Liver stiffness measured by MR elastography is a predictor of early HCC recurrence after treatment

A Blood Hepatocellular Carcinoma Signature Recognizes Very Small Tumor Nodules with Metastatic Traits

Background and Aims

Hepatocellular carcinoma (HCC) cases with small nodules are commonly treated with radiofrequency ablation (RFA), but the recurrence rate remains high. This study aimed to establish a blood signature for identifying HCC with metastatic traits pre-RFA.

Methods

Data from HCC patients treated between 2010 and 2017 were retrospectively collected. A blood signature for metastatic HCC was established based on blood levels of alpha-fetoprotein and des-γ-carboxy-prothrombin, cell-free DNA (cfDNA) mutations, and methylation changes in target genes in frozen-stored plasma samples that were collected before RFA performance. The HCC blood signature was validated in patients prospectively enrolled in 2021.

Results

Of 251 HCC patients in the retrospective study, 33.9% experienced recurrence within 1 year post-RFA. The HCC blood signature identified from these patients included des-γ-carboxy-prothrombin ≥40 mAU/mL with cfDNA mutation score, where cfDNA mutations occurred in the genes of TP53, CTNNB1, and TERT promoter. This signature effectively predicted 1-year post-RFA recurrence of HCC with 92% specificity and 91% sensitivity in the retrospective dataset, and with 87% specificity and 76% sensitivity in the prospective dataset (n=32 patients). Among 14 cases in the prospective study with biopsy tissues available, positivity for the HCC blood signature was associated with a higher HCC tissue score and shorter distance between HCC cells and microvasculature.

Conclusions

This study established an HCC blood signature in pre-RFA blood that potentially reflects HCC with metastatic traits and may be valuable for predicting the disease's early recurrence post-RFA.

Magnetic resonance elastography as a preoperative assessment for predicting intrahepatic recurrence in patients with hepatocellular carcinoma

PURPOSE

Magnetic resonance elastography (MRE) is a noninvasive tool for diagnosing hepatic fibrosis with high accuracy. We investigated the preoperative clinical and imaging predictors of intrahepatic recurrence after curative resection of hepatocellular carcinoma (HCC), and evaluated MRE as a predictor of intrahepatic recurrence.

METHODS

We retrospectively evaluated 80 patients who underwent preoperative contrast-enhanced magnetic resonance imaging (MRI) with two-dimensional MRE and curative resection for treatment-naïve HCC between May 2019 and December 2021. Liver stiffness (LS) was measured on the elastograms, and the optimal cutoff of LS for predicting intrahepatic recurrence was obtained using receiver operating characteristic (ROC) analysis. An LS above this cutoff was defined as MRE-recurrence. Preoperative imaging features of the tumor were assessed on MRI, including features in the Liver Imaging Reporting and Data System and microvascular invasion (MVI). Recurrence-free survival (RFS) rates were estimated using the Kaplan-Meier method, and differences were compared using the log-rank test. Using a Cox proportional hazards model, we conducted a multivariable analysis to investigate the factors affecting recurrence-free survival.

RESULTS

During a median follow-up period of 32 months (range, 4-52 months), thirteen patients (16.3%) developed intrahepatic recurrence. ROC analysis determined an LS cutoff of ≥4.35 kPa to define MRE-recurrence. The 4-year RFS rate was significantly higher in patients without MRE-recurrence than in those with MRE-recurrence (93.4% vs. 48.9%; p = 0.001). In multivariable analysis, MRE-recurrence (Hazard ratio [HR], 5.9; 95% confidence interval [CI], 1.5-23.1) and MVI (HR, 3.4; 95% CI, 1.0-11.3) were independent predictors of intrahepatic recurrence.

CONCLUSIONS

Patients without MRE-recurrence had significantly higher RFS rates than those with MRE-recurrence. MRE-recurrence and MVI were independent predictors of intrahepatic recurrence in patients after curative resection for HCC.

Interpretation, Reporting, and Clinical Applications of Liver MR Elastography

Chronic liver disease is highly prevalent and often leads to fibrosis or cirrhosis and complications such as liver failure and hepatocellular carcinoma. The diagnosis and staging of liver fibrosis is crucial to determine management and mitigate complications. Liver biopsy for histologic assessment has limitations such as sampling bias and high interreader variability that reduce precision, which is particularly challenging in longitudinal monitoring. MR elastography (MRE) is considered the most accurate noninvasive technique for diagnosing and staging liver fibrosis. In MRE, low-frequency vibrations are applied to the abdomen, and the propagation of shear waves through the liver is analyzed to measure liver stiffness, a biomarker for the detection and staging of liver fibrosis. As MRE has become more widely used in clinical care and research, different contexts of use have emerged. This review focuses on the latest developments in the use of MRE for the assessment of liver fibrosis; provides guidance for image acquisition and interpretation; summarizes diagnostic performance, along with thresholds for diagnosis and staging of liver fibrosis; discusses current and emerging clinical applications; and describes the latest technical developments.

From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring

In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.

