Blood markers for type-1,-2, and -3 inflammation are associated with severity of acutely decompensated cirrhosis.

BACKGROUND AND AIMS: In patients with acutely decompensated cirrhosis (ADC) who present with clinically apparent precipitants (i.e., infections, acute liver injury), alterations in blood markers of inflammation associate with progression toward severe phenotypes (e.g., acute-on-chronic liver failure, ACLF). It is unclear whether alterations in blood inflammatory markers may associate with progression of ADC independently of precipitants. METHODS: We prospectively enrolled 394 patients admitted for ADC who were classified into four phenotypes of increasing severity: no organ dysfunction (n=168), organ dysfunction alone (n=72), organ failure without ACLF (n=91), and ACLF (n=63). Clinical blood cell counts and serum levels of inflammatory markers (including soluble markers related to type-1, type-2, and type-3 inflammation) were obtained at enrollment. Ordinal regression with adjacent categories logit model adjusted for confounders (including precipitants) was used to analyze associations between changes in each blood inflammatory marker and the worsening of ADC. RESULTS: Inflammatory markers that were associated with higher risk of progressing to the next more severe stage were as follows: increasing neutrophil counts (adjusted common odds ratio [cOR] 1.17, 95%CI 1.06-1.28); increasing levels of the type-2 cytokine interleukin (IL)-25 (cOR 1.21, 95%CI 1.06-1.39), type-3 cytokines IL-6 (cOR 1.15, 95%CI 1.02-1.28) and IL-22 (cOR 1.16, 95%CI 1.03-1.30), or anti-inflammatory soluble CD163 (cOR 1.94, 95%CI 1.58-2.38); decreasing lymphocyte counts (cOR 0.77, 95%CI 0.68-0.87), or decreasing levels of the type-1 cytokine IFN-γ (cOR 0.85, 95%CI 0.75-0.95). CONCLUSIONS: Among patients with ADC, alterations in blood levels of cytokines related to type-1, type-2 and type-3 inflammation, together with neutrophilia, lymphopenia and elevated anti-inflammatory signals were individually associated with an increased risk of progressing toward ACLF, independently of the presence of clinically apparent precipitants.

Denoising of volumetric magnetic resonance imaging using multi-channel three-dimensional convolutional neural network with applications on fast spin echo acquisitions.

Background: Three-dimensional (3D) magnetic resonance imaging (MRI) can be acquired with a high spatial resolution with flexibility being reformatted into arbitrary planes, but at the cost of reduced signal-to-noise ratio. Deep-learning methods are promising for denoising in MRI. However, the existing 3D denoising convolutional neural networks (CNNs) rely on either a multi-channel two-dimensional (2D) network or a single-channel 3D network with limited ability to extract high dimensional features. We aim to develop a deep learning approach based on multi-channel 3D convolution to utilize inherent noise information embedded in multiple number of excitation (NEX) acquisition for denoising 3D fast spin echo (FSE) MRI. Methods: A multi-channel 3D CNN is developed for denoising multi-NEX 3D FSE magnetic resonance (MR) images based on the feature extraction of 3D noise distributions embedded in 2-NEX 3D MRI. The performance of the proposed approach was compared to several state-of-the-art MRI denoising methods on both synthetic and real knee data using 2D and 3D metrics of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Results: The proposed method achieved improved denoising performance compared to the current state-of-the-art denoising methods in both slice-by-slice 2D and volumetric 3D metrics of PSNR and SSIM. Conclusions: A multi-channel 3D CNN is developed for denoising of multi-NEX 3D FSE MR images. The superior performance of the proposed multi-channel 3D CNN in denoising multi-NEX 3D MRI demonstrates its potential in tasks that require the extraction of high-dimensional features.

Indirect Shear Wave Excitation for Brain Magnetic Resonance Elastography With Minimal Cerebral Blood Flow Alteration.

Magnetic resonance elastography (MRE) of brain relies on inducing and measuring shear waves in the brain. However, studies have shown vibration could induce changes in cerebral blood flow (CBF), which has a modulation effect and can affect the biomechanical properties measured. OBJECTIVE: This work demonstrates the initial prototype of the indirect excitation method, which can generate shear waves in the brain with minimal changes in CBF. METHODS: A simple system was designed to produce stable vibrations underneath the neck. Instead of directly stimulating the skull, shear waves were indirectly transmitted to the brain through the spine and brainstem. RESULTS: Phantom results showed that the proposed actuator did not interfere with the routine imaging sequence and successfully generated multifrequency shear waves. When compared with the conventional direct head stimulation method, brain MRE results from the proposed actuator showed no significant differences in terms of intraclass correlation coefficients (ICC) and coefficients of variation (CV). Moreover, the octahedral shear strain (OSS) generated by the indirect excitation in the frontal and parietal lobes decreased by 25.96% and 16.73% respectively. Evaluation of CBF in healthy volunteers revealed no significant changes for the indirect excitation method, whereas significant decreases in CBF were observed in four subregions when employing direct excitation. CONCLUSION: The proposed actuator offers a more accurate and comfortable approach to MRE measurements while causing minimal CBF alterations. SIGNIFICANCE: This work presents the first demonstration of an indirect excitation brain MRE system that minimizes CBF changes, thus holding potential for future applications of brain MRE.

