Intravital imaging: dynamic insights into liver immunity in health and disease.

Inflammation is a critical component of most acute and chronic liver diseases. The liver is a unique immunological organ with a dense vascular network, leading to intense crosstalk between tissue-resident immune cells, passenger leucocytes and parenchymal cells. During acute and chronic liver diseases, the multifaceted immune response is involved in disease promoting and repair mechanisms, while upholding core liver immune functions. In recent years, single-cell technologies have unravelled a previously unknown heterogeneity of immune cells, reshaping the complexity of the hepatic immune response. However, inflammation is a dynamic biological process, encompassing various immune cells, orchestrated in temporal and spatial dimensions, and driven by multiorgan signals. Intravital microscopy (IVM) has emerged as a powerful tool to investigate immunity by visualising the dynamic interplay between different immune cells and their surroundings within a near-natural environment. In this review, we summarise the experimental considerations to perform IVM and highlight recent technological developments. Furthermore, we outline the unique contributions of IVM to our understanding of liver immunity. Through the lens of liver disease, we discuss novel immune-mediated disease mechanisms uncovered by imaging-based studies.

Confidence maps for reliable estimation of proton density fat fraction and R 2 * in the liver.

PURPOSE: The objective was to develop a fully automated algorithm that generates confidence maps to identify regions valid for analysis of quantitative proton density fat fraction (PDFF) and R 2 * $$ {R}_2^{\ast } $$ maps of the liver, generated with chemical shift-encoded MRI (CSE-MRI). Confidence maps are urgently needed for automated quality assurance, particularly with the emergence of automated segmentation and analysis algorithms. METHODS: Confidence maps for both PDFF and R 2 * $$ {R}_2^{\ast } $$ maps are generated based on goodness of fit, measured by normalized RMS error between measured complex signals and the CSE-MRI signal model. Based on Cramér-Rao lower bound and Monte-Carlo simulations, normalized RMS error threshold criteria were developed to identify unreliable regions in quantitative maps. Simulation, phantom, and in vivo clinical studies were included. To analyze the clinical data, a board-certified radiologist delineated regions of interest (ROIs) in each of the nine liver segments for PDFF and R 2 * $$ {R}_2^{\ast } $$ analysis in consecutive clinical CSE-MRI data sets. The percent area of ROIs in areas deemed unreliable by confidence maps was calculated to assess the impact of confidence maps on real-world clinical PDFF and R 2 * $$ {R}_2^{\ast } $$ measurements. RESULTS: Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF and R 2 * $$ {R}_2^{\ast } $$ measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF and R 2 * $$ {R}_2^{\ast } $$ maps, as identified by confidence maps. CONCLUSION: A proposed confidence map algorithm that identifies reliable areas of PDFF and R 2 * $$ {R}_2^{\ast } $$ measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility.

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.

MRI imaging features for predicting macrotrabecular-massive subtype hepatocellular carcinoma: a systematic review and meta-analysis.