Liver Cancer Vascularity Driven by Extracellular Matrix Stiffness: Implications for Imaging Research

BACKGROUND

Extracellular matrix stiffness represents a barrier to effective local and systemic drug delivery. Increasing stiffness disrupts newly formed vessel architecture and integrity, leading to tumor-like vasculature. The resulting vascular phenotypes are manifested through different cross-sectional imaging features. Contrast-enhanced studies can help elucidate the interplay between liver tumor stiffness and different vascular phenotypes.

PURPOSE

This study aims to correlate extracellular matrix stiffness, dynamic contrast-enhanced computed tomography, and dynamic contrast-enhancement ultrasound imaging features of 2 rat hepatocellular carcinoma tumor models.

METHODS AND MATERIALS

Buffalo-McA-RH7777 and Sprague Dawley (SD)-N1S1 tumor models were used to evaluate tumor stiffness by 2-dimensional shear wave elastography, along with tumor perfusion by dynamic contrast-enhanced ultrasonography and contrast-enhanced computed tomography. Atomic force microscopy was used to calculate tumor stiffness at a submicron scale. Computer-aided image analyses were performed to evaluate tumor necrosis, as well as the percentage, distribution, and thickness of CD34+ blood vessels.

RESULTS

Distinct tissue signatures between models were observed according to the distribution of the stiffness values by 2-dimensional shear wave elastography and atomic force microscopy ( P < 0.05). Higher stiffness values were attributed to SD-N1S1 tumors, also associated with a scant microvascular network ( P ≤ 0.001). Opposite results were observed in the Buffalo-McA-RH7777 model, exhibiting lower stiffness values and richer tumor vasculature with predominantly peripheral distribution ( P = 0.03). Consistent with these findings, tumor enhancement was significantly greater in the Buffalo-McA-RH7777 tumor model than in the SD-N1S1 on both dynamic contrast-enhanced ultrasonography and contrast-enhanced computed tomography ( P < 0.005). A statistically significant positive correlation was observed between tumor perfusion on dynamic contrast-enhanced ultrasonography and contrast-enhanced computed tomography in terms of the total area under the curve and % microvessel tumor coverage ( P < 0.05).

CONCLUSIONS

The stiffness signatures translated into different tumor vascular phenotypes. Two-dimensional shear wave elastography and dynamic contrast-enhanced ultrasonography adequately depicted different stromal patterns, which resulted in unique imaging perfusion parameters with significantly greater contrast enhancement observed in softer tumors.

Noninvasive Fibrosis Assessment in Chronic Hepatitis C Infection: An Update

Liver biopsy is historically the gold standard for liver fibrosis assessment of chronic hepatitis C patients. However, with the introduction and validation of noninvasive tests (NITs) to evaluate advanced fibrosis, and the direct-acting antiviral agents for treatment of chronic hepatitis C virus (HCV), the role of NITs have become even more complex. There is now need for longitudinal monitoring and elucidation of cutoff values for prediction of liver-related complication after sustained virological response. The aim of this report is to provide a critical overview of the various NITs available for the assessment of liver fibrosis in HCV patients.

Stiffness on shear wave elastography as a potential microenvironment biomarker for predicting tumor recurrence in HBV-related hepatocellular carcinoma

BACKGROUND

To explore the pathologic basis and prognostic value of tumor and liver stiffness measured pre-operatively by two-dimensional shear wave elastography (2D-SWE) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients who undergo hepatic resection.

METHODS

A total of 191 HBV-infected patients with solitary resectable HCC were prospectively enrolled. The stiffness of intratumoral tissue, peritumoral tissue, adjacent liver tissue, and distant liver tissue was evaluated by 2D-SWE. The correlations between stiffness and pathological characteristics were analyzed in 114 patients. The predictive value of stiffness for recurrence-free survival (RFS) was evaluated, and Cutoff Finder was used for determining optimal cut-off stiffness values. Cox proportional hazards analysis was used to identify independent predictors of RFS.

RESULTS

Pathologically, intratumoral stiffness was associated with stroma proportion and microvascular invasion (MVI) while peritumoral stiffness was associated with tumor size, capsule, and MVI. Adjacent liver stiffness was correlated with capsule and liver fibrosis stage while distant liver stiffness was correlated with liver fibrosis stage. Peritumoral stiffness, adjacent liver stiffness, and distant liver stiffness were all correlated to RFS (all p < 0.05). Higher peritumoral stiffness (> 49.4 kPa) (HR = 1.822, p = 0.023) and higher adjacent liver stiffness (> 24.1 kPa) (HR = 1.792, p = 0.048) were significant independent predictors of worse RFS, along with tumor size and MVI. The nomogram based on these variables showed a C-index of 0.77 for RFS prediction.

CONCLUSIONS

Stiffness measured by 2D-SWE could be a tumor microenvironment and tumor invasiveness biomarker. Peritumoral stiffness and adjacent liver stiffness showed important values in predicting tumor recurrence after curative resection in HBV-related HCC.

CLINICAL RELEVANCE STATEMENT

Tumor and liver stiffness measured by two-dimensional shear wave elastography serve as imaging biomarkers for predicting hepatocellular carcinoma recurrence, reflecting biological behavior and tumor microenvironment.