A Comparative Study of Three Systems for Liver Magnetic Resonance Elastography.

BACKGROUND: Different MR elastography (MRE) systems may produce different stiffness measurements, making direct comparison difficult in multi-center investigations. PURPOSE: To assess the repeatability and reproducibility of liver stiffness measured by three typical MRE systems. STUDY TYPE: Prospective. POPULATION/PHANTOMS: Thirty volunteers without liver disease history (20 males, aged 21-28)/5 gel phantoms. FIELD STRENGTH/SEQUENCE: 3.0 T United Imaging Healthcare (UIH), 1.5 T Siemens Healthcare, 3.0 T General Electric Healthcare (GE)/Echo planar imaging-based MRE sequence. ASSESSMENT: Wave images of volunteers and phantoms were acquired by three MRE systems. Tissue stiffness was evaluated by two observers, while phantom stiffness was assessed automatically by code. The reproducibility across three MRE systems was quantified based on the mean stiffness of each volunteer and phantom. STATISTICAL TESTS: Intraclass correlation coefficients (ICC), coefficients of variation (CV), and Bland-Altman analyses were used to assess the interobserver reproducibility, the interscan repeatability, and the intersystem reproducibility. Paired t-tests were performed to assess the interobserver and interscan variation. Friedman tests with Dunn's multiple comparison correction were performed to assess the intersystem variation. P values less than 0.05 indicated significant difference. RESULTS: The reproducibility of stiffness measured by the two observers demonstrated consistency with ICC > 0.92, CV < 4.32%, Mean bias < 2.23%, and P > 0.06. The repeatability of measurements obtained using the electromagnetic system for the liver revealed ICC > 0.96, CV < 3.86%, Mean bias < 0.19%, P > 0.90. When considering the range of reproducibility across the three systems for liver evaluations, results ranged with ICCs from 0.70 to 0.87, CVs from 6.46% to 10.99%, and Mean biases between 1.89% and 6.30%. Phantom studies showed similar results. The values of measured stiffness differed across all three systems significantly. DATA CONCLUSION: Liver stiffness values measured from different MRE systems can be different, but the measurements across the three MRE systems produced consistent results with excellent reproducibility. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Viscoelastic parameters derived from multifrequency MR elastography for depicting hepatic fibrosis and inflammation in chronic viral hepatitis.

OBJECTIVES: The capability of MR elastography (MRE) to differentiate fibrosis and inflammation, and to provide precise diagnoses is crucial, whereas the coexistence of fibrosis and inflammation may obscure the diagnostic accuracy. METHODS: In this retrospective study, from June 2020 to December 2022, chronic viral hepatitis patients who underwent multifrequency MRE (mMRE) were included in, and further divided into, training and validation cohorts. The hepatic viscoelastic parameters [shear wave speed (c) and loss angle (φ) of the complex shear modulus] were obtained from mMRE. The logistic regression and receiver operating characteristic (ROC) curves were generated to evaluate performance of viscoelastic parameters for fibrosis and inflammation. RESULTS: A total of 233 patients were assigned to training cohort and validation cohorts (mean age, 52 years ± 13 (SD); 51 women; training cohort, n = 170 (73%), and validation cohort, n = 63 (27%)). Liver c exhibited superior performance in detecting fibrosis with ROC (95% confidence interval) of ≥ S1 (0.96 (0.92-0.99)), ≥ S2 (0.86 (0.78-0.92)), ≥ S3 (0.89 (0.84-0.95)), and S4 (0.88 (0.83-0.93)). Similarly, φ was effective in diagnosing inflammation with ROC values of ≥ G2 (0.72 (0.63-0.81)), ≥ G3 (0.88 (0.83-0.94)), and G4 (0.92 (0.87-0.98)). And great predictive discrimination for fibrosis and inflammation were shown in validation cohort (all AUCs > 0.75). CONCLUSION: The viscoelastic parameters derived from multifrequency MRE could realize simultaneous detection of hepatic fibrosis and inflammation. CRITICAL RELEVANCE STATEMENT: Fibrosis and inflammation coexist in chronic liver disease which obscures the diagnostic performance of MR elastography, whereas the viscoelastic parameters derived from multifrequency MR elastography could realize simultaneous detection of hepatic fibrosis and inflammation. KEY POINTS: • Hepatic biomechanical parameters derived from multifrequency MR elastography could effectively detect fibrosis and inflammation. • Liver stiffness is useful for detecting fibrosis independent of inflammatory activity. • Fibrosis could affect the diagnostic efficacy of liver viscosity in inflammation, especially in early-grade of inflammation.