PURPOSE: To identify significant MRI features associated with macrotrabecular-massive hepatocellular carcinoma (MTM-HCC), and to assess the distribution of Liver Imaging Radiology and Data System (LI-RADS, LR) category assignments. METHODS: PubMed and EMBASE were searched up to March 28, 2023. Random-effects model was constructed to calculate pooled diagnostic odds ratios (DORs) and 95% confidence intervals (CIs) for each MRI feature for differentiating MTM-HCC from NMTM-HCC. The pooled proportions of LI-RADS category assignments in MTM-HCC and NMTM-HCC were compared using z-test. RESULTS: Ten studies included 1978 patients with 2031 HCCs (426 (20.9%) MTM-HCC and 1605 (79.1%) NMTM-HCC). Six MRI features showed significant association with MTM-HCC: tumor in vein (TIV) (DOR = 2.4 [95% CI, 1.6-3.5]), rim arterial phase hyperenhancement (DOR =2.6 [95% CI, 1.4-5.0]), corona enhancement (DOR = 2.6 [95% CI, 1.4-4.5]), intratumoral arteries (DOR = 2.6 [95% CI, 1.1-6.3]), peritumoral hypointensity on hepatobiliary phase (DOR = 2.2 [95% CI, 1.5-3.3]), and necrosis (DOR = 4.2 [95% CI, 2.0-8.5]). The pooled proportions of LI-RADS categories in MTM-HCC were LR-3, 0% [95% CI, 0-2%]; LR-4, 11% [95% CI, 6-16%]; LR-5, 63% [95% CI, 55-71%]; LR-M, 12% [95% CI, 6-19%]; and LR-TIV, 13% [95% CI, 6-22%]. In NMTM-HCC, the pooled proportions of LI-RADS categories were LR-3, 1% [95% CI, 0-2%]; LR-4, 8% [95% CI, 3-15%]; LR-5, 77% [95% CI, 71-82%]; LR-M, 5% [95% CI, 3-7%]; and LR-TIV, 6% [95% CI, 2-11%]. MTM-HCC had significantly lower proportion of LR-5 and higher proportion of LR-M and LR-TIV categories. CONCLUSIONS: Six MRI features showed significant association with MTM-HCC. Additionally, compared to NMTM-HCC, MTM-HCC are more likely to be categorized LR-M and LR-TIV and less likely to be categorized LR-5. CLINICAL RELEVANCE STATEMENT: Several MR imaging features can suggest macrotrabecular-massive hepatocellular carcinoma subtype, which can assist in guiding treatment plans and identifying potential candidates for clinical trials of new treatment strategies. KEY POINTS: • Macrotrabecular-massive hepatocellular carcinoma is a subtype of HCC characterized by its aggressive nature and unfavorable prognosis. • Tumor in vein, rim arterial phase hyperenhancement, corona enhancement, intratumoral arteries, peritumoral hypointensity on hepatobiliary phase, and necrosis on MRI are indicative of macrotrabecular-massive hepatocellular carcinoma. • Various MRI characteristics can be utilized for the diagnosis of the macrotrabecular-massive hepatocellular carcinoma subtype. This can prove beneficial in guiding treatment decisions and identifying potential candidates for clinical trials involving novel treatment approaches.

Clinical impact of MRI on indeterminate findings on contrast-enhanced CT suspicious of HCC.

OBJECTIVES: In patients evaluated for hepatocellular carcinoma (HCC), magnetic resonance imaging (MRI) is often used secondarily when multiphase contrast-enhanced computed tomography (ceCT) is inconclusive. We investigated the clinical impact of adding MRI. MATERIALS AND METHODS: This single-institution retrospective study included 48 MRI scans (44 patients) conducted from May 2016 to July 2023 due to suspicion of HCC on a multiphase ceCT scan. Data included medical history, preceding and subsequent imaging, histology when available, and decisions made at multidisciplinary team meetings. RESULTS: In case of possible HCC recurrence, 63% of the MRI scans were diagnostic of HCC. For 80% of the negative MRI scans, the patients were diagnosed with HCC within a median of 165 days in the suspicious area of the liver. In case of possible de-novo HCC in patients with cirrhosis, 22% of the scans were diagnostic of HCC and 33% of the negative MRI scans were of patients diagnosed with HCC within a median of 109 days. None of the non-cirrhotic patients with possible de-novo HCC and negative MRI scans (64%) were later diagnosed with HCC, but 3/5 of the indeterminate scans were of patients diagnosed with HCC in a biopsy. CONCLUSIONS: Secondary MRI to a multiphase ceCT scan suspicious of HCC is highly valuable in ruling out HCC in non-cirrhotic patients and in diagnosing HCC non-invasively in cirrhotic patients and patients with prior HCC. Patients with cirrhosis or prior HCC are still at high risk of having HCC if MRI results are inconclusive or negative.

Diagnostic accuracy of FibroScan-AST (FAST) score for the non-invasive identification of patients with fibrotic non-alcoholic steatohepatitis: a systematic review and meta-analysis.