KEY POINTS

• Stiffness measured by two-dimensional shear wave elastography is a useful biomarker of tumor microenvironment and invasiveness. • Higher stiffness indicated more aggressive behavior of hepatocellular carcinoma. • The study showed the prognostic value of peritumoral stiffness and adjacent liver stiffness for recurrence-free survival. • The nomogram integrating peritumoral stiffness, adjacent liver stiffness, tumor size, and microvascular invasion showed a C-index of 0.77.

MR Elastography in Cancer

ABSTRACT

The mechanical traits of cancer include abnormally high solid stress as well as drastic and spatially heterogeneous changes in intrinsic mechanical tissue properties. Whereas solid stress elicits mechanosensory signals promoting tumor progression, mechanical heterogeneity is conducive to cell unjamming and metastatic spread. This reductionist view of tumorigenesis and malignant transformation provides a generalized framework for understanding the physical principles of tumor aggressiveness and harnessing them as novel in vivo imaging markers. Magnetic resonance elastography is an emerging imaging technology for depicting the viscoelastic properties of biological soft tissues and clinically characterizing tumors in terms of their biomechanical properties. This review article presents recent technical developments, basic results, and clinical applications of magnetic resonance elastography in patients with malignant tumors.

Perioperative predictors of outcome of hepatectomy for HBV-related hepatocellular carcinoma

Hepatitis B virus (HBV) is identified as a major risk factor for hepatocellular carcinoma (HCC), resulting in so-called hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC). Hepatectomy for HCC is acknowledged as an efficient treatment strategy, especially for early HCC. Furthermore, patients with advanced HCC can still obtain survival benefits through surgical treatment combined with neoadjuvant therapy, adjuvant therapy, transcatheter arterial chemoembolization, and radiofrequency ablation. Therefore, preoperative and postoperative predictors of HBV-related HCC have crucial indicative functions for the follow-up treatment of patients with feasible hepatectomy. This review covers a variety of research results on preoperative and postoperative predictors of hepatectomy for HBV-related HCC over the past decade and in previous landmark studies. The relevant contents of Hepatitis C virus-related HCC, non-HBV non-HCV HCC, and the artificial intelligence application in this field are briefly addressed in the extended content. Through the integration of this review, a large number of preoperative and postoperative factors can predict the prognosis of HBV-related HCC, while most of the predictors have no standardized thresholds. According to the characteristics, detection methods, and application of predictors, the predictors can be divided into the following categories: 1. serological and hematological predictors, 2. genetic, pathological predictors, 3. imaging predictors, 4. other predictors, 5. analysis models and indexes. Similar results appear in HCV-related HCC, non-HBV non-HCV HCC. Predictions based on AI and big biological data are actively being applied. A reasonable prediction model should be established based on the economic, health, and other levels in specific countries and regions.

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy

OBJECTIVES

The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence.

METHODS

In this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction.

RESULTS

MVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram.

CONCLUSION

The proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy.

KEY POINTS

• Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.

Can Liver Ultrasound Elastography Predict the Risk of Hepatocellular Carcinoma Recurrence After Radiofrequency Ablation? A Systematic Review and Meta-Analysis

PURPOSE

The role of liver stiffness (LS) on ultrasound elastography in the prediction of hepatocellular carcinoma (HCC) recurrence after treatment with radiofrequency ablation (RFA) is still unclear. Our aim was to perform a systematic review and meta-analysis to assess whether LS can predict the recurrence of HCC after RFA.

MATERIALS AND METHODS

Medline via PubMed, Embase, Scopus, and Cochrane Library databases, and abstracts of international conference proceedings were searched up to June 30, 2020. Cohort studies were included if they assessed the association between LS values measured by ultrasound elastography before RFA and HCC recurrence.

RESULTS

9 studies including 1373 patients with HCC treated by RFA, 643 of whom developed HCC recurrence, were identified. The mean value of LS before RFA was significantly higher in patients who developed HCC recurrence than in those who did not (weighted mean difference=11.98 kPa, 95%CI: 7.60-16.35, I2=63.8%). There was a significant positive association between LS value and HCC recurrence both at univariate (unadjusted HR=1.03, 95%CI: 1.00-1.07, I2=72.7%) and multivariate analysis (adjusted HR=1.03, 95%CI: 1.02-1.04, I2=0). Patients with LS value ≥13-14 kPa or >1.5 m/s have a higher risk of both HCC recurrence (unadjusted HR=2.18, 95%CI: 1.46-3.25, I2=49.7%; adjusted HR=2.41, 95%CI: 1.53-3.79, I2=0) and overall mortality (adjusted HR=4.38; 95%CI: 2.33-8.25, I2=0) in comparison with those with LS below these cutoffs.

CONCLUSION

Liver ultrasound elastography appears to be a reliable tool to predict HCC recurrence and overall survival after RFA. This technique may be useful for the management of patients with HCC treated by RFA.