Three-Dimensional Multifrequency MR Elastography for Microvascular Invasion and Prognosis Assessment in Hepatocellular Carcinoma.

BACKGROUND: Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies. PURPOSE: To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE). STUDY TYPE: Prospective. POPULATION: 188 patients with HCC, divided into a training cohort (n = 150) and a validation cohort (n = 38). In the training cohort, 106/150 patients completed a 2-year follow-up. FIELD STRENGTH/SEQUENCE: 1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences. ASSESSMENT: Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors. STATISTICAL TESTS: Student's t-test, Mann-Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log rank tests. P < 0.05 was considered significant. RESULTS: Tumor c and liver c were independent predictors of MVI and RFS, respectively. Adding tumor c significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance. DATA CONCLUSION: Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value.

PURPOSE: Diffusion-weighted imaging (DWI) of the liver suffers from low resolution, noise, and artifacts. This study aimed to investigate the effect of deep learning reconstruction (DLR) on image quality and apparent diffusion coefficient (ADC) quantification of liver DWI at 3 Tesla. METHOD: In this prospective study, images of the liver obtained at DWI with b-values of 0 (DWI0), 50 (DWI50) and 800 s/mm2 (DWI800) from consecutive patients with liver lesions from February 2022 to February 2023 were reconstructed with and without DLR (non-DLR). Image quality was assessed qualitatively using Likert scoring system and quantitatively using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and liver/parenchyma boundary sharpness from region-of-interest (ROI) analysis. ADC value of lesion were measured. Phantom experiment was also performed to investigate the factors that determine the effect of DLR on ADC value. Qualitative score, SNR, CNR, boundary sharpness, and apparent diffusion coefficients (ADCs) for DWI were compared using paired t-test and Wilcoxon signed rank test. P < 0.05 was considered statistically significant. RESULTS: A total of 85 patients with 170 lesions were included. DLR group showed a higher qualitative score than the non-DLR group. for example, with DWI800 the score was 4.77 ± 0.52 versus 4.30 ± 0.63 (P < 0.001). DLR group also showed higher SNRs, CNRs and boundary sharpness than the non-DLR group. DLR reduced the ADC of malignant tumors (1.105[0.904, 1.340] versus 1.114[0.904, 1.320]) (P < 0.001), but there was no significant difference in the diagnostic value of malignancy for DLR and non-DLR groups (P = 57.3). The phantom study confirmed a reduction of ADC in images with low resolution, and a stronger reduction of ADC in heterogeneous structures than in homogeneous ones (P < 0.001). CONCLUSIONS: DLR improved image quality of liver DWI. DLR reduced the ADC value of lesions, but did not affect the diagnostic performance of ADC in distinguishing malignant tumors on a 3.0-T MRI system.

Three-dimensional multifrequency magnetic resonance elastography improves preoperative assessment of proliferative hepatocellular carcinoma.

BACKGROUND: To investigate the viscoelastic signatures of proliferative hepatocellular carcinoma (HCC) using three-dimensional (3D) magnetic resonance elastography (MRE). METHODS: This prospective study included 121 patients with 124 HCCs as training cohort, and validation cohort included 33 HCCs. They all underwent preoperative conventional magnetic resonance imaging (MRI) and tomoelastography based on 3D multifrequency MRE. Viscoelastic parameters of the tumor and liver were quantified as shear wave speed (c, m/s) and loss angle (φ, rad), representing stiffness and fluidity, respectively. Five MRI features were evaluated. Multivariate logistic regression analyses were used to determine predictors of proliferative HCC to construct corresponding nomograms. RESULTS: In training cohort, model 1 (Combining cirrhosis, hepatitis virus, rim APHE, peritumoral enhancement, and tumor margin) yielded an area under the curve (AUC), sensitivity, specificity, accuracy of 0.72, 58.73%,78.69%, 67.74%, respectively. When adding MRE properties (tumor c and tumor φ), established model 2, the AUC increased to 0.81 (95% CI 0.72-0.87), with sensitivity, specificity, accuracy of 71.43%, 81.97%, 75%, respectively. The C-index of nomogram of model 2 was 0.81, showing good performance for proliferative HCC. Therefore, integrating tumor c and tumor φ can significantly improve the performance of preoperative diagnosis of proliferative HCC (AUC increased from 0.72 to 0.81, p = 0.012). The same finding was observed in the validation cohort, with AUC increasing from 0.62 to 0.77 (p = 0.021). CONCLUSIONS: Proliferative HCC exhibits low stiffness and high fluidity. Adding MRE properties (tumor c and tumor φ) can improve performance of conventional MRI for preoperative diagnosis of proliferative HCC. CRITICAL RELEVANCE STATEMENT: We investigated the viscoelastic signatures of proliferative hepatocellular carcinoma (HCC) using three-dimensional (3D) magnetic resonance elastography (MRE), and find that adding MRE properties (tumor c and tumor φ) can improve performance of conventional MRI for preoperative diagnosis of proliferative HCC.

Clinic-radiomics model using liver magnetic resonance imaging helps predict chronicity of drug-induced liver injury.

BACKGROUND AND AIMS: Some drug-induced liver injury (DILI) cases may become chronic, even after drug withdrawal. Radiomics can predict liver disease progression. We established and validated a predictive model incorporating the clinical characteristics and radiomics features for predicting chronic DILI. METHODS: One hundred sixty-eight DILI patients who underwent liver gadolinium-diethylenetriamine pentaacetate-enhanced magnetic resonance imaging were recruited. The patients were clinically diagnosed using the Roussel Uclaf causality assessment method. Patients who progressed to chronicity or recovery were randomly divided into the training (70%) and validation (30%) cohorts, respectively. Hepatic T1-weighted images were segmented to extract 1672 radiomics features. Least absolute shrinkage and selection operator regression was used for feature selection, and Rad-score was constructed using support vector machines. Multivariable logistic regression analysis was performed to build a clinic-radiomics model incorporating clinical characteristics and Rad-scores. The clinic-radiomics model was evaluated for its discrimination, calibration, and clinical usefulness in the independent validation set. RESULTS: Of 1672 radiomics features, 28 were selected to develop the Rad-score. Cholestatic/mixed patterns and Rad-score were independent risk factors of chronic DILI. The clinic-radiomics model, including the Rad-score and injury patterns, distinguished chronic from recovered DILI patients in the training (area under the receiver operating characteristic curve [AUROC]: 0.89, 95% confidence interval [95% CI]: 0.87-0.92) and validation (AUROC: 0.88, 95% CI: 0.83-0.91) cohorts with good calibration and great clinical utility. CONCLUSION: The clinic-radiomics model yielded sufficient accuracy for predicting chronic DILI, providing a practical and non-invasive tool for managing DILI patients.

MR Elastography With Optimization-Based Phase Unwrapping and Traveling Wave Expansion-Based Neural Network (TWENN).

Magnetic Resonance Elastography (MRE) can characterize biomechanical properties of soft tissue for disease diagnosis and treatment planning. However, complicated wavefields acquired from MRE coupled with noise pose challenges for accurate displacement extraction and modulus estimation. Using optimization-based displacement extraction and Traveling Wave Expansion-based Neural Network (TWENN) modulus estimation, we propose a new pipeline for processing MRE images. An objective function with Dual Data Consistency (Dual-DC) has been used to ensure accurate phase unwrapping and displacement extraction. For the estimation of complex wavenumbers, a complex-valued neural network with displacement covariance as an input has been developed. A model of traveling wave expansion is used to generate training datasets for the network with varying levels of noise. The complex shear modulus map is obtained through fusion of multifrequency and multidirectional data. Validation using brain and liver simulation images demonstrates the practical value of the proposed pipeline, which can estimate the biomechanical properties with minimal root-mean-square errors when compared to state-of-the-art methods. Applications of the proposed method for processing MRE images of phantom, brain, and liver reveal clear anatomical features, robustness to noise, and good generalizability of the pipeline.

Multi-channel GAN-based calibration-free diffusion-weighted liver imaging with simultaneous coil sensitivity estimation and reconstruction.

