High-grade Hepatocellular Carcinoma Research Articles

Advancements in hepatocellular carcinoma management: the role of 18F-FDG PET-CT in diagnosing portal vein tumor thrombosis.

Portal vein thrombosis, a relatively frequent complication associated with hepatocellular carcinoma (HCC) and liver cirrhosis, is recognized as a significant global health concern. This is mainly due to these conditions' high prevalence and potentially severe outcomes. The aim of our study was to conduct a comprehensive literature review to evaluate the efficacy, accuracy, and clinical implications of 18F-FDG PET-CT in diagnosing and managing portal vein tumor thrombosis (PVTT) in patients with HCC. HCC, which accounts for 80% of liver malignancies, ranks as the fourth most prevalent cancer globally and is a significant contributor to cancer-related mortality. The majority of HCC patients are diagnosed at an advanced stage, leading to a deterioration in patient outcomes. Involvement of the portal vein is also a significant negative factor. This review analyzes the application of 18F-FDG PET-CT in the detection and management of PVTT in patients with HCC, with an emphasis on the importance of the maximum standardized uptake value as an essential diagnostic and prognostic marker. 18F-FDG PET-CT is invaluable for detecting recurrence and guiding management strategies, particularly in patients with high-grade HCC, and plays a pivotal role in differentiating malignant portal vein thrombi from their benign counterparts.

CT-based radiomics for predicting pathological grade in hepatocellular carcinoma.

To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT). Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency. Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.

Multiphase MRI-Based Radiomics for Predicting Histological Grade of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer. Accurate preoperative prediction of histological grade holds potential for improving clinical management and disease prognostication. To evaluate the performance of a radiomics signature based on multiphase MRI in assessing histological grade in solitary HCC. Retrospective. A total of 405 patients with histopathologically confirmed solitary HCC and with liver gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI within 1 month of surgery. Contrast-enhanced T1-weighted spoiled gradient echo sequence (LAVA) at 1.5 or 3.0 T. Tumors were graded (low/high) according to results of histopathology. Basic clinical characteristics (including age, gender, serum alpha-fetoprotein (AFP) level, history of hepatitis B, and cirrhosis) were collected and tumor size measured. Radiomics features were extracted from Gd-EOB-DTPA-enhanced MRI data. Three feature selection strategies were employed sequentially to identify the optimal features: SelectFromModel (SFM), SelectPercentile (SP), and recursive feature elimination with cross-validation (RFECV). Probabilities of five single-phase radiomics-based models were averaged to generate a radiomics signature. A combined model was built by combining the radiomics signature and clinical predictors. Pearson χ2 test/Fisher exact test, Wilcoxon rank sum test, interclass correlation coefficient (ICC), univariable/multivariable logistic regression analysis, area under the receiver operating characteristic (ROC) curve (AUC), DeLong test, calibration curve, Brier score, decision curve, Kaplan-Meier curve, and log-rank test. A P-value <0.05 was considered statistically significant. High-grade HCCs were present in 33.8% of cases. AFP levels (odds ratio [OR] 1.89) and tumor size (>5 cm; OR 2.33) were significantly associated with HCC grade. The combined model had excellent performance in assessing HCC grade in the test dataset (AUC: 0.801), and demonstrated satisfactory calibration and clinical utility. A model that combined a radiomics signature derived from preoperative multiphase Gd-EOB-DTPA-enhanced MRI and clinical predictors showed good performance in assessing HCC grade. 3 TECHNICAL EFFICACY: Stage 5.

Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma.

To compare the efficacy of radiomics models via five machine learning algorithms in predicting the histological grade of hepatocellular carcinoma (HCC) before surgery and to develop the most stable model to classify high-risk HCC patients. Contrast-enhanced computed tomography (CECT) images of 175 HCC patients before surgery were analysed, and radiomics features were extracted from CECT images (including arterial and portal phases). Five machine learning models, including Bayes, random forest (RF), k-nearest neighbors (KNN), logistic regression (LR), and support vector machine (SVM), were applied to establish the model. The stability of the five models was weighed by the relative standard deviation (RSD), and the lowest RSD value was chosen as the most stable model to predict the histological grade of HCC. The area under the curve (AUC) and Delong tests were devoted to assessing the predictive efficacy of the models. High-grade HCC accounted for 28.57% (50/175) of the 175 patients. The RSD value of AUC via the RF machine learning model was the lowest (2.3%), followed by Bayes (3.2%), KNN (6.4%), SVM (8.7%) and LR (31.3%). In addition, the RF model (AUC = 0.995) was better than the other four models in the training set (p < 0.05), as well as obtained good predictive performance in the test set (AUC = 0.837). Among the five machine learning models, the RF-based radiomics model was the most stable and performed excellently in identifying high histological grade of HCC.