Hepatocellular carcinoma: State of the art diagnostic imaging

Primary liver cancer is the fourth most common malignancy worldwide, with hepatocellular carcinoma (HCC) comprising up to 90% of cases. Imaging is a staple for surveillance and diagnostic criteria for HCC in current guidelines. Because early diagnosis can impact treatment approaches, utilizing new imaging methods and protocols to aid in differentiation and tumor grading provides a unique opportunity to drastically impact patient prognosis. Within this review manuscript, we provide an overview of imaging modalities used to screen and evaluate HCC. We also briefly discuss emerging uses of new imaging techniques that offer the potential for improving current paradigms for HCC characterization, management, and treatment monitoring.

Clinical application of Magnetic resonance elastography in hepatocellular carcinoma: from diagnosis to prognosis

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and a major public health problem worldwide. Liver fibrosis is closely correlated with liver functional reserve and the risk of HCC development. Meanwhile, malignant tumors generally have high cellularity compared to benign tumors, which results in increased stiffness. Magnetic resonance elastography (MRE) has emerged as a new non-invasive technique for assessing tissue stiffness with excellent diagnostic accuracy, not only for assessing liver fibrosis but also for measuring tumor stiffness. Recent studies provide new evidence that MRE may play an important role in the management of patients with HCC and show several novel clinical applications, such as predicting the development of HCC, differentiating between benign/malignant liver lesions (FLL) and HCC pathological grades, assessing treatment response, and predicting recurrence after treatment, although some findings are controversial. Therefore, we conducted this review to summarize these novel applications of MRE in HCC patients and also discuss their limitations and future advancement.

Resectable and transplantable hepatocellular carcinoma: Integration of liver stiffness assessment in the decision-making algorithm

BACKGROUND

Liver resection is a curative treatment for hepatocellular carcinoma (HCC) and an alternative to liver transplantation (LT). However, post-liver resection recurrence rates remain high. This study aimed to determine whether liver stiffness measurement (LSM) correlated with recurrence and to propose a method for predicting HCC recurrence exclusively using pre-liver resection criteria.

METHODS

This retrospective monocentric study included patients who had undergone LR liver resection for HCC between 2015 and 2018 and who had (1) preoperative alpha-fetoprotein scores indicating initial transplant viability and (2) available preoperative LSM data. We developed a predictive score for recurrence over time using Cox univariate regression and multivariate analysis with a combination plot before selecting the optimal thresholds (receiver operating characteristic curves + Youden test).

RESULTS

Sixty-six patients were included. After an average follow-up of 40 months, the recurrence rate was 45% (n = 30). Three-year overall survival was 88%. Four preoperative variables significantly impacted the time to recurrence: age ≥70 years, LSM ≥11 kPa, international normalized ratio (INR) ≥1.2, and maximum HCC diameter ≥3 cm. By assigning 1 point per positive item, patients with a score <2 (n = 22) demonstrated greater mean overall survival (69.7 vs 54.8 months, P = .02) and disease-free survival (52.2 vs 34.7 months, P = .02) than those with a score ≥2. Patients experiencing early recurrence (<1 year) presented a significantly higher preoperative LSM (P = .06).

CONCLUSION

We identified a simple preoperative score predictive of early hepatocellular carcinoma recurrence after liver resection, highlighting the role of liver stiffness. This score could help physicians select patients and make decisions concerning perioperative medical treatment.

Intravoxel incoherent motion diffusion-weighted imaging and shear wave elastography for evaluating peritumoral liver fibrosis after transarterial chemoembolization in a VX2 rabbit liver tumor model

In this study, we sought to evaluate changes in peritumoral fibrosis after transarterial chemoembolization (TACE) in a rabbit VX2 liver tumor model using intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI) and ultrasound shear wave elastography (SWE). A total of 20 rabbits underwent implantation of VX2 tumor tissues in the left lobe of the liver. The rabbits were randomly divided into an experimental group (n = 10) or a control group (n = 10). Those in the experimental group were treated with an emulsion of lipiodol and pirarubicin through a microcatheter 2–3 weeks after implantation; those in the control group were treated with sterile water. Compared with the control group, the true diffusion coefficient (D) and pseudodiffusion coefficient (D*) values in liver tissues were significantly lower (p < 0.05 for all) and liver stiffness values (LSV) (10.58 ± 0.89 kPa) were higher in the experimental group (7.65 ± 0.86 kPa; p < 0.001). The median stage of liver fibrosis based on METAVIR scores was 1 (1,1) in the control group and 2 (2,3) in the experimental group (Z = 4.15, p < 0.001). D, D*, and LSV were significantly correlated with pathologic staining in the assessment of liver fibrosis (r = −0.54 p = 0.015; r = −0.50, p = 0.025; r = 0.91, p < 0.001; respectively). These data suggest that TACE aggravates liver injury and liver fibrosis, especially surrounding the tumor, in a rabbit VX2 liver tumor model. IVIM DWI and SWE can be used to evaluate the change in liver fibrosis.