Introduction: Diffusion-weighted imaging (DWI) with parallel reconstruction may suffer from a mismatch between the coil calibration scan and imaging scan due to motions, especially for abdominal imaging. Methods: This study aimed to construct an iterative multichannel generative adversarial network (iMCGAN)-based framework for simultaneous sensitivity map estimation and calibration-free image reconstruction. The study included 106 healthy volunteers and 10 patients with tumors. Results: The performance of iMCGAN was evaluated in healthy participants and patients and compared with the SAKE, ALOHA-net, and DeepcomplexMRI reconstructions. The peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), root mean squared error (RMSE), and histograms of apparent diffusion coefficient (ADC) maps were calculated for assessing image qualities. The proposed iMCGAN outperformed the other methods in terms of the PSNR (iMCGAN: 41.82 ± 2.14; SAKE: 17.38 ± 1.78; ALOHA-net: 20.43 ± 2.11 and DeepcomplexMRI: 39.78 ± 2.78) for b = 800 DWI with an acceleration factor of 4. Besides, the ghosting artifacts in the SENSE due to the mismatch between the DW image and the sensitivity maps were avoided using the iMCGAN model. Discussion: The current model iteratively refined the sensitivity maps and the reconstructed images without additional acquisitions. Thus, the quality of the reconstructed image was improved, and the aliasing artifact was alleviated when motions occurred during the imaging procedure.

Reduced tumor stiffness quantified by tomoelastography as a predicative marker for glypican-3-positive hepatocellular carcinoma.

Background: Glypican-3 (GPC3) expression is investigated as a promising target for tumor-specific immunotherapy of hepatocellular carcinoma (HCC). This study aims to determine whether GPC3 alters the viscoelastic properties of HCC and whether tomoelastography, a multifrequency magnetic resonance elastography (MRE) technique, is sensitive to it. Methods: Ninety-five participants (mean age, 58 ± 1 years; 78 men and 17 women) with 100 pathologically confirmed HCC lesions were enrolled in this prospective study from July 2020 to August 2021. All patients underwent preoperative multiparametric MRI and tomoelastography. Tomoelastography provided shear wave speed (c, m/s) representing tissue stiffness and loss angle (φ, rad) relating to viscosity. Clinical, laboratory, and imaging parameters were compared between GPC3-positive and -negative groups. Univariable and multivariable logistic regression were performed to determine factors associated with GPC3-positive HCC. The diagnostic performance of combined biomarkers was established using logistic regression analysis. Area-under-the-curve (AUC) analysis was done to assess diagnostic performance in detecting GPC3-positive HCC. Findings: GPC3-positive HCCs (n=72) had reduced stiffness compared with GPC3-negative HCCs (n=23) while viscosity was not different (c: 2.34 ± 0.62 versus 2.72 ± 0.62 m/s, P=0.010, φ: 1.11 ± 0.21 vs 1.18 ± 0.27 rad, P=0.21). Logistic regression showed c and elevated serum alpha-fetoprotein (AFP) level above 20 ng/mL were independent factors for GPC3-positive HCC. Stiffness with a cutoff of c = 2.8 m/s in conjunction with an elevated AFP yielded a sensitivity of 80.3%, specificity of 70.8%, and AUC of 0.80. Interpretation: Reduced stiffness quantified by tomoelastography may be a mechanical signature of GPC3-positive HCC. Combining reduced tumor stiffness and elevated AFP level may provide potentially valuable biomarker for GPC3-targeted immunotherapy.

Noninvasive Assessment of Liver Fibrosis and Inflammation in Chronic Hepatitis B: A Dual-task Convolutional Neural Network (DtCNN) Model Based on Ultrasound Shear Wave Elastography.

Background and Aims: Liver stiffness (LS) measured by shear wave elastography (SWE) is often influenced by hepatic inflammation. The aim was to develop a dual-task convolutional neural network (DtCNN) model for the simultaneous staging of liver fibrosis and inflammation activity using 2D-SWE. Methods: A total of 532 patients with chronic hepatitis B (CHB) were included to develop and validate the DtCNN model. An additional 180 consecutive patients between December 2019 and April 2021 were prospectively included for further validation. All patients underwent 2D-SWE examination and serum biomarker assessment. A DtCNN model containing two pathways for the staging of fibrosis and inflammation was used to improve the classification of significant fibrosis (≥F2), advanced fibrosis (≥F3) as well as cirrhosis (F4). Results: Both fibrosis and inflammation affected LS measurements by 2D-SWE. The proposed DtCNN performed the best among all the classification models for fibrosis stage [significant fibrosis AUC=0.89 (95% CI: 0.87-0.92), advanced fibrosis AUC=0.87 (95% CI: 0.84-0.90), liver cirrhosis AUC=0.85 (95% CI: 0.81-0.89)]. The DtCNN-based prediction of inflammation activity achieved AUCs of 0.82 (95% CI: 0.78-0.86) for grade ≥A1, 0.88 (95% CI: 0.85-0.90) grade ≥A2 and 0.78 (95% CI: 0.75-0.81) for grade ≥A3, which were significantly higher than the AUCs of the single-task groups. Similar findings were observed in the prospective study. Conclusions: The proposed DtCNN improved diagnostic performance compared with existing fibrosis staging models by including inflammation in the model, which supports its potential clinical application.