Non-invasive preoperative prediction of Edmondson-Steiner grade of hepatocellular carcinoma based on contrast-enhanced ultrasound using ensemble learning.

This study aimed to explore the clinical value of non-invasive preoperative Edmondson-Steiner grade of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS). 212 cases of HCCs were retrospectively included, including 83 cases of high-grade HCCs and 129 cases of low-grade HCCs. Three representative CEUS images were selected from the arterial phase, portal vein phase, and delayed phase and stored in a 3-dimensional array. ITK-SNAP was used to segment the tumor lesions manually. The Radiomics method was conducted to extract high-dimensional features on these contrast-enhanced ultrasound images. Then the independent sample T-test and the Least Absolute Shrinkage and Selection Operator (LASSO) were employed to reduce the feature dimensions. The optimized features were modeled by a classifier based on ensemble learning, and the Edmondson Steiner grading was predicted in an independent testing set using this model. A total of 1338 features were extracted from the 3D images. After the dimension reduction, 10 features were finally selected to establish the model. In the independent testing set, the integrated model performed best, with an AUC of 0.931. This study proposed an Edmondson-Steiner grading method for HCC with CEUS. The method has good classification performance on independent testing sets, which can provide quantitative analysis support for clinical decision-making.

Guiding Radiotherapy Target Area Delineation by Gd-EOB-DTPA-Enhanced MRI

<h3>Purpose/Objective(s)</h3> To compare the differences between the diameters and volumes of liver tumors measured with preoperative Gd-EOB-DTPA-enhanced MRI, enhanced CT and postoperative gross pathology specimens and to measure the hepatic lesion microinvasion range for accurate radiotherapy GTV delineation. This study also provides an important reference for CTV delineation. <h3>Materials/Methods</h3> Twenty-eight hepatocellular carcinoma (HCC) patients who underwent EOB-MRI and enhanced CT before surgery were enrolled. The results from EOB-MRI, enhanced CT and pathology were compared. The differences in GTV were calculated as V_MRI = G_MRI-G_P and V_CT = G_CT-G_P, and MI_MAX was the microinvasion distance around the tumor. <h3>Results</h3> The V_MRI was 5.90 ± 6.52 mm, and the mean V_CT was 11.40 ± 8.06 mm. The volume difference between MRI and pathological examinations was significantly smaller than that between CT and pathological examinations (<i>P</i><0.05). Twenty-seven of the 28 patients had microscopic infiltration. The mean MI_MAX was 3.27±1.38 mm; thus, grade I MI_MAX < grade II MI_MAX < grade III MI_MAX < grade IV MI_MAX, and the differences were statistically significant (<i>P</i><0.05). <h3>Conclusion</h3> EOB-MRI, enhanced CT and pathological GTV are comparable. GTVs delineated with EOB-MRI and enhanced CT can replace GTVs determined by pathology. The consistency between GTVs delineated by EOB-MRI and those delineated by pathology is high, and the GTVs delineated by EOB-MRI are more accurate than those delineated by enhanced CT; high-grade HCC requires a large boundary. The HCC boundary needs to be expanded by 5.97 mm to include 95% of the pathological microinvasions.