Liver fibrosis assessment: MR and US elastography

Elastography has emerged as a preferred non-invasive imaging technique for the clinical assessment of liver fibrosis. Elastography methods provide liver stiffness measurement (LSM) as a surrogate quantitative biomarker for fibrosis burden in chronic liver disease (CLD). Elastography can be performed either with ultrasound or MRI. Currently available ultrasound-based methods include strain elastography, two-dimensional shear wave elastography (2D-SWE), point shear wave elastography (pSWE), and vibration-controlled transient elastography (VCTE). MR Elastography (MRE) is widely available as two-dimensional gradient echo MRE (2D-GRE-MRE) technique. US-based methods provide estimated Young's modulus (eYM) and MRE provides magnitude of the complex shear modulus. MRE and ultrasound methods have proven to be accurate methods for detection of advanced liver fibrosis and cirrhosis. Other clinical applications of elastography include liver decompensation prediction, and differentiation of non-alcoholic steatohepatitis (NASH) from simple steatosis (SS). In this review, we briefly describe the different elastography methods, discuss current clinical applications, and provide an overview of advances in the field of liver elastography.

The Extracellular Matrix Stiffening: A Trigger of Prostate Cancer Progression and Castration Resistance?

Despite advancements made in diagnosis and treatment, prostate cancer remains the second most diagnosed cancer among men worldwide in 2020, and the first in North America and Europe. Patients with localized disease usually respond well to first-line treatments, however, up to 30% develop castration-resistant prostate cancer (CRPC), which is often metastatic, making this stage of the disease incurable and ultimately fatal. Over the last years, interest has grown into the extracellular matrix (ECM) stiffening as an important mediator of diseases, including cancers. While this process is increasingly well-characterized in breast cancer, a similar in-depth look at ECM stiffening remains lacking for prostate cancer. In this review, we scrutinize the current state of literature regarding ECM stiffening in prostate cancer and its potential association with disease progression and castration resistance.

microRNA-378a-3p regulates the progression of hepatocellular carcinoma by regulating PD-L1 and STAT3

Programmed death ligand 1 (PD-L1) plays an essential role in the development or progression of hepatocellular carcinoma (HCC). MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression during normal and pathophysiological events. Here, we explored the functions and detailed mechanisms of miR-378a-3p and PD-L1 in HCC progression. First, miR-378a-3p was selected by analyzing miRNA levels in two HCC Gene Expression Omnibus datasets. We found that miR-378a-3p levels exhibited a downward trend in HCC and were negatively correlated with PD-L1 levels. Additionally, a dual luciferase assay predicted that miR-378a-3p directly targets PD-L1. Moreover, the transfection of miR-378a-3p mimics into Li-7 and HuH-7 cells effectively decreased the PD-L1 mRNA and protein expression levels, and inhibited Treg differentiation in co-culture models by modulating the expression levels of certain cytokines. Furthermore, the overexpression of miR-378a-3p hindered cell proliferation and migration but facilitated apoptosis by repressing STAT3 signaling in HCC cells. In conclusion, miR-378a-3p appears to inhibit HCC tumorigenesis by regulating PD-L1 and STAT3 levels. Thus, miR-378a-3p may be a potential target for HCC therapy.

Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

Background/Aims

Posthepatectomy liver failure (PHLF) is a major complication that increases mortality in patients with hepatocellular carcinoma after surgical resection. The aim of this retrospective study was to evaluate the utility of magnetic resonance elastography-assessed liver stiffness (MRE-LS) for the prediction of PHLF and to develop an MRE-LS-based risk prediction model.

Methods

A total of 160 hepatocellular carcinoma patients who underwent surgical resection with available preoperative MRE-LS data were enrolled. Clinical and laboratory parameters were collected from medical records. Logistic regression analyses were conducted to identify the risk factors for PHLF and develop a risk prediction model.

Results

PHLF was present in 24 patients (15%). In the multivariate logistic analysis, high MRE-LS (kPa; odds ratio [OR] 1.49, 95% confidence interval [CI] 1.12 to 1.98, p=0.006), low serum albumin (≤3.8 g/dL; OR 15.89, 95% CI 2.41 to 104.82, p=0.004), major hepatic resection (OR 4.16, 95% CI 1.40 to 12.38, p=0.014), higher albumin-bilirubin score (>-0.55; OR 3.72, 95% CI 1.15 to 12.04, p=0.028), and higher serum α-fetoprotein (>100 ng/mL; OR 3.53, 95% CI 1.20 to 10.40, p=0.022) were identified as independent risk factors for PHLF. A risk prediction model for PHLF was established using the multivariate logistic regression equation. The area under the receiver operating characteristic curve (AUC) of the risk prediction model was 0.877 for predicting PHLF and 0.923 for predicting grade B and C PHLF. In leave-one-out cross-validation, the risk model showed good performance, with AUCs of 0.807 for all-grade PHLF and 0. 871 for grade B and C PHLF.

Conclusions

Our novel MRE-LS-based risk model had excellent performance in predicting PHLF, especially grade B and C PHLF.

Different Risk Factors for Early and Late Recurrence After Curative Resection of Hepatocellular Carcinoma

BACKGROUND

Factors of early and late recurrence after curative resection of hepatocellular carcinoma (HCC) may be different. The aim of this study was to identify clinical factors, including liver stiffness measurement (LSM), which are associated with HCC recurrence after curative resection.

METHODS

Patients who underwent preoperative LSM and primary curative resection for HCC between October 2015 and May 2018 were retrospectively reviewed, with 1 year as the cut-off between early and late recurrence.

RESULTS

Recurrence was observed in 42/149 (28.2%) patients over a median follow-up of 38.3 months (early recurrence: 10 [6.7%] patients; late recurrence: 32 [21.5%] patients). Multivariate analysis identified LSM (P = 0.026) and tumor size (P = 0.010) as the only factors that were significantly associated with recurrence-free survival. Compared with patients without recurrence, those with early recurrence had larger tumor size (P = 0.035) and those with late recurrence had higher LSM (P = 0.024). Receiver-operating characteristic analysis indicated that the optimal LSM cut-off value for predicting HCC recurrence was 7.4 kPa.

CONCLUSION

Tumor size was associated with early HCC recurrence after curative resection and LSM was associated with late recurrence. LSM cut-off of 7.4 kPa is recommended in predicting recurrence.

The Advance of Magnetic Resonance Elastography in Tumor Diagnosis

The change in tissue stiffness caused by pathological changes in the tissue's structure could be detected earlier, prior to the manifestation of their clinical features. Magnetic resonance elastography (MRE) is a noninvasive imaging technique that uses low-frequency vibrations to quantitatively measure the elasticity or stiffness of tissues. In tumor tissue, stiffness is directly related to tumor development, invasion, metastasis, and chemoradiotherapy resistance. It also dictates the choice of surgical method. At present, MRE is widely used in assessing different human organs, such as the liver, brain, breast, prostate, uterus, gallbladder, and colon stiffness. In the field of oncology, MRE's value lies in tumor diagnosis (especially early diagnosis), selection of treatment method, and prognosis evaluation. This article summarizes the principle of MRE and its research and application progress in tumor diagnosis and treatment.

Tumor stiffness measured by 3D magnetic resonance elastography can help predict the aggressiveness of endometrial carcinoma: preliminary findings

Background

Preoperative evaluation of aggressiveness, including tumor histological subtype, grade of differentiation, Federation International of Gynecology and Obstetrics (FIGO) stage, and depth of myometrial invasion, is significant for treatment planning and prognosis in endometrial carcinoma (EC). The purpose of this study was to evaluate whether three-dimensional (3D) magnetic resonance elastography (MRE) can help predict the aggressiveness of EC.

Methods

From August 2015 to January 2019, 82 consecutive patients with suspected uterine tumors underwent pelvic MRI and MRE scans, and 15 patients with confirmed EC after surgical resection were enrolled. According to pathological results (tumor grade, histological subtype, FIGO stage, and myometrial invasiveness), the patients were divided into two subgroups. The independent-samples t-test or Mann-Whitney U test was used to compare the stiffness between different groups. The diagnostic performance was determined with receiver operating characteristic (ROC) curve analysis.

Results

The stiffness of EC with ≥ 50 % (n = 6) myometrial invasion was significantly higher than that with < 50 % (n = 9) myometrial invasion (3.68 ± 0.59 kPa vs. 2.61 ± 0.72 kPa, p = 0.009). Using a stiffness of 3.04 kPa as a cutoff value resulted in 100 % sensitivity and 77.8 % specificity for differentiating ≥ 50 % myometrial invasion from < 50 % myometrial invasion of EC. The stiffness of poorly differentiated EC (n = 8) was significantly higher than that of well/moderately differentiated EC (n = 7) (3.47 ± 0.64 kPa vs. 2.55 ± 0.82 kPa, p = 0.028). Using a stiffness of 3.04 kPa as a cutoff value resulted in 75 % sensitivity and 71.4 % specificity for differentiating poorly differentiated from well/moderately differentiated EC. The stiffness of FIGO stage II/III EC was significantly higher than that of FIGO stage I EC (3.69 ± 0.65 kPa vs. 2.72 ± 0.76 kPa, p = 0.030). Using a stiffness of 3.04 kPa as a cutoff value resulted in 100 % sensitivity and 70 % specificity for differentiating FIGO stage I EC from FIGO stage II/III EC. The tumor stiffness value in type II (n = 3) EC was higher than that in type I (n = 12) EC (3.67 ± 0.59 kPa vs. 2.88 ± 0.85 kPa), but the difference was not significant (p = 0.136).

Conclusions

Tumor stiffness measured by 3D MRE may be potentially useful for predicting tumor grade, FIGO stage and myometrial invasion of EC and can aid in the preoperative risk stratification of EC.

Liver stiffness measured by magnetic resonance elastography in early recurrence of hepatocellular carcinoma after treatment: A protocol for systematic review and meta analysis

BACKGROUND

With high diagnostic accuracy, magnetic resonance elastography (MRE) is a noninvasive tool and can be adopted to measure liver stiffness (LS). In this study, meta-analysis was carried out to further evaluate whether LS measured by MRE can predict early recurrence in patients with hepatocellular carcinoma (HCC).

METHODS

PUBMED, EMBASE, Web of Science, China National Knowledge Infrastructure, and Cochrane Library database were searched for studies related to LS measured by MRE in the prediction of recurrence in patients with HCC. Survival outcome was estimated by hazard ratios and 95% confidence intervals. Meta-analysis was conducted with the Stata 16.0.