Correlation analysis of structural and biomechanical properties of hepatocellular carcinoma tissue.

It is known that biomechanical and structural properties of tumor tissues are potential biomarkers for the diagnosis and prognosis of tumors such as Hepatocellular carcinoma (HCC). Although there are many studies on the characterization of biomechanical properties of HCC at the cellular level, limited information is known from in vitro studies. Here, tissue samples from 14 patients diagnosed with HCC were analyzed. Indentation tests showed the instantaneous shear modulus and long-term shear modulus of the HCC tissue were 1.19 ± 0.86 kPa and 0.29 ± 0.25 kPa, respectively. The volume fraction of collagen fibers estimated by analyzing the histology images was positively correlated with either instantaneous shear modulus (r = 0.64, p = 0.016) or long-term shear modulus (r = 0.76, p = 0.002). Diffusion-weighted images with 13b-values were also collected and the diffusivity from Intravoxel Incoherent Motion (IVIM) and Diffusion Kurtosis Imaging (DKI) models were 1.09 ± 0.47 ∙ 10-3 mm2/s and 2.06 ± 0.83∙10-3 mm2/s, respectively. Significant positive correlations were observed between long-term shear modulus and the diffusivity estimated from IVIM (r = 0.600, p = 0.026). The shifted apparent diffusion coefficient (ADC) estimated based on b = 600 and 2000 s/mm2 was negatively correlated with both instantaneous shear modulus (r = -0.745, p = 0.003) and long-term shear modulus (r = -0.591, p = 0.029). In addition, the diffusivity and non-Gaussian Kurtosis parameters estimated at different b values showed a significant negative correlation (r = -0.675, p = 0.010). Results revealed the close relationships between the structural and biomechanical properties of HCC tissue. The interrelation of diffusion and biomechanical properties are not only crucial for HCC characterization but could also provide helpful information for HCC diagnosis and prognosis.

Added Value of Viscoelasticity for MRI-Based Prediction of Ki-67 Expression of Hepatocellular Carcinoma Using a Deep Learning Combined Radiomics (DLCR) Model.

This study aimed to explore the added value of viscoelasticity measured by magnetic resonance elastography (MRE) in the prediction of Ki-67 expression in hepatocellular carcinoma (HCC) using a deep learning combined radiomics (DLCR) model. This retrospective study included 108 histopathology-proven HCC patients (93 males; age, 59.6 ± 11.0 years) who underwent preoperative MRI and MR elastography. They were divided into training (n = 87; 61.0 ± 9.8 years) and testing (n = 21; 60.6 ± 10.1 years) cohorts. An independent validation cohort including 43 patients (60.1 ± 11.3 years) was included for testing. A DLCR model was proposed to predict the expression of Ki-67 with cMRI, including T2W, DW, and dynamic contrast enhancement (DCE) images as inputs. The images of the shear wave speed (c-map) and phase angle (φ-map) derived from MRE were also fed into the DLCR model. The Ki-67 expression was classified into low and high groups with a threshold of 20%. Both c and φ values were ranked within the top six features for Ki-67 prediction with random forest selection, which revealed the value of MRE-based viscosity for the assessment of tumor proliferation status in HCC. When comparing the six CNN models, Xception showed the best performance for classifying the Ki-67 expression, with an AUC of 0.80 ± 0.03 (CI: 0.79-0.81) and accuracy of 0.77 ± 0.04 (CI: 0.76-0.78) when cMRI were fed into the model. The model with all modalities (MRE, AFP, and cMRI) as inputs achieved the highest AUC of 0.90 ± 0.03 (CI: 0.89-0.91) in the validation cohort. The same finding was observed in the independent testing cohort, with an AUC of 0.83 ± 0.03 (CI: 0.82-0.84). The shear wave speed and phase angle improved the performance of the DLCR model significantly for Ki-67 prediction, suggesting that MRE-based c and φ-maps can serve as important parameters to assess the tumor proliferation status in HCC.

Tomoelastography based on multifrequency MR elastography predicts liver function reserve in patients with hepatocellular carcinoma: a prospective study.

BACKGROUND: Estimating liver function reserve is essential for preoperative surgical planning and predicting post-hepatectomy complications in patients with hepatocellular carcinoma (HCC). We investigated hepatic viscoelasticity quantified by tomoelastography, a multifrequency magnetic resonance elastography technique, to predict liver function reserve. METHODS: One hundred fifty-six patients with suspected HCC (mean age, 60 ± 1 years; 131 men) underwent preoperative tomoelastography examination between July 2020 and August 2021. Sixty-nine were included in the final analysis, and their 15-min indocyanine green retention rates (ICG-R15s) were obtained to determine liver function reserve. Tomoelastography quantified the shear wave speed (c, m/s), which represents stiffness, and loss angle (φ, rad), which represents fluidity. Both were correlated with the ICG-R15. A prediction model based on logistic regression for major hepatectomy tolerance (ICG-R15 ≥ 14%) was established. RESULTS: Patients were assigned to either the ICG-R15 < 14% (n = 50) or ICG-R15 ≥ 14% (n = 19) group. Liver c (r = 0.617) and φ (r = 0.517) were positively correlated with the ICG-R15 (both p < 0.001). At fibrosis stages F1-2, φ was positively correlated with the ICG-R15 (r = 0.528; p = 0.017), but c was not (p = 0.104). At stages F3-4, c (r = 0.642; p < 0.001) and φ (r = 0.377; p = 0.008) were both positively correlated with the ICG-R15. The optimal cutoffs of c and φ for predicting ICG-R15 ≥ 14% were 2.04 m/s and 0.79 rad, respectively. The area under the receiver operating characteristic curve was higher for c (0.892) than for φ (0.779; p = 0.045). CONCLUSIONS: Liver stiffness and fluidity, quantified by tomoelastography, were correlated with liver function and may be used clinically to noninvasively assess liver function reserve and stratify treatments.

COVID-19 vaccines in patients with decompensated cirrhosis: a retrospective cohort on safety data and risk factors associated with unvaccinated status.

BACKGROUND: Safety data reported from the large-scale clinical trials of the coronavirus disease 2019 (COVID-19) vaccine are extremely limited in patients with decompensated cirrhosis. The vaccination campaign in this specific population could be difficult due to uncertainty about the adverse events following vaccination. We aimed to assessed the COVID-19 vaccination rate, factors associated with unvaccinated status, and the adverse events following vaccination in patients with decompensated cirrhosis. METHODS: This is a retrospective study from Ruijin Hospial (Shanghai, China) on an ongoing prospective cohort designed for long-term survival analysis of decompensated cirrhotic patients who recovered from decompensating events or acute-on-chronic liver failure (ACLF) between 2016 and 2018. We assessed the COVID-19 vaccination rate, the number of doses, type of vaccine, safety data, patient-reported reasons for remaining unvaccinated, factors associated with unvaccinated status, and the adverse events of COVID-19 vaccine. Binary logistic regression was used for identifying factors associated with unvaccinated status. RESULTS: A total of 229 patients with decompensated cirrhosis without previous SARS-CoV-2 infection participated (mean age, 56 ± 12.2 years, 75% male, 65% viral-related cirrhosis). Mode of decompensation were grade II‒III ascites (82.5%), gastroesophageal varices bleeding (7.9%), hepatic encephalopathy (7.9%). Eighty-five participants (37.1%) received at least one dose of vaccination (1 dose: n = 1, 2 doses: n = 65, 3 doses: n = 19) while 62.9% remained unvaccinated. Patient-reported reasons for remaining unvaccinated were mainly fear of adverse events (37.5%) and lack of positive advice from healthcare providers (52.1%). The experience of hepatic encephalopathy (OR = 5.61, 95% CI: 1.24-25.4) or ACLF (OR = 3.13, 95% CI: 1.12-8.69) and post-liver transplantation status (OR = 2.47, 95% CI: 1.06-5.76) were risk factors of remaining unvaccinated independent of residential areas. The safety analysis demonstrated that 75.3% had no adverse events, 23.6% had non-severe reactions (20% injection-site pain, 1.2% fatigue, 2.4% rash) and 1.2% had a severe event (development of acute decompensation requiring hospitalization). CONCLUSIONS: Patients with decompensated cirrhosis in eastern China are largely remained at unvaccinated status, particularly those with previous episodes of ACLF or hepatic encephalopathy and liver transplantation recipients. Vaccination against COVID-19 in this population is safe.

A new visual quantitative assessment of ultrasound attenuation parameters for the mild liver steatosis.

Background: Controlled attenuation parameter (CAP) without the guidance of the grey scale sonogram was a classic method in the quantitative evaluation of liver steatosis, it is recommended by international guidelines. Our study aimed to compare the diagnostic efficiency of a new real-time visual liver steatosis analysis (LiSA) versus CAP in chronic hepatitis B patients with liver steatosis. Methods: Patients were enrolled who underwent liver biopsy and received both LiSA (Hepatus, Mindray, probe LFP5-1U/s, China) and CAP (FibroScan502, Echosens, probe M, France) measurement simultaneously in our hospital from November 2018 to December 2019. The obtained values were both expressed as dB/m. Based on the liver fat content validated by liver biopsy, these patients were divided into the S0 group (fat content <5%) and S1 group (fat content ≥5%). The efficiency of the LiSA and CAP value in the diagnosis of liver steatosis was evaluated. Independent factors influencing the LiSA value were predicted by correlation analysis and multiple linear regression analysis. Results: A total of 151 patients were included in the analysis according to the exclusion criteria from 304 enrolled liver biopsy chronic hepatitis B (CHB) patients. Both LiSA and CAP successfully differentiated the S0 group from the S1 group. Receiver operating characteristic (ROC) curves showed that both LiSA and CAP had good diagnostic performance [area under the ROC curve area under the curve (AUC) >0.7] in evaluating liver steatosis, while there was no significant difference between the 2 methods (AUC 0.825 vs. 0.798, P=0.067). Using the optimal cutoff point, the specificity and sensitivity of LiSA in diagnosing liver steatosis were 89.18% and 79.16%, respectively. The specificity and sensitivity of CAP in diagnosing liver steatosis were 87.20% and 76.31%, respectively. Conclusions: Both LiSA and CAP are efficient for evaluating liver steatosis noninvasively.

Fast magnetic resonance elastography with multiphase radial encoding and harmonic motion sparsity based reconstruction.

Objective. To achieve fast magnetic resonance elastography (MRE) at a low frequency for better shear modulus estimation of the brain.Approach. We proposed a multiphase radial DENSE MRE (MRD-MRE) sequence and an improved GRASP algorithm utilizing the sparsity of the harmonic motion (SH-GRASP) for fast MRE at 20 Hz. For the MRD-MRE sequence, the initial position encoded by spatial modulation of magnetization (SPAMM) was decoded by an arbitrary number of readout blocks without increasing the number of phase offsets. Based on the harmonic motion, a modified total variation and temporal Fourier transform were introduced to utilize the sparsity in the temporal domain. Both phantom and brain experiments were carried out and compared with that from multiphase Cartesian DENSE-MRE (MCD-MRE), and conventional gradient echo sequence (GRE-MRE). Reconstruction performance was also compared with GRASP and compressed sensing.Main results. Results showed the scanning time of a fully sampled image with four phase offsets for MRD-MRE was only 1/5 of that from GRE-MRE. The wave patterns and estimated stiffness maps were similar to those from MCD-MRE and GRE-MRE. With SH-GRASP, the total scan time could be shortened by additional 4 folds, achieving a total acceleration factor of 20. Better metric values were also obtained using SH-GRASP for reconstruction compared with other algorithms.Significance. The MRD-MRE sequence and SH-GRASP algorithm can be used either in combination or independently to accelerate MRE, showing the potentials for imaging the brain as well as other organs.

Differentiation of Hepatocellular Carcinoma from Hepatic Hemangioma and Focal Nodular Hyperplasia using Computed Tomographic Spectral Imaging.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy. This study was designed to investigate the value of computed tomography (CT) spectral imaging in differentiating HCC from hepatic hemangioma (HH) and focal nodular hyperplasia (FNH). METHODS: This was a retrospective study of 51 patients who underwent spectral multiple-phase CT at 40-140 keV during the arterial phase (AP) and portal venous phase (PP). Slopes of the spectral curves, iodine density, water density derived from iodine- and water-based material decomposition images, iodine uptake ratio (IUR), normalized iodine concentration, and the ratio of iodine concentration in liver lesions between AP and PP were measured or calculated. RESULTS: As energy level decreased, the CT values of HCC (n=31), HH (n=17), and FNH (n=7) increased in both AP and PP. There were significant differences in IUR in the AP, IUR in the PP, normalized iodine concentration in the AP, slope in the AP, and slope in the PP among HCC, HH, and FNH. The CT values in AP, IUR in the AP and PP, normalized iodine concentration in the AP, slope in the AP and PP had high sensitivity and specificity in differentiating HH and HCC from FNH. Quantitative CT spectral data had higher sensitivity and specificity than conventional qualitative CT image analysis during the combined phases. CONCLUSIONS: Mean CT values at low energy (40-90 keV) and quantitative analysis of CT spectral data (IUR in the AP) could be helpful in the differentiation of HCC, HH, and FNH.