S3269 A Rare Case of HCC Recurrence as a Bleeding Gastric Ulcer

Introduction: Hepatocellular Carcinoma (HCC) is a potential complication of cirrhosis. Liver transplantation is often viewed as curative of HCC when fulfilling the Milan criteria prior to surgery. However, recurrence rates of HCC post-transplant remain high. Here, we present a case of post-transplant HCC recurrence presenting as a bleeding gastric ulcer. Case Description/Methods: A 72-year-old Caucasian female presented with coffee-ground emesis. Three years prior, she had orthotopic liver transplantation due to hepatitis C virus (HCV) related cirrhosis, previously treated with Vosevi/ribavarin with sustained virologic response and HCC with evidence of moderate differentiation and focus of vascular invasion on explanted liver. Upper endoscopy revealed a cratered gastric ulcer with heaped edges. Biopsies from the ulcer edges showed poorly differentiated adenocarcinoma with signet ring features. Immunohistochemical testing showed tumor cells positive for CK8/18, Glutamine Synthetase, and polyclonal CEA with canalicular pattern; features consistent with high grade HCC. CT scan revealed a necrotic mass replacing most of the pancreatic body and tail, a lung mass and multiple vertebral compression fractures. Fine needle biopsy of pancreatic mass was consistent with metastatic carcinoma compatible with HCC. (Figure) Discussion: Tumor recurrence post-liver transplantation is noted in 15-20% of cases after 5 years. There is no clear consensus for Standard post-transplantation monitoring. A multi-center study has proposed a protocol using the RETREAT (Risk Estimation of Tumor Recurrence Post-Transplantation) scoring system for determining screening of HCC recurrence. This includes: AFP levels prior to transplant (max score of 3 for AFP >1000), vascular invasion (max score of 2 for any evidence of invasion), and sum of the diameters of HCC lesions (max score of 3 for sum >10). In our patient, with microvascular invasion, a score of 2 would standardize her to screening every 6 months for 2 years which was consistent with the monitoring she had. The median time to HCC tumor recurrence is within 12-16 months post-transplantation, with over 75% occurring in first 2 years. Initial presentation of HCC recurrence as a direct gastrointestinal invasion is exceeding rare. There have been few case reports describing similar presentations as in our case, however, none have occurred after liver transplantation. Given its rarity, differential diagnosis of gastric ulcers in patients with history of HCC should include metastatic disease.Figure 1.: A. Gastric Ulcer B. Pancreatic Mass C. Lung Mass.

Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study.

To develop a radiomics-based hepatocellular carcinoma (HCC) grade classifier model based on data from gadoxetic acid-enhanced MRI. This retrospective study included 137 patients who underwent hepatectomy for a single HCC and gadoxetic acid-enhanced MRI within 60days before surgery. HCC grade was categorized as low or high (modified Edmondson-Steiner grade I-II vs. III-IV). We used the hepatobiliary phase (HBP), portal venous phase, T2-weighted image(T2WI), and T1-weighted image(T1WI). From the volume of interest in HCC, 833 radiomic features were extracted. Radiomic and clinical features were selected using a random forest regressor, and the classification model was trained and validated using a random forest classifier and tenfold stratified cross-validation. Eight models were developed using the radiomic features alone or by combining the radiomic and clinical features. Models were validated with internal enrolled data (internal validation) and a dataset (28 patients) at a separate institution (external validation). The area under the curve (AUC) of the validation results was compared using the DeLong test. In internal and external validation, the HBP radiomics-only model showed the highest AUC (internal 0.80 ± 0.09, external 0.70 ± 0.09). In external validation, all models showed lower AUC than those for internal validation, while the T2WI and T1WI models failed to predict the HCC grade (AUC 0.30-0.58) in contrast to the internal validation results (AUC 0.67-0.78). The radiomics-based machine learning model from gadoxetic acid-enhanced liver MRI could distinguish between low- and high-grade HCCs. The radiomics-only HBP model showed the best AUC among the eight models, good performance in internal validation, and fair performance in external validation.

Preoperative gadoxetic acid-enhanced MRI predicts aggressive pathological features in LI-RADS category 5 hepatocellular carcinoma

To investigate whether Liver Imaging Reporting and Data System (LI-RADS) imaging features and non-LI-RADS imaging features can predict aggressive pathological features in adult patients with hepatocellular carcinoma (HCC). From February 2018 to September 2021, 236 adult patients with cirrhosis or hepatitis B virus infection in which liver cancer was suspected underwent MRI within 1 month before surgery. Significant MRI findings and alpha-fetoprotein (AFP) level predicted high-grade HCC and microvascular invasion (MVI) by univariate and multivariate logistic regression models. The study included 112 patients with histopathologically confirmed liver cancer (≤5 cm), 35 of whom (31.3%) high-grade HCC and 42 of 112 (37.5%) patients had MVI. Mosaic architecture (odds ratio [OR]=6.031; 95% confidence interval [CI]: 1.366, 26.626; p=0.018), coronal enhancement (OR=5.878; 95% CI: 1.471, 23.489; p=0.012), and intratumoural vessels (OR=5.278; 95% CI: 1.325, 21.020; p=0.018) were significant independent predictors of high-grade HCC. A non-smooth tumour margin (OR=10.237; 95% CI: 1.547, 67.760; p=0.016), coronal enhancement (OR=3.800; 95% CI: 1.152, 12.531; p=0.028), and peritumoural hypointensity on the hepatobiliary phase (HBP; OR=10.322; 95% CI: 2.733, 38.986; p=0.001) were significant independent predictors of MVI. In high-risk adult patients with single LR-5 HCC (≤5 cm), mosaic architecture, coronal enhancement, and intratumoural vessels are independent predictors of high-grade HCC. Non-smooth tumour margin, coronal enhancement, and peritumoural hypointensity on HBP independently predicted MVI.