OBJECTIVE: A simple combined score with liver stiffness, controlled attenuation parameter and serum aspartate aminotransferase (AST), the FibroScan-AST (FAST) score, has been proposed to non-invasively identify patients with fibrotic non-alcoholic steatohepatitis (NASH). We performed a systematic review and meta-analysis of published studies to evaluate the overall diagnostic accuracy of the FAST score in identifying patients with fibrotic NASH. DESIGN: We systematically searched MEDLINE, Ovid Embase, Scopus and Cochrane Library electronic databases for full-text published articles in any language between 3 February 2020 and 30 April 2022. We included original articles that reported data for the calculation of sensitivity and specificity of the FAST score for identifying adult patients with fibrotic NASH adults, according to previously described rule-out (≤0.35) and rule-in (≥0.67) cut-offs. RESULTS: We included 12 observational studies for a total of 5835 participants with biopsy-confirmed non-alcoholic fatty liver disease. The pooled prevalence of fibrotic NASH was 28% (95% CI 21% to 34%). The FAST score's pooled sensitivity was 89% (95% CI 82% to 93%), and the pooled specificity was 89% (95% CI 83% to 94%) according to the aforementioned rule-in/rule-out cut-offs. The negative predictive value and positive predictive value of the FAST score were 92% (95% CI 91% to 95%) and 65% (95% CI 53% to 68%), respectively. Subgroup analyses and influential bias analyses did not alter these findings. CONCLUSION: The results of our meta-analysis show that the FAST score has a good performance for non-invasive diagnosis of fibrotic NASH. Therefore, this score can be used to efficiently identify patients who should be referred for a conclusive liver biopsy and/or consideration for treatment with emerging pharmacotherapies. PROSPERO REGISTRATION NUMBER: CRD42022350945.

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.

Updates on LI-RADS Treatment Response Criteria for Hepatocellular Carcinoma: Focusing on MRI.

As the incidence of hepatocellular carcinoma (HCC) and subsequent treatments with liver-directed therapies rise, the complexity of assessing lesion response has also increased. The Liver Imaging Reporting and Data Systems (LI-RADS) treatment response algorithm (LI-RADS TRA) was created to standardize the assessment of response after locoregional therapy (LRT) on contrast-enhanced CT or MRI. Originally created based on expert opinion, these guidelines are currently undergoing revision based on emerging evidence. While many studies support the use of LR-TRA for evaluation of HCC response after thermal ablation and intra-arterial embolic therapy, data suggest a need for refinements to improve assessment after radiation therapy. In this manuscript, we review expected MR imaging findings after different forms of LRT, clarify how to apply the current LI-RADS TRA by type of LRT, explore emerging literature on LI-RADS TRA, and highlight future updates to the algorithm. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

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.

Ultrasound-guided aspiration in addition to antibiotics for treatment of liver abscess in children: A randomized controlled trial.

BACKGROUND AND AIM: The criteria for aspiration for pediatric liver abscess are unclear. In this randomized controlled trial, we evaluated the efficacy of ultrasound-guided needle aspiration in addition to antibiotics in children with uncomplicated liver abscess. METHODS: We enrolled 110 children aged 1-18 years (mean [SD] = 7.7 [3.7] years) with uncomplicated liver abscess. The primary outcome was clinical cure at 6 weeks (absence of fever and abdominal pain in the preceding 14 days with reduction in abscess size on ultrasonography). The secondary outcomes were clinical response at 4 weeks, fever resolution time, time to abdominal pain reduction and abdominal tenderness, duration of hospitalization, and treatment failure. RESULTS: Clinical cure at 6 weeks was not significantly different (48/50 [96%] vs 39/46 [85%]; P = 0.082) between aspiration plus antibiotics group and antibiotics only group. The clinical response at 4 weeks was also comparable (49/50 [98%] vs 43/46 [93.5%]; P = 0.347). The mean (SD) of fever resolution time was significantly less in the aspiration plus antibiotics group (198 [90.8] h vs 248.2 [104.6] h; P = 0.014). Time to achieve reduction in abdominal pain (8.32 [3.1] vs 9.46 [3.1] days; P = 0.077) and abdominal tenderness (5.7 [2.4] vs 6.3 [2.3] days; P = 0.242), duration of hospitalization (16.6 [3.9] vs 18.2 [4.4] days; P = 0.07), and adverse event profile (9/50 [18%] vs 14/46 [30%]; P = 0.217) were comparable between the two groups. CONCLUSION: Majority of children with uncomplicated liver abscess achieved clinical cure at 6 weeks with intravenous antibiotics, irrespective of aspiration. However, needle aspiration may slightly reduce the duration of fever and abdominal pain/abdominal tenderness.

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.

Clinical applications of artificial intelligence in liver imaging.

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.

Spatiotemporal imaging and analysis of mouse and human liver bud morphogenesis.

