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Background: The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance., Purpose: To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients., Materials and Methods: A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation., Results: AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239-5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178-4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007-4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948)., Conclusion: SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits., (© 2024. Italian Society of Medical Radiology.)

Objectives: Impeded diffusion fraction (IDF) is a novel and promising diffusion-weighted imaging (DWI) technique that allows for the detection of various diffusion compartments, including macromolecular coordinated water, free diffusion, perfusion, and cellular free water. This study aims to investigate the clinical potential of IDF-DWI in detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC)., Methods: 66 patients were prospectively included. Metrics derived from IDF-DWI and the apparent diffusion coefficient (ADC) were calculated. Multivariate logistic regression was employed to identify clinical risk factors. Diagnostic performance was evaluated using the area under the receiver operating characteristics curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and the calibration error (cal-error). Additionally, a power analysis was conducted to determine the required sample size., Results: The results suggested a significantly higher fraction of impeded diffusion (F ID ) originating from IDF-DWI in MVI-positive HCCs (p < 0.001). Moreover, the ADC was found to be significantly lower in MVI-positive HCCs (p = 0.019). Independent risk factors of MVI included larger tumor size and elevated alpha-fetoprotein (AFP) levels. The nomogram model incorporating ADC, F ID , tumor size, and AFP level yielded the highest diagnostic accuracy for MVI (AUC-PR = 0.804, AUC-ROC = 0.783, cal-error = 0.044), followed by F ID (AUC-PR = 0.693, AUC-ROC = 0.760, cal-error = 0.060) and ADC (AUC-PR = 0.570, AUC-ROC = 0.651, cal-error = 0.164)., Conclusion: IDF-DWI shows great potential in noninvasively, accurately, and preoperatively detecting MVI in HCC and may offer clinical benefits for prognostic prediction and determination of treatment strategy., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: Habitat imaging allows for the quantification and visualization of various subregions within the tumor. We aim to develop an approach using diffusion-weighted imaging (DWI)-based habitat imaging for preoperatively predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC)., Methods: Sixty-five patients were prospectively included and underwent multi-b DWI examinations. Based on the true diffusion coefficient (D t ), perfusion fraction (f), and mean kurtosis coefficient (MK), which respectively characterize cellular density, perfusion, and heterogeneity, the HCCs were divided into four habitats. The volume fraction of each habitat was quantified. The logistic regression was used to explore the risk factors from habitat fraction and clinical variables. Clinical, habitat, and nomogram models were constructed using the identified risk factors from clinical characteristics, habitat fraction, and their combination, respectively. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curves (AUCs)., Results: MVI-positive HCC exhibited a significantly higher fraction of habitat 4 (f 4 ) and a significantly lower fraction of habitat 2 (f 2 ) (p < 0.001), which were selected as risk factors. Additionally, tumor size and elevated alpha-fetoprotein (AFP) were also included as risk factors for MVI. The nomogram model demonstrated the highest diagnostic performance (AUC = 0.807), followed by the habitat model (AUC = 0.777) and the clinical model (AUC = 0.708). Decision curve analysis indicated that the nomogram model offered more net benefit in identifying MVI compared to the clinical model., Conclusions: DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively determining the MVI of HCC with high accuracy., Clinical Relevance Statement: The proposed strategy, diffusion-weighted imaging-based habitat imaging, can be applied for preoperatively and noninvasively identifying microvascular invasion in hepatocellular carcinoma, which offers potential benefits in terms of prognostic prediction and clinical management., Key Points: • This study proposed a strategy of DWI-based habitat imaging for hepatocellular carcinoma. • The habitat imaging-derived metrics can serve as diagnostic markers for identifying the microvascular invasion. • Integrating the habitat-based metric and clinical variable, a predictive nomogram was constructed and displayed high accuracy for predicting microvascular invasion., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: Varying diffusion curvature (VDC) MRI is an emerging diffusion-weighted imaging (DWI) technique that can capture non-Gaussian diffusion behavior and reflect tissue heterogeneity. However, its clinical utility has hardly been evaluated. We aimed to investigate the value of the VDC technique in noninvasively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC)., Methods: 74 patients with HCCs, including 39 MVI-positive and 35 MVI-negative HCCs were included into this prospective study. Quantitative metrics between subgroups, clinical risk factors, as well as diagnostic performance were evaluated. The power analysis was also carried out to determine the statistical power., Results: MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D 1 (0.680 ± 0.100 × 10 -3 vs 0.572 ± 0.148 × 10 -3  mm 2 /s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10 -3 vs 1.471 ± 0.322 × 10 -3  mm 2 /s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D 0 between the subgroups (p = 0.562). Tumor size (odds ratio (OR) = 1.242) and alpha-fetoprotein (AFP) (OR = 2.527) were identified as risk factors for MVI. A predictive nomogram was constructed based on D 1 , ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D 1 (AUC = 0.753) and ADC (AUC = 0.647). The diagnostic performance of the nomogram-based model was also validated by the calibration curve and decision curve., Conclusion: VDC can aid in the noninvasive and preoperative diagnosis of HCC with MVI, which may result in the clinical benefit in terms of prognostic prediction and clinical decision-making., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To construct an MRI-based habitat imaging model to help predict component percentage in combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) preoperatively, and investigate the biologic underpinnings of habitat imaging in cHCC-CCA., Methods: The study consisted of one retrospective model-building dataset and one prospective validation dataset from two hospitals. All voxels were assigned into different clusters according to the similarity of enhancement pattern by using K-means clustering method, and each habitat's volume fraction in each lesion was calculated. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select optimal predictors, and then to establish an MRI-based habitat imaging model. R-squared was calculated to evaluate performance of the prediction models. Model performance was also verified in the prospective dataset with RNA sequencing data, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was then applied to investigate the biologic underpinnings of habitat imaging., Results: A total of 129 patients were enrolled (mean age, 56.1 ± 10.4, 102 man), among which 104 patients were in the retrospective model-building set, while 25 patients in the prospective validation set. Three habitats, habitat1 (HCC-alike habitat), habitat2 (iCCA-alike habitat), and habitat3 (in-between habitat), were identified. Habitat 1's volume fraction, habitat 3's volume fraction, nonrim APHE, nonperipheral washout, and LI-RADS categorization were selected to develop an HCC percentage prediction model with R-squared of 0.611 in the model-building set and 0.541 in the validation set. Habitat 1's volume fraction was correlated with genes involved in regulation of actin cytoskeleton and Rap1 signaling pathway, which regulate cell migration and tumor metastasis., Conclusion: Preoperative prediction of HCC percentage in patients with cHCC-CCA was achieved using an MRI-based habitat imaging model, which may correlate with signaling pathways regulating cell migration and tumor metastasis., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: The diagnosis of intrahepatic cholangiocarcinoma (iCCA) is challenging in hepatitis B virus (HBV)-infected patients, due to the overlapping clinical manifestations and atypical imaging patterns compared to patients without HBV., Purpose: To investigate the preoperative imaging characteristics of iCCA in patients with HBV in comparison to those without HBV., Study Type: Retrospective., Subjects: 431 patients with histopathologically confirmed iCCA (143 HBV-positive and 288 HBV-negative patients) were retrospectively enrolled from three institutes, and patients were allocated to the training (n = 302) and validation (n = 129) cohorts from different institutes or time period; 100 matching HBV-positive hepatocellular carcinoma (HCC) patients were also enrolled., Field Strength/sequence: 1.5-T and 3-T, including T1- and T2-weighted, diffusion-weighted and dynamic gadopentetate dimeglumine-enhanced imaging., Assessment: Clinical and MRI features were analyzed and compared between HBV-positive and HBV-negative patients with iCCA, and between HBV-positive patients with iCCA and HCC., Statistical Tests: Univariate and multivariate logistic regression analyses with odds ratio (OR) to identify independent features for discriminating HBV-associated iCCA. Diagnostic model generation by incorporating independent features, and the performance for discrimination was evaluated by receiver operating characteristics with the area under the curve (AUC) and 95% confidence interval (CI). AUCs were compared by the DeLong's method. A P-value <0.05 was considered statistically significant., Results: Compared to patients without HBV, washout or degressive enhancement pattern (OR = 51.837), well-defined tumor margin (OR = 8.758) and no peritumoral bile duct dilation (OR = 4.651) were independent significant features for discriminating HBV-associated iCCAs. All these features were also the predominant MRI manifestations for HBV-associated HCC. The combined index showed an AUC of 0.798 (95% CI 0.748-0.842) in the training cohort and an AUC of 0.789 (95% CI 0.708-0.856) in the validation cohort for discrimination. The sensitivity, specificity, and accuracy were all >70%, which was superior to each single feature alone in both cohorts. [Correction added after first online publication on 29 June 2023. The Field Strength/Sequence has been updated from 5-T to 1.5-T.] DATA CONCLUSION: Preoperative MRI may help to discriminate HBV-associated iCCA., Evidence Level: 3 TECHNICAL EFFICACY STAGE: 2., (© 2023 International Society for Magnetic Resonance in Medicine.)

Objectives: To establish a non-invasive diagnostic system for intrahepatic mass-forming cholangiocarcinoma (IMCC) via decision tree analysis., Methods: Totally 1008 patients with 504 pathologically confirmed IMCCs and proportional hepatocellular carcinomas (HCC) and combined hepatocellular cholangiocarcinomas (cHCC-CC) from multi-centers were retrospectively included (internal cohort n = 700, external cohort n = 308). Univariate and multivariate logistic regression analyses were applied to evaluate the independent clinical and MRI predictors for IMCC, and the selected features were used to develop a decision tree-based diagnostic system. Diagnostic efficacy of the established system was calculated by the receiver operating characteristic curve analysis in the internal training-testing and external validation cohorts, and also in small lesions  ≤ 3 cm., Results: Multivariate analysis revealed that female, no chronic liver disease or cirrhosis, elevated carbohydrate antigen 19-9 (CA19-9) level, normal alpha-fetoprotein (AFP) level, lobulated tumor shape, progressive or persistent enhancement pattern, no enhancing tumor capsule, targetoid appearance, and liver surface retraction were independent characteristics favoring the diagnosis of IMCC over HCC or cHCC-CC (odds ratio = 3.273-25.00, p < 0.001 to p = 0.021). Among which enhancement pattern had the highest weight of 0.816. The diagnostic system incorporating significant characteristics above showed excellent performance in the internal training (area under the curve (AUC) 0.971), internal testing (AUC 0.956), and external validation (AUC 0.945) cohorts, as well as in small lesions  ≤ 3 cm (AUC 0.956)., Conclusions: In consideration of the great generalizability and clinical efficacy in multi-centers, the proposed diagnostic system may serve as a non-invasive, reliable, and easy-to-operate tool in IMCC diagnosis, providing an efficient approach to discriminate IMCC from other HCC-containing primary liver cancers., Clinical Relevance Statement: This study established a non-invasive, easy-to-operate, and explainable decision tree-based diagnostic system for intrahepatic mass-forming cholangiocarcinoma, which may provide essential information for clinical decision-making., Key Points: • Distinguishing intrahepatic mass-forming cholangiocarcinoma (IMCC) from other primary liver cancers is important for both treatment planning and outcome prediction. • The MRI-based diagnostic system showed great performance with satisfying generalization ability in the diagnosis and discrimination of IMCC. • The diagnostic system may serve as a non-invasive, easy-to-operate, and explainable tool in the diagnosis and risk stratification for IMCC., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

The effectiveness of thermal ablation for the treatment of liver tumours is limited by the risk of incomplete ablation, which can result in residual tumours. Herein, an enhancement strategy is proposed based on the controlled release of Ganoderma applanatum polysaccharide (GAP) liposome-microbubble complexes (GLMCs) via ultrasound (US)-targeted microbubble destruction (UTMD) and sublethal hyperthermic (SH) field. GLMCs were prepared by conjugating GAP liposomes onto the surface of microbubbles via biotin-avidin linkage. In vitro, UTMD promotes the cellular uptake of liposomes and leads to apoptosis of M2-like macrophages. Secretion of arginase-1 (Arg-1) and transforming growth factor-beta (TGF-β) by M2-like macrophages decreased. In vivo, restriction of tumour volume was observed in rabbit VX2 liver tumours after treatment with GLMCs via UTMD in GLMCs + SH + US group. The expression levels of CD68 and CD163, as markers of tumour-associated macrophages (TAMs) in the GLMCs + SH + US group were reduced in liver tumour tissue. Decreased Arg-1, TGF-β, Ki67, and CD31 factors related to tumour cell proliferation and angiogenesis was evident on histological analysis. In conclusion, thermal/US-triggered drug release from GLMCs suppressed rabbit VX2 liver tumour growth in the SH field by inhibiting TAMs, which represents a potential approach to improve the effectiveness of thermal ablation., Competing Interests: Declaration of Competing Interest The authors declare no competing financial interests., (Copyright © 2023. Published by Elsevier B.V.)

Purpose: Characterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the recurrence-free survival (RFS) of HCC (≤ 5 cm) with habitat imaging of HCC and its peritumoral micro-environment., Material and Methods: A total of 264 patients with HCC were included. Taking advantage of the enhancement ratio at the arterial and hepatobiliary phase of contrast-enhanced MRI, all HCCs and their peritumoral tissue of 3 mm and 4 mm were encoded with different habitats. Besides, the quantitative fraction of each habitat of HCC and peritumoral tissue were calculated. Univariable and multivariable Cox regression analysis was performed to select the prognostic factors. The nomogram-based predictor was established. Kaplan-Meier analysis was conducted to stratify the recurrence risk. Fivefold cross-validation was performed to determine the predictive performance with the concordance index (C-Index). Decision curve analysis was used to evaluate the net benefit., Results: Qualitatively, the spatial distribution of the habitats varied for different survival outcomes. Quantitatively, the fraction of habitat 3 in peritumoral tissue of 4 mm (f 3 -P 4 ) was selected as independent risk factors (OR = 89.2, 95% CI = 14.5-549.2, p < 0.001) together with other two clinical variables. Integrating both clinical variables and f 3 -P 4 , a nomogram was constructed and showed high predictive efficacy (C-Index: 0.735, 95% CI 0.617-0.854) and extra net benefit according to the decision curve. Furthermore, patients with low f 3 -P 4 or risk score given by nomogram have far longer RFS than those with high f 3 -P 4 or risk score (stratification by f 3 -P 4 : 131.9 vs 55.0 months and stratification by risk score:131.9 vs 34.1 months)., Conclusion: Habitat imaging of HCC and peritumoral microenvironment can be used for effectively and non-invasively estimating the RFS, which holds potential in guiding clinical management and decision making., (© 2023. Italian Society of Medical Radiology.)

Purpose: To investigate the significance of the predominant component of combined hepatocellular-cholangiocarcinoma (cHCC-CC) in terms of MRI manifestation and its potential prognostic value compared to hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC)., Materials and Methods: A total of 300 patients with chronic liver disease from two centers were retrospectively enrolled, including 100 surgically proven cases of cHCC-CC, HCC, and ICC each. Univariate and multivariate regression analyses were performed to identify independent predictors for distinguishing HCC-predominant cHCC-CC and ICC-predominant cHCC-CC from HCC and ICC, respectively. Diagnostic models were constructed based on the independent features. Recurrence-free survival (RFS) was estimated and compared between groups., Results: The predominant component was an independent predictor for RFS in cHCC-CC (hazard ratio = 1.957, P = 0.044). The presence of targetoid appearance (odds ratio(OR) = 10.907, P = 0.001), lack of enhancing capsule (OR = 0.072, P = 0.001) and arterial peritumoral enhancement (OR = 0.091, P = 0.003) were independent predictors suggestive of HCC-predominant cHCC-CC over HCC; their combination yielded an area under the curve of 0.756. No significant differences were observed in RFS between HCC-predominant cHCC-CC and HCC (P = 0.864). Male gender (OR = 4.049, P = 0.015), higher alpha fetoprotein (OR = 16.789, P < 0.001) and normal carbohydrate antigen 19-9 (OR = 0.343, P = 0.036) levels, presence of enhancing capsule (OR = 7.819, P < 0.001) and hemorrhage (OR = 23.526, P = 0.004), and lack of targetoid appearance (OR = 0.129, P = 0.005) and liver surface retraction (OR = 0.190, P = 0.021) were independent predictors suggestive of ICC-predominant cHCC-CC over ICC; their combination yielded an area under the curve value of 0.898. ICC-predominant cHCC-CC exhibited poorer survival with shorter RFS than ICC (P = 0.009)., Conclusion: The predominant histopathological component is closely related to the imaging manifestation of cHCC-CC; and more importantly, it plays a significant prognostic role, which may alter the RFS prognosis of cHCC-CC., (© 2023. Italian Society of Medical Radiology.)

Rationale and Objectives: To noninvasively and preoperatively identify the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) with the restricted spectrum imaging (RSI)., Materials and Methods: 62 patients were included into this prospective study and underwent the RSI examination with a 3.0-T scanner. Mono-exponential diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) and RSI-derived metrics including f 1 (fraction of restricted diffusion), f 2 (fraction of hindered diffusion), f 3 (fraction of free diffusion), and f 1 f 2 (the multiply of f 1 and f 2 ) were calculated. Univariate and multivariate logistic regression were used to select the independent risk factors. Nomogram-based model was constructed with the selected indexes. Receiver operative characteristics analysis and calibration curve were used to evaluate the diagnostic accuracy., Results: MVI-positive HCC showed significantly higher f 1 and lower ADC values (ADC: 1.549 ± 0.228 ×10 -3 vs 1.365 ± 0.239 ×10 -3 mm 2 /s, P = .003; f 1 : 0.1633 ± 0.0341 vs 0.2221 ± 0.0491, P < .001). Tumor size and f 1 were selected as independent risk factors for MVI. The nomogram-based model was then constructed with tumor size and f 1 . Nomogram-based model (area under ROC curve [AUC]= 0.856) yielded the best diagnostic accuracy followed by f 1 (AUC=0.842) and ADC (AUC=0.708). The AUC of both the f 1 and nomogram model were significantly higher than that of ADC., Conclusion: RSI-derived metrics can be utilized to noninvasively and efficiently identify the MVI of HCC. Considering the importance of MVI as a significant prognostic factor for HCC, the utilization of RSI has the potential to assist in prognostic prediction and clinical management., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Microvascular invasion (MVI) is a well-established poor prognostic factor for hepatocellular carcinoma (HCC). Preoperative prediction of MVI is important for both therapeutic and prognostic purposes, but noninvasive methods are lacking., Purpose: To develop an MR elastography (MRE)-based nomogram for the preoperative prediction of MVI in HCC., Study Type: Prospective., Subjects: A total of 111 patients with surgically resected single HCC (52 MVI-positive and 59 MVI-negative), randomly allocated to training and validation cohorts (7:3 ratio)., Field Strength/sequence: 2D-MRE and conventional sequences (T1-weighted in-phase and opposed phase gradient echo, T2-weighted fast spin echo, diffusion-weighted single-shot spin echo echo-planar, and dynamic contrast-enhanced T1-weighted gradient echo) at 3.0 T., Assessment: MRE-stiffness and conventional qualitative and quantitative MRI features were evaluated and compared between MVI-positive and MVI-negative HCCs., Statistical Tests: Univariable and multivariable logistic regression analyses were applied to identify potential predictors for MVI, and a nomogram was constructed according to the predictive model. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance. Harrell's C-index evaluated the discrimination performance of the nomogram, calibration curves analyzed its diagnostic performance and decision curve analysis determined its clinical usefulness. A P value <0.05 was considered statistically significant., Results: Tumor stiffness >6.284 kPa (odds ratio [OR] = 24.38) and the presence of arterial peritumoral enhancement (OR = 6.36) were independent variables associated with MVI. The areas under the ROC curves for tumor stiffness were 0.81 (95% confidence interval [CI]: 0.70, 0.89) and 0.77 (95% CI: 0.60, 0.90) in the training and validation cohorts, respectively. When both predictive variables were integrated, the best nomogram performance was achieved with C-indices of 0.88 (95% CI: 0.78, 0.94) and 0.87 (95% CI: 0.71, 0.96) in the two cohorts, fitting well in calibration curves. The decision curve exhibited optimal net benefit with a wide range of threshold probabilities for the nomogram., Data Conclusion: An MRE-based nomogram may be a potential noninvasive imaging biomarker for predicting MVI of HCC preoperatively., Evidence Level: 2., Technical Efficacy: Stage 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Purpose: To assess the value of pre-treatment MRI in predicting treatment response to combined targeted immunotherapy in advanced hepatocellular carcinoma (HCC)., Methods: Totally 35 HCC participants who underwent pre-treatment contrast-enhanced MRI and received combined tyrosine kinase inhibitor (TKI) and anti-PD-1 antibody treatment were enrolled. Univariable and multivariable logistic regression analyses were carried out for comparing clinical and MRI characteristics between patients with therapeutic response and those without. A predictive model based on MRI data and the corresponding nomogram were developed using data generated by multivariate analysis, and the diagnostic performance was evaluated. A cutoff for the combined index was measured by receiver operating characteristic curve analysis, and progression-free survival (PFS) rates were compared between cases with high and low combined index values., Results: Fifteen (42.86%) cases achieved overall response during treatment. Multivariable analysis revealed that homogeneous signal (odds ratio [OR] = 13.51, P = 0.010) and no arterial peritumoral enhancement (APE; OR = 10.29, P = 0.024) independently predicted treatment response. The combined model including both significant MRI parameters showed a satisfactory predictive performance with the largest area under the curve of 0.837 (95%CI 0.673-0.939), and both sensitivity and specificity of 80.0%. HCCs with high-combined index had higher PFS rate compared with those showing a low value (P = 0.034)., Conclusion: The combination of pre-treatment MRI features of homogeneous signal and no APE could be used for predicting treatment response to combined targeted immunotherapy in advanced HCC., Competing Interests: Declaration of Competing Interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Purpose: To explore the potential value of diffusion kurtosis imaging (DKI) for identification of cytokeratin 19 (CK19) status of HCCs., Methods: This study was approved by the local institute review board and written informed consent was obtained. 73 patients with pathologically confirmed HCCs were included in this prospective study. All the diffusion-weighted (DW) images were acquired using a 3.0-T MR scanner with 4 b-values (0, 800, 1500 and 2000 s/mm 2 ). The mean diffusion value (MD) and mean kurtosis coefficient (MK) from DKI, apparent diffusion coefficient (ADC) from DW imaging (b = 0, 500 s/mm 2 ), and tumor-to-liver signal intensity ratios on ADC map (SIR ADC ) and DW images with b-value of 500 s/mm 2 (SIR b500 ) were calculated and compared between CK19-positive (n = 23) and CK19-negative (n = 50) HCC groups. Univariate and multivariate logistic regression analyses were used to identify risk factors for the positive expression of CK19., Results: Increased a-fetoprotein level (p = 0.021) and SIR b500 (p = 0.006) and decreased ADC (p = 0.021) and MD (p < 0.001) were significantly correlated with CK19-positive HCCs at univariate analysis. Decreased MD value (odds ratio: 0.042, p = 0.002) and a-fetoprotein level (odds ratio: 5.139, p = 0.015) were the independent risk factors for CK19-positive HCCs at multivariate analysis. The area under the curve of MD value by receiver operating characteristic analysis was 0.823 with a sensitivity of 86.96% and a specificity of 76% for the prediction of CK19-positive HCCs., Conclusion: The decreased MD value derived from DKI is potential quantitative biomarker for predicting CK19-positive HCCs., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To assess the clinical potential of a set of new diffusion parameters (D, β, and μ) derived from fractional order calculus (FROC) diffusion model in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC)., Materials and Methods: Between January 2019 to November 2020, a total of 63 patients with HCC were enrolled in this study. Diffusion-weighted images were acquired by using ten b-values (0-2000 s/mm 2 ). The FROC model parameters including diffusion coefficient (D), fractional order parameter (β), a microstructural quantity (μ) together with a conventional apparent diffusion coefficient (ADC) were calculated. Intraclass coefficients were calculated for assessing the agreement of parameters quantified by two radiologists. The differences of these values between the MVI-positive and MVI-negative HCC groups were compared by using independent sample t-test or the Mann-Whitney U test. Then the parameters showing significant differences between subgroups, including the β and D, were integrated to develop a comprehensive predictive model via binary logistic regression. The diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis., Results: Among all the studied diffusion parameters, significant differences were found in D, β, and ADC between the MVI-positive and MVI-negative groups. MVI-positive HCCs showed significantly higher β values (0.65 ± 0.17 vs. 0.51 ± 0.13, P = 0.001), along with lower D values (0.84 ± 0.11 μm 2 /ms vs. 1.03 ± 0.13 μm 2 /ms, P < 0.001) and lower ADC values (1.38 ± 0.46 μm 2 /ms vs. 2.09 ± 0.70 μm 2 /ms, P < 0.001) than those of MVI-negative HCCs. According to the ROC analysis, the combination of D and β demonstrated the largest area under the ROC curve (0.920) compared with individual parameters (D: 0.912; β: 0.733; and ADC: 0.831) for differentiating MVI-positive from MVI-negative HCCs., Conclusions: The FROC parameters can be used as noninvasive quantitative imaging markers for preoperatively predicting the MVI status of HCCs., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Rationale and Objectives: To investigate the impact of preoperative gadoxetate disodium (EOB) MRI-based radiomics on predicting glypican 3 (GPC3)-positive expression and the relevant recurrence-free survival (RFS) of HCC ≤ 5 cm., Materials and Methods: Between January 2014 and October 2018, 259 patients with solitary HCC ≤ 5 cm who underwent hepatectomy and preoperative EOB-MRI were retrieved. Multivariate logistic regression was implemented to identify independent predictors for GPC3. By combining five feature selection strategies and three classifiers, 15 GPC3-oriented radiomics models could be constructed, the best of which with independent clinicoradiologic predictors was integrated into the comprehensive nomogram., Results: GPC3 was an independent risk factor of postoperative recrudescence for HCC. Alpha-fetoprotein >20 ng/mL, homogenous T2 signal and hypointensity on hepatobiliary phase were independently related to GPC3-positive expression in the clinicoradiologic model. With 10 features selected by support vector machines-recursive feature elimination, logistic regression-based classifier achieved the best performance among 15 radiomics models. After five-fold cross-validation, our comprehensive nomogram acquired better average area under receiver operating characteristic curves (training and validation cohorts: 0.931 vs. 0.943) than the clinicoradiologic algorithm (0.738 vs. 0.739) and the optimal radiomics model (0.943 vs. 0.931). Net reclassification indexes further demonstrated the superiority of GPC3 nomogram over clinicoradiologic and radiomics algorithms (46.54%, p < 0.001; 7.84%, p = 0.207). Meanwhile, higher radiomics score significantly shortened the median RFS (from >77.9 to 48.2 months, p = 0.044), which was analogue to that of the histological GPC3-positive phenotype (from >73.9 to 43.2 months, p < 0.001)., Conclusions: Preoperative EOB-MRI radiomics-based nomogram satisfactorily distinguished GPC3 status and outcomes of solitary HCC ≤ 5 cm., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: Combined immune and anti-angiogenic treatment has shown promising results for unresectable hepatocellular carcinoma (HCC), but with a high risk of early progression. In this study, we aimed to investigate whether pre-treatment magnetic resonance imaging (MRI) features and MRI-based nomogram could predict the risk of disease progression of unresectable HCC after first-line lenvatinib/anti-PD-1 antibody therapy., Materials and Methods: Thirty-seven HCC participants with qualified pre-treatment contrast-enhanced MRI were enrolled. All patients received combined lenvatinib and anti-PD-1 antibody treatment. Progression free survival rate was analyzed using the Kaplan-Meier method. Potential clinical-radiological risk factors for progression were analyzed using the log-rank tests and Cox regression model. The performance of MRI-based nomogram was evaluated based on C-index, calibration, and decision curve analyses., Results: The 6-month and 12-month cumulative progression free survival rates were 59.5% (95% confidence interval (CI), 43.6%-75.4%) and 48.0% (95% CI, 31.7%-64.3%). On multivariate analysis, no or incomplete tumor capsule (hazard ratio (HR) = 15.215 [95% CI 2.707-85.529], p = 0.002), heterogeneous signal on T2-weighted imaging (HR = 28.179 [95% CI 2.437-325.838]; p = 0.008) and arterial contrast-to-noise ratio ≤95.45 (HR = 5.113 [95% CI 1.538-17.00]; p = 0.008) were independent risk factors for disease progression. Satisfactory predictive performance of the nomogram incorporating the three independent imaging features was obtained with a C-index value of 0.880 (95% CI 0.824-0.937), and the combined nomogram had more favorable clinical prediction performance than any single feature., Conclusion: MRI features can be considered effective predictors of disease progression for unresectable HCC with first-line lenvatinib plus anti-PD-1 antibody therapy, and the combined MRI-based nomogram achieved a superior prognostic model, which may help to identify appropriate candidates for the therapy., (Copyright © 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: To evaluate Cytokeratin 19 expression in hepatocellular carcinoma (HCC) with diffusion parameters derived from mono-exponential model (MEM), stretched exponential model (SEM), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM) imaging, and fractional order calculus (FROC) model and compare their predictive performance., Method: 61 patients with pathologically confirmed primary HCC were included in this prospective study. All the DWIs were acquired using a 3.0 T MR scanner with 10b-values (0-2000 s/mm 2 ). The apparent diffusion coefficient (ADC), distributed diffusion coefficient (DDC), heterogeneity index (α), apparent kurtosis coefficient (AK), apparent diffusion coefficient (AD), pseudo-diffusion coefficient (D p ), true diffusion coefficient (D t ), perfusion fraction (f), diffusion coefficient (D), fractional order parameter (β), and a microstructural quantity (μ) were calculated. The diagnostic efficacy of various diffusion parameters for predicting CK19 expression of HCC was compared., Results: ADC, DDC, D t , D p , AD, and D were significantly lower in CK19-positive HCCs than in CK19-negative HCCs (P ≤ 0.05). β was significantly higher in CK19-positive group (P = 0.001). AD (AUC = 0.845) had the greatest AUC values in differentiating CK19-positive and CK19-negative HCC with individual parameters. The combination of β, AD, and D p generated the highest area under the ROC curve (AUC = 0.881) compared with individual parameters., Conclusions: ADC, DDC, D t , D p , D, and β may serve as noninvasive and quantitative imaging markers and holds great potential in accurately accessing CK19 status of HCC. More importantly, the combination of different diffusion parameters yielded better diagnostic performance., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The tumor microenvironment (TME) was usually studied in tumor tissue and in relation to only tumor progression, with little involved in occurrence, recurrence and metastasis of tumor. Thus, a new concept "peritumor microenvironment (PME)" was proposed in the proteomic characterization of peritumor liver tissues in human hepatocellular carcinoma (HCC). The PME for occurrence (PME-O) and progression (PME-P) were almost totally different at proteome composition and function. Proteins for occurrence and progression rarely overlapped and crossed. Immunity played a central role in PME-O, whereas inflammation, angiogenesis and metabolism were critical in PME-P. Proteome profiling identified three PME subtypes with different features of HCC. Thymidine phosphorylase (TYMP) was validated as an antiangiogenic target in an orthotopic HCC mouse model. Overall, the proteomic characterization of the PME revealed that the entire processes of HCC occurrence and progression differ substantially. These findings could enable advances in cancer biology, diagnostics and therapeutics., (© 2022. The Author(s).)

The effect of the cytochrome P450 oxidoreductase (POR) rs10954732 (G > A) polymorphism on hepatocellular carcinoma (HCC) susceptibility is unknown. Here we found that A allele carriers showed a 69% decrease in susceptibility to HCC with overall survival (OS) prolonged to 199%, accompanied by lower activity for cytochrome P450 2E1. A total of 222 differentially expressed proteins were mainly enriched in neutrophil and T cell activation and involved in the immune and inflammatory responses, constituting the altered immune tumor microenvironment related with A allele by proteomics analysis. Hepsin (HPN) showed significant down-regulation in HCC and up-regulation in A allele carriers. A lower HPN level was associated with increased susceptibility to HCC and a worse prognosis. Moreover, HPN is a potential independent prognostic biomarker for HCC and is strongly associated with clinicopathological features, tumor-infiltrating status of immune cells both in our discovery cohort and database surveys. Our findings provide a new potential mechanism by which HPN may play an important role in the susceptibility of rs10954732 A allele carriers to HCC and their prognosis through tumor immune infiltration, thus offering potential insights for future studies on tumor immunotherapy., (© 2022. The Author(s).)

Although there are many studies on the relationship between genetic polymorphisms and the incidence of diseases, mechanisms are rarely known. We report the mechanism by which signal transducer and activator of transcription 4 (stat4) rs7574865 promotes the occurrence and progression of hepatocellular carcinoma (HCC). We found that the GG genotype at stat4 rs7574865 was a risk genotype, and STAT4 levels in serum and peritumoral tissue from HCC patients with the GG genotype were significantly higher than those found in TT or TG carriers. Furthermore, HCC patients with the GG genotype or elevated STAT4 levels had poor prognoses. In vitro experiments demonstrated that STAT4 silencing promoted apoptosis and inhibited the invasion and migration of HepG2 and L02 cells. Proteomic analysis of HCC peritumors identified 273 proteins related to STAT4, of which CYP2E1 activity and FGL2 content exhibited the highest positive correlation. The relationship between CYP2E1 and FGL2 was also confirmed in cyp2e1 -/- mice and in CYP2E1 inhibitor-treated mice. In conclusion, this study elucidates the mechanism by which the stat4 rs7574865 polymorphism promotes the occurrence and progression of HCC via the Stat4/CYP2E1/FGL2 pathway., (© 2022. The Author(s).)

Purpose: No previous researches have extracted radiomics features from susceptibility weighted imaging (SWI) for biomedical applications. This research aimed to explore the correlation between histopathology of hepatocellular carcinoma (HCC) and radiomics features extracted from SWI., Methods: A total of 53 patients were ultimately enrolled into this retrospective study with MR examinations undertaken at a 3T scanner. About 107 radiomics features were extracted from SWI images of each patient. Then, the Spearman correlation test was performed to evaluate the correlation between the SWI-derived radiomics features and histopathologic indexes including histopathologic grade, microvascular invasion (MVI) as well as the expression status of cytokeratin 7 (CK-7), cytokeratin 19 (CK-19) and Glypican-3 (GPC-3). With SWI-derived radiomics features utilized as independent variables, four logistic regression-based diagnostic models were established for diagnosing patients with positive CK-7, CK-19, GPC-3 and high histopathologic grade, respectively. Then, receiver operating characteristic analysis was performed to evaluate the diagnostic performance., Results: A total of 11, 32, 18 and one SWI-derived radiomics features were significantly correlated with histopathologic grade, the expression of CK-7, the expression of CK-19 and the expression of GPC-3 (P < 0.05), respectively. None of the SWI-derived radiomics features was correlated with MVI status. The areas under the curve were 0.905, 0.837, 0.800 and 0.760 for diagnosing patients with positive CK-19, positive CK-7, high histopathologic grade and positive GPC-3., Conclusion: Extracting the radiomics features from SWI images was feasible to evaluate multiple histopathologic indexes of HCC.

Surgery is the mainstay for treating hepatocellular carcinoma (HCC). However, it is a great challenge for surgeons to identify HCC in its early developmental stage. The diagnostic sensitivity for a tiny HCC with a diameter less than 1.0 cm is usually as low as 10-33% for computed tomography (CT) and 29-43% for magnetic resonance imaging (MRI). Although MRI is the preferred imaging modality for detecting HCC, with its unparalleled spatial resolution for soft tissue, the commercially available contrast agent, such as Gd 3+ -DTPA, cannot accurately define HCC because of its short circulation lifetime and lack of tumor-targeting specificity. Endoglin (CD105), a type I membrane glycoprotein, is highly expressed both in HCC cells and in the endothelial cells of neovasculature, which are abundant at the tumor periphery. In this work, a novel single-stranded DNA oligonucleotide-based aptamer was screened by systematic evolution of ligands in an exponential enrichment assay and showed a high binding affinity ( K D = 98 pmol/L) to endoglin. Conjugating the aptamers and imaging reporters on a G5 dendrimer created an HCC-targeting nanoprobe that allowed the successful visualization of orthotopic HCC xenografts with diameters as small as 1-4 mm. Significantly, the invasive tumor margin was clearly delineated, with a tumor to normal ratio of 2.7 by near-infrared (NIR) fluorescence imaging and 2.1 by T 1 -weighted MRI. This multimodal nanoprobe holds promise not only for noninvasively defining tiny HCC by preoperative MRI but also for guiding tumor excision via intraoperative NIR fluorescence imaging, which will probably gain benefit for the patient's therapeutic response and improve the survival rate.

Background: Percutaneous ablation has quickly arisen as one of the important alternative treatments for hepatocellular carcinoma (HCC). We aimed to compare the therapeutic effects of radiofrequency ablation (RFA) and other ablative techniques on HCCs., Methods: Databases were searched to identify literature on complete tumor ablation (CTA), overall survival (OS), local tumor recurrence (LTR), and complications of RFA in the treatment of HCC, compared with those of microwave ablation (MWA), percutaneous ethanol injection (PEI), PEI plus RFA, cryoablation (CRA), laser ablation (LSA), and high-intensity focused ultrasound. Randomized controlled trials and high-quality cohort studies were included in the assessment., Results: The effects of MWA and CRA appeared to be similar to those of RFA, but lower rates of LTR and higher rates of CTA in large tumors compared with RFA were reported (P < 0.05). CTA rates were lower in patients treated with PEI (odds ratio [OR] 0.16, 95% confidence interval [CI] 0.06-0.42), and higher in those treated with PEI plus RFA (OR 2.28, 95% CI 1.19-3.60), with an increased incidence of fever (P < 0.05). LSA resulted in lower CTA rates (OR 0.32, 95% CI 0.13-0.81) and OS (hazard ratio 1.47, 95% CI 1.01-2.15), with a lower incidence of complications., Conclusions: Compared with RFA, identical effects were found in MWA and CRA groups. Fewer complications were observed in PEI and LSA group. PEI plus RFA appeared more effective, with a higher rate of complications. Well-designed randomized controlled trials are further needed to confirm above results.