Gd-EOB-DTPA-enhanced MRI radiomic features for predicting histological grade of hepatocellular carcinoma.

Prediction models for the histological grade of hepatocellular carcinoma (HCC) remain unsatisfactory. The purpose of this study is to develop preoperative models to predict histological grade of HCC based on gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) radiomics. And to compare the performance between artificial neural network (ANN) and logistic regression model. A total of 122 HCCs were randomly assigned to the training set (n=85) and the test set (n=37). There were 242 radiomic features extracted from volumetric of interest (VOI) of arterial and hepatobiliary phases images. The radiomic features and clinical parameters [gender, age, alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), alanine aminotransferase (ALT), aspartate transaminase (AST)] were selected by permutation test and decision tree. ANN of arterial phase (ANN-AP), logistic regression model of arterial phase (LR-AP), ANN of hepatobiliary phase (ANN-HBP), logistic regression mode of hepatobiliary phase (LR-HBP), ANN of combined arterial and hepatobiliary phases (ANN-AP + HBP), and logistic regression model of combined arterial and hepatobiliary phases (LR-AP + HBP) were built to predict HCC histological grade. Those prediction models were assessed and compared. ANN-AP and LR-AP were composed by AST and radiomic features based on arterial phase. ANN-HBP and LR-HBP were composed by AFP and radiomic features based on hepatobiliary phase. ANN-AP + HBP and LR-AP + HBP were composed by AST and radiomic features based on arterial and hepatobiliary phases. The prediction models could distinguish between high-grade tumors [Edmondson-Steiner (E-S) grade III and IV] and low-grade tumors (E-S grade I and II) in both training set and test set. In the test set, the AUCs of ANN-AP, LR-AP, ANN-HBP, LR-HBP, ANN-AP + HBP and LR-AP + HBP were 0.889, 0.777, 0.941, 0.819, 0.944 and 0.792 respectively. The ANN-HBP was significantly superior to LR-HBP (P=0.001). And the ANN-AP + HBP was significantly superior to LR-AP + HBP (P=0.007). Prediction models consisting of clinical parameters and Gd-EOB-DTPA-enhanced MRI radiomic features (based on arterial phase, hepatobiliary phase, and combined arterial and hepatobiliary phases) could distinguish between high-grade HCCs and low-grade HCCs. And the ANN was superior to logistic regression model in predicting histological grade of HCC.

E-cadherin and aquaporin 1 co-expression analysis in hepatocellular carcinoma: a pilot study

Hepatocellular carcinoma (HCC) is the main primary liver malignancy, being associated with both health and economic burden worldwide. Recently, novel molecular markers and possible therapeutic targets were identified. Different adhesion molecules, as well as possible angiogenesis-associated targets can be prime candidates when investigating novel therapies. Considering these premises, our goal was to study the co-existence of E-cadherin and aquaporin 1 (AQP1) in a series of HCC diagnosed patients. Utilizing archived tissue fragments from 17 patients diagnosed with well-to-moderate and poorly differentiated HCC, as well as four samples of normal liver tissue and using a highly specific biotin-free tyramide amplification technique, we have assessed here the expression of E-cadherin and AQP1 during HCC carcinogenesis. Moreover, as we have observed that some of the AQP1 expression seems membrane-bound, we have sought to evaluate their co-localization. Our data showed, as expected, that E-cadherin decreases from control tissue to low-grade and respectively, high-grade HCC. AQP1 was expressed, also as already known, at the level of endothelial blood vessels and bile ducts epithelia, however, we have showed here for the first time that this water pore is also expressed in the cytoplasm and membranes of hepatocytes, both in control and HCC tissue. Moreover, AQP1 expression parallels the decrease of E-cadherin expression during carcinogenesis, but together with this downregulation, we have also found a spatial decrease in the colocalization of the two proteins. Altogether, utilizing a biotin-free tyramide signal amplification technique, this study shows for the first time that AQP1 is expressed at the level of liver epithelia, in both control and HCC tissue.

The phosphorylated retinoid X receptor-α promotes diethylnitrosamine-induced hepatocarcinogenesis in mice through the activation of β-catenin signaling pathway.