BACKGROUND: The process of liver organogenesis has served as a paradigm for organ formation. However, there remains a lack of understanding regarding early mouse and human liver bud morphogenesis and early liver volumetric growth. Elucidating dynamic changes in liver volumes is critical for understanding organ development, implementing toxicological studies, and for modeling hPSC-derived liver organoid growth. New visualization, analysis, and experimental techniques are desperately needed. RESULTS: Here, we combine observational data with digital resources, new 3D imaging approaches, retrospective analysis of liver volume data, mathematical modeling, and experiments with hPSC-derived liver organoids. Mouse and human liver organogenesis, characterized by exponential growth, demonstrate distinct spatial features and growth curves over time, which we mathematically modeled using Gompertz models. Visualization of liver-epithelial and septum transversum mesenchyme (STM) interactions suggests extended interactions, which together with new spatial features may be responsible for extensive exponential growth. These STM interactions are modeled with a novel in vitro human pluripotent stem cell (hPSC)-derived hepatic organoid system that exhibits cell migration. CONCLUSIONS: Our methods enhance our understanding of liver organogenesis, with new 3D visualization, analysis, mathematical modeling, and in vitro models with hPSCs. Our approach highlights mouse and human differences and provides potential hypothesis for further investigation in vitro and in vivo.

[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.

Clinical utility of 30% relative decline in MRI-PDFF in predicting fibrosis regression in non-alcoholic fatty liver disease.

OBJECTIVE: Emerging data suggest that a 30% relative decline in liver fat, as assessed by MRI-proton density fat fraction (MRI-PDFF), may be associated with Non-Alcoholic Fatty Liver Disease Activity Score improvement, but the association between decline in MRI-PDFF and fibrosis regression is not known. Therefore, we aimed to examine the association between ≥30% relative decline in MRI-PDFF and fibrosis regression in non-alcoholic fatty liver disease (NAFLD). DESIGN: This prospective study included 100 well-characterised patients with biopsy-proven NAFLD with paired contemporaneous MRI-PDFF assessment at two time points. MRI-PDFF response was defined as ≥30% relative decline in MRI-PDFF. The primary outcome was ≥1 stage histological fibrosis regression. RESULTS: The median (IQR) age was 54 (43-62) years and body mass index was 31.9 (29-36) kg/m2. In multivariable-adjusted logistic regression analysis (adjusted for age, gender, diabetes status, race/ethnicity, interval between biopsies, gamma-glutamyl transferase, liver stiffness by magnetic resonance elastography and change in platelet counts), MRI-PDFF response was an independent predictor of fibrosis regression with an adjusted OR of 6.46 (95% CI 1.1 to 37.0, p=0.04). The proportion of patients with MRI-PDFF response with fibrosis regression, no change in fibrosis and fibrosis progression was 40.0%, 24.6% and 13.0%, respectively, and the proportion of patients with MRI-PDFF response increased with fibrosis regression (p=0.03). CONCLUSION: ≥30% reduction in MRI-PDFF in early phase trials can provide a useful estimate of odds of ≥1 stage improvement in fibrosis. These data may be helpful in sample size estimation in non-alcoholic steatohepatitis trials.

Robust partial Fourier reconstruction for diffusion-weighted imaging using a recurrent convolutional neural network.

PURPOSE: To develop an algorithm for robust partial Fourier (PF) reconstruction applicable to diffusion-weighted (DW) images with non-smooth phase variations. METHODS: Based on an unrolled proximal splitting algorithm, a neural network architecture is derived, which alternates between data consistency operations and regularization implemented by recurrent convolutions. In order to exploit correlations, multiple repetitions of the same slice are jointly reconstructed under consideration of permutation-equivariance. The algorithm is trained on DW liver data of 60 volunteers and evaluated on retrospectively and prospectively subsampled data of different anatomies and resolutions. RESULTS: The proposed method is able to significantly outperform conventional PF techniques on retrospectively subsampled data in terms of quantitative measures as well as perceptual image quality. In this context, joint reconstruction of repetitions as well as the particular type of recurrent network unrolling are found to be beneficial with respect to reconstruction quality. On prospectively PF-sampled data, the proposed method enables DW imaging with higher signal without sacrificing image resolution or introducing additional artifacts. Alternatively, it can be used to counter the TE increase in acquisitions with higher resolution. Furthermore, generalizability can be shown to prospective brain data exhibiting anatomies and contrasts not present in the training set. CONCLUSION: This work demonstrates that robust PF reconstruction of DW data is feasible even at strong PF factors in anatomies prone to phase variations. Since the proposed method does not rely on smoothness priors of the phase but uses learned recurrent convolutions instead, artifacts of conventional PF methods can be avoided.

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.