RESULTS

The results of this meta-analysis will be submitted to a peer-reviewed journal for publication.

CONCLUSION

This study will provide evidence support for LS measured by MRE in predicting the recurrence of HCC.

ETHICS AND DISSEMINATION

The private information from individuals will not be published. This systematic review also should not damage participants' rights. Ethical approval is not available. The results may be published in a peer-reviewed journal or disseminated in relevant conferences.

OSF REGISTRATION NUMBER

DOI 10.17605/ OSF.IO / SURH3.

Preoperative combi-elastography for the prediction of early recurrence after curative resection of hepatocellular carcinoma

PURPOSE

To estimate the prognostic value of preoperative combi-elastography for early recurrence (ER) of hepatocellular carcinoma (HCC) after radical resection.

METHODS

A total of 94 HCC patients undergoing hepatectomy from January to August 2019 were included. The combined elastography (ARIETTA 850, Hitachi Healthcare) was used for real-time tissue elastography and shear wave measurement analysis. Six elastography related indicators were calculated. The patients were randomly divided into a training and a validation group in a 7:3 ratio and prediction model was assessed about discrimination capability by using area under the receiver operating curve. Univariate and multivariate analyses were performed to determine the prognostic value of clinicopathological factors, laboratory tests, and elastography for HCC ER.

RESULTS

The Vs, E, F, and A indexes were significantly higher in patients with ER than in those without ER (P = 0.002, P = 0.002, P < 0.001, and P < 0.001, respectively). Multivariate logistic regression analysis indicated that microvascular invasion (MVI, odds ratio [OR] = 3.964, 95% confidence interval [CI] = 1.326-11.845; P = 0.010) and the F index (OR = 9.533, 95%CI = 1.921-47.296; P = 0.006) were independent predictors of ER in HCC. A ER prediction model based on laboratory tests, MVI and F index were moderate [area under curves (AUCs) in training and validation cohort were 0.829(95%CI: 0.723-0.935; P < 0.001) and 0.846 (95%CI: 0.699-0.994; P = 0.002), respectively].

CONCLUSION

Preoperative combi-elastography analysis could be used as a potential prognostic tool for HCC ER and assist in clinical decision-making.

Quantitative magnetic resonance imaging for chronic liver disease

Chronic liver disease (CLD) has rapidly increased in prevalence over the past two decades, resulting in significant morbidity and mortality worldwide. Historically, the clinical gold standard for diagnosis, assessment of severity, and longitudinal monitoring of CLD has been liver biopsy with histological analysis, but this approach has limitations that may make it suboptimal for clinical and research settings. Magnetic resonance (MR)-based biomarkers can overcome the limitations by allowing accurate, precise, and quantitative assessment of key components of CLD without the risk of invasive procedures. This review briefly describes the limitations associated with liver biopsy and the need for non-invasive biomarkers. It then discusses the current state-of-the-art for MRI-based biomarkers of liver iron, fat, and fibrosis, and inflammation.

Impact of Graft Weight Change During Perfusion on Hepatocellular Carcinoma Recurrence After Living Donor Liver Transplantation

BACKGROUNDS

Inadequate liver volume and weight is a major source of morbidity and mortality after adult living donor liver transplantation (LDLT). The purpose of our study was to investigate HCC recurrence, graft failure, and patient survival according to change in right liver graft weight after histidine-tryptophan-ketoglutarate (HTK) solution perfusion in LDLT.

METHODS

Two hundred twenty-eight patients underwent LDLT between 2013 and 2017. We calculated the change in graft weight by subtracting pre-perfusion graft weight from post-perfusion graft weight. Patients with increased graft weight were defined as the positive group, and patients with decreased graft weight were defined as the negative group.

RESULTS

After excluding patients who did not meet study criteria, 148 patients underwent right or extended right hepatectomy. The negative group included 89 patients (60.1%), and the positive group included 59 patients (39.9%). Median graft weight change was -28 g (range; -132-0 g) in the negative group and 21 g (range; 1-63 g) in the positive group (P<0.001). Median hospitalization time was longer for the positive group than the negative group (27 days vs. 23 days; P=0.048). There were no statistical differences in tumor characteristics, postoperative complications, early allograft dysfunction, or acute rejection between the two groups. Disease-free survival, death-censored graft survival, and patient survival were lower in the positive group than the negative group. Additionally, the positive group showed strong association with HCC recurrence, death-censored graft survival, and patient survival in multivariate analysis.

CONCLUSION

This study suggests that positive graft weight change during HTK solution perfusion indicates poor prognosis in LDLT.