Previous studies have shown that phosphorylation of the retinoid X receptor-α (RXRα) is associated with the development of hepatocellular carcinoma (HCC). However, these findings were revealed using HCC cell lines that express phosphorylated-RXRα (p-RXRα) proteins; therefore, it remains unclear whether p-RXRα affects hepatocarcinogenesis in vivo. Therefore, to investigate the biological function of p-RXRα in vivo, we developed a doxycycline-inducible ES cell line and transgenic mouse, both of which overexpress the phosphomimetic mutant form of RXRα, T82D/S260D, in a doxycycline-dependent manner. We found that the development of liver tumors, especially high-grade adenoma and HCC, was enhanced in diethylnitrosamine (DEN)-treated T82D/S260D-inducible mice. Moreover, the increased incidence of liver tumors in the transgenic mice was attributable to the promotion of cell cycle progression. Interestingly, the expression of β-catenin protein and its target gene cyclin D1 was elevated in the liver tumors of DEN-treated T82D/S260D-inducible mice, concurrent with increased cytoplasmic and nuclear β-catenin protein expression, indicating its stabilization and transcriptional activation. These results indicate that p-RXRα promotes DEN-induced hepatocarcinogenesis in mice through the activation of the β-catenin signaling pathway, suggesting that p-RXRα may serve as a possible therapeutic target for HCC.

Evaluation of nuclear PGAM2 value in hepatocellular carcinoma prognosis.

Phosphoglycerate mutase (PGAM) is a critical enzyme in glycolysis. PGAM2 is abundant in several types of tissues and malignant tumours. However, there is limited information regarding their clinicopathological significance in dysplastic nodules and hepatocellular carcinoma (HCC). This study aims to investigate the prognostic value of PGAM2 as a new biomarker for HCC. The PGAM2 expression level was evaluated by immunohistochemistry in liver cirrhosis (n = 10), low-grade dysplastic nodules (n = 15), high-grade dysplastic nodules (n = 15) and HCCs (n = 20) and 178 pairs of HCC and adjacent peritumoral liver tissues. We selected X-tile software for counting cut-point based on the outcomes for prognosis analysis, and used Kaplan-Meier analysis and Cox regression analysis can assess the prognosis of clinicopathologic parameters. Nuclear PGAM2 was significantly overexpressed in peritumoral liver tissues compared with HCC tissues (P = 0.0010). Kaplan-Meier analyses of 178 HCC samples revealed that nuclear PGAM2's high expression level, but not cytoplasmic PGAM2, was significantly related to good overall survival rate (OS). In addition, univariate and multivariate Cox analyses indicated nuclear PGAM2 expression could be regarded as valuable predictors for OS in HCC. PGAM2 was highly expressed in HCC tissues than liver cirrhosis tissues, and nuclear PGAM2's high expression might demonstrate HCC patients have poor postoperative results. Thus, nuclear PGAM2 can be regarded as valuable predictors for OS in HCC patients after surgery.

Gene networks and transcriptional regulators associated with liver cancer development and progression

BackgroundTreatment options for hepatocellular carcinoma (HCC) are limited, and overall survival is poor. Despite the high frequency of this malignoma, its basic disease mechanisms are poorly understood. Therefore, the aim of this study was to use different methodological approaches and combine the results to improve our knowledge on the development and progression of HCC.MethodsTwenty-three HCC samples were characterized by histological, morphometric and cytogenetic analyses, as well as comparative genomic hybridization (aCGH) and genome-wide gene expression followed by a bioinformatic search for potential transcriptional regulators and master regulatory molecules of gene networks.ResultsHistological evaluation revealed low, intermediate and high-grade HCCs, and gene expression analysis split them into two main sets: GE1-HCC and GE2-HCC, with a low and high proliferation gene expression signature, respectively. Array-based comparative genomic hybridization demonstrated a high level of chromosomal instability, with recurrent chromosomal gains of 1q, 6p, 7q, 8q, 11q, 17q, 19p/q and 20q in both HCC groups and losses of 1p, 4q, 6q, 13q and 18q characteristic for GE2-HCC. Gene expression and bioinformatics analyses revealed that different genes and gene regulatory networks underlie the distinct biological features observed in GE1-HCC and GE2-HCC. Besides previously reported dysregulated genes, the current study identified new candidate genes with a putative role in liver cancer, e.g. C1orf35, PAFAH1B3, ZNF219 and others.ConclusionAnalysis of our findings, in accordance with the available published data, argues in favour of the notion that the activated E2F1 signalling pathway, which can be responsible for both inappropriate cell proliferation and initial chromosomal instability, plays a pivotal role in HCC development and progression. A dedifferentiation switch that manifests in exaggerated gene expression changes might be due to turning on transcriptional co-regulators with broad impact on gene expression, e.g. POU2F1 (OCT1) and NFY, as a response to accumulating cell stress during malignant development. Our findings point towards the necessity of different approaches for the treatment of HCC forms with low and high proliferation signatures and provide new candidates for developing appropriate HCC therapies.

Pathological considerations of CEUS LI-RADS: correlation with fibrosis stage and tumour histological grade.

To evaluate the influence of pathological factors, such as fibrosis stage and histological grade, on the Liver Imaging Reporting and Data System (LI-RADS) v2017 category of contrast-enhanced ultrasonography (CEUS) in patients with high risk of hepatocellular carcinoma (HCC). Between June 2015 and December 2016, 441 consecutive patients at high risk of HCC with 460 pathologically proven HCCs were enrolled in this retrospective study. All patients underwent a CEUS examination. The major features (arterial phase hyperenhancement, late and mild washout) were assessed, and LI-RADS categories were assigned according to CEUS LI-RADS v2017. CEUS LI-RADS categories and major features were compared in different histological grades and fibrosis stages. The CEUS LR-5 category was more frequently assigned in the low-grade group (151/280) than in the high-grade group (66/159) (p = 0.013), whereas the LR-TIV category was more frequently assigned in the high-grade group (36/159) than in the low-grade group (40/280) (p= 0.035). CEUS LI-RADS category was not significantly different among different fibrosis stages (p ≥ 0.05). Arterial phase hyperenhancement (APHE) and the hepatic fibrosis stage showed a significant correlation in HCCs ≥ 2cm and the low-grade group (p= 0.027 and p= 0.003, respectively). No major features of CEUS LI-RADS showed statistically significant differences between the low- and high-grade groups (p≥ 0.05). Hepatic fibrosis stage can influence APHE but showed no impact on the CEUS LI-RADS classification, whereas the histological grade of HCC influenced the LR-5 and LR-TIV categories. • Histological grade influenced CEUS LR-5 and LR-TIV category (p = 0.013 and p = 0.035 respectively). Low-grade HCCs occurred more frequently in LR-5 category whereas high-grade HCCs occurred more frequently in LR-TIV category. • Fibrosis stage shows significant influence on APHE on HCCs of the size ≥ 2cm and low-grade group (p = 0.027 and p = 0.003, respectively). • Hepatic fibrosis stage and HCC histological grade exhibited limited impact on CEUS LI-RADS. CEUS LI-RADS may be feasible for diagnosing HCC in patients regardless of histological grade and fibrosis stage.

The androgen receptor expression and its activity have different relationships with prognosis in hepatocellular carcinoma

The role of the Androgen Receptor (AR) expression and its activity in the prognosis of hepatocellular carcinoma (HCC) remains inconclusive. The aim of this study is to analyze the role of the AR expression and its activity as prognostic biomarkers in HCC. Three-hundred and thirty-seven patients from The Cancer Genome Atlas (TCGA) (107 females; 59.42 years [SD = 13.0]) were included. To infer AR activity, the expression-profile of previously validated androgen responsive genes (ARGs) was included. AR activity was shown by the AR-Score-21 (21 ARGs) and AR-Score-13 (13 ARGs) that were computed based on the expression of the selected ARGs. Those ARGs whose expression was significantly different between histological grades were used for computing two new AR-Scores. HCC patients with higher AR expression showed a higher median overall survival (OS). AR-Score 21 and AR-Score-13 did not show any association with prognosis. Six of the 21 ARGs of the AR-Score-21 and 7 of the 13 ARGs of the AR-Score-13 showed a significant different expression profile among histological grades. Based on these differences, another two AR-Scores were computed (AR-Score-6 and AR-Score-7). They showed the relative increase of upregulated to downregulated ARGs in high-grade HCC. Higher AR activity inferred by these AR-Scores was associated with worse outcomes. The expression of AR is associated with a better prognosis in HCC. However, the activity of the AR seems to be qualitatively different among histological grades. The AR activity inferred by the shifted ARGs is associated with a worse prognosis in HCC patients.

DYNLL1 is hypomethylated and upregulated in a tumor stage- and grade-dependent manner and associated with increased mortality in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and cellular mechanisms regulating HCC pathogenesis and progression are not completely understood. DYNLL1 is essential for the development and expansion of MYC-driven B cell lymphoma, and also regulates genