement of LI-RADS using MRI. STATISTICAL TESTS: The meta-analytic pooled intraclass correlation coefficient (ICC) for lesion size and kappa value (κ) for major features (arterial-phase hyperenhancement [APHE], nonperipheral washout [WO], enhancing capsule [EC]) and LI-RADS categorization (LR) were calculated using the random-effects model. Sensitivity analysis and meta-regression analysis were performed to explore the cause of study heterogeneity. RESULTS: The meta-analytic pooled ICC of lesion size was 0.97 (95% confidence interval [CI], 0.94-1.00). Meta-analytic pooled κ of APHE, WO, EC, and LR were 0.72 (95% CI, 0.62-0.82), 0.69 (95% CI, 0.60-0.78), 0.66 (95% CI, 0.58-0.74), and 0.70 (95% CI, 0.56-0.85), respectively. Substantial study heterogeneity was noted in all five variables (I2 ≥ 89.1%, P < 0.001). Study design, type, and clarity of blinding review were factors that significantly influenced study heterogeneity (P ≤ 0.05). DATA CONCLUSION: LI-RADS demonstrated overall substantial interreader agreement for major features and the category on MRI, but showed heterogeneous results between studies. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:795-804.

Liver imaging reporting and data system category M: A systematic review and meta-analysis.

BACKGROUND AND AIMS: The Liver Imaging Reporting and Data System (LI-RADS) category M (LR-M) was introduced to preserve the high specificity of LI-RADS algorithm for diagnosing hepatocellular carcinoma (HCC). We aimed to systematically determine the probability of the LR-M for HCC and non-HCC malignancy, and to determine the sources of heterogeneity between reported results. METHODS: Original studies reporting the probability of LR-M for HCC and non-HCC malignancy on magnetic resonance imaging (MRI) were identified in MEDLINE and EMBASE. The meta-analytic pooled percentages of HCC and non-HCC in LR-M were calculated. Meta-regression analysis was performed to explore study heterogeneity. The meta-analytic frequency of each LR-M imaging feature was determined. RESULTS: We found 10 studies reporting the diagnostic performance of LR-M (1819 lesions in 1631 patients), and six reporting the frequency of LR-M imaging features. The pooled percentages of HCC and non-HCC malignancy for LR-M were 28.2% (95% confidence interval [CI], 23.8%-33.1%; I2  = 83%) and 69.6% (95% CI, 64.6%-74.1%; I2  = 83%) respectively. The study type and MRI scanner field strength were significantly associated with study heterogeneity (P ≤ .04). Of the seven imaging features, rim arterial phase hyperenhancement showed the highest frequency in both non-HCC (48.9%; 95% CI, 43.0%-54.8%) and HCC groups (9.8%; 95% CI, 6.9%-13.6%). CONCLUSIONS: The LR-M category most commonly included non-HCC malignancy but also included 28.2% of HCC. Substantial study heterogeneity was noted, and it was significantly associated with study type and MRI scanner field strength. In addition, the frequency of LR-M imaging features was variable.