MR elastography of liver: current status and future perspectives

Non-invasive evaluation of liver fibrosis has evolved over the last couple of decades. Currently, elastography techniques are the most widely used non-invasive methods for clinical evaluation of chronic liver disease (CLD). MR elastography (MRE) of the liver has been used in the clinical practice for nearly a decade and continues to be widely accepted for detection and staging of liver fibrosis. With MRE, one can directly visualize propagating shear waves through the liver and an inversion algorithm in the scanner automatically converts the shear wave properties into an elastogram (stiffness map) on which liver stiffness can be calculated. The commonly used MRE method, two-dimensional gradient recalled echo (2D-GRE) sequence has produced excellent results in the evaluation of liver fibrosis in CLD from various etiologies and newer clinical indications continue to emerge. Advances in MRE technique, including 3D MRE, automated liver elasticity calculation, improvements in shear wave delivery and patient experience, are promising to provide a faster and more reliable MRE of liver. Innovations, including evaluation of mechanical parameters, such as loss modulus, displacement, and volumetric strain, are promising for comprehensive evaluation of CLD as well as understanding pathophysiology, and in differentiating various etiologies of CLD. In this review, the current status of the MRE of liver in CLD are outlined and followed by a brief description of advanced techniques and innovations in MRE of liver.

Liver stiffness in magnetic resonance elastography is prognostic for sorafenib-treated advanced hepatocellular carcinoma

OBJECTIVE

We investigated whether liver stiffness (LS) quantified using magnetic resonance elastography (MRE) could predict the prognosis of advanced hepatocellular carcinoma (HCC) patients treated with sorafenib.

METHODS

We selected 50 sorafenib-treated advanced HCC patients who underwent MRE within 3 months before drug administration from a prospectively maintained cohort of chronic liver disease patients, according to the inclusion and exclusion criteria. Univariate and multivariate analyses were performed to evaluate the prognostic role of laboratory data, tumor characteristics, and MRE-assessed LS for overall survival (OS), progression-free survival (PFS), and significant liver injury (grade ≥ 3) after sorafenib administration.

RESULTS

High MRE-assessed LS either as continuous (per kPa, hazard ratio (HR) 1.54; 95% confidence interval (CI) 1.23-1.92, p < 0.001) or categorical (> 7.5 kPa, HR 4.06, 95% CI 1.40-11.79, p < 0.01) variable was significantly associated with poor OS along with higher serum alpha-fetoprotein (AFP, ≥ 400 ng/mL) and advanced tumor stage (modified Union for International Cancer Control (mUICC) IVb). Higher MRE-assessed LS was also significantly associated with the development of significant liver injury after sorafenib administration (per kPa, HR 1.62, 95% CI 1.21-2.17, p = 0.001; > 7.5 kPa, HR 10.11, 95% CI 2.41-42.46, p = 0.002). PFS analysis identified higher serum AFP (≥ 400 ng/mL) and advanced tumor stage (mUICC IVb) as significant risk factors for early disease progression, whereas LS was not associated with PFS CONCLUSION: Higher MRE-assessed LS is a potential biomarker for predicting poor OS and significant liver injury in advanced HCC patients treated with sorafenib.

KEY POINTS

• Higher pretreatment LS by MRE (> 7.5 kPa), higher AFP (≥ 400 ng/mL), and advanced tumor stage (mUICC IVb) were associated with poor OS in advanced HCC patients treated with sorafenib. • Higher pretreatment LS by MRE was associated with developing significant (grade ≥ 3) liver injury during sorafenib treatment, which required termination of the therapy. • Patients with high pretreatment LS by MRE should be monitored carefully for potential liver injury during sorafenib treatment.

Integrating baseline MR imaging biomarkers into BCLC and CLIP improves overall survival prediction of patients with hepatocellular carcinoma (HCC)

OBJECTIVES

We aimed to evaluate the independent predictive role of baseline imaging biomarkers for overall survival (OS) and transplant-free survival (TFS) in patients with HCC and assess the incremental value of these biomarkers to current staging systems.

METHODS

In this retrospective IRB approved study, the clinical, laboratory, and imaging parameters of 304 HCC patients were collected. Cox regression model was utilized to identify the potential predictors of survival. Recursive partitioning test was utilized to identify the optimal ADC cutoff for stratifying patients' OS. Patients were stratified based on Barcelona Clinic Liver Cancer (BCLC) and Cancer of the Liver Italian Program (CLIP). Binary ADC value (above vs. below the cutoff) and tumor margin (well- vs. ill-defined) were integrated into BCLC and CLIP. OS and TFS was compared for patients based on standard criteria with and without imaging biomarkers.

RESULTS

At baseline, patients with low tumor ADC and well-defined tumor margin (favorable imaging biomarkers) had longer survival, as compared to those with high ADC and ill-defined tumor margin (unfavorable imaging biomarkers) (median OS of 43 months vs. 7 months, respectively) (p < 0.001). Tumor ADC and tumor margin remained strong independent predictors of survival after adjustment for demographics, BCLC and CLIP staging, and tumor burden. Incorporating ADC and tumor margin improved performance of OS prediction by 9% in BCLC group and 6% in CLIP group.

CONCLUSION

Incorporating ADC and tumor margin to current staging systems for HCC significantly improve prediction of OS and TFS of these criteria.

KEY POINTS

• ADC and tumor margin are predictors of overall survival in HCC patients, independent of clinical, laboratory, and other imaging variables. • Adding ADC and tumor margin improved the prognostic value of BCLC and CLIP criteria by 9% and 6%, respectively. • High ADC and ill-defined tumor margin at baseline predicted poor survival, regardless of patient's liver function and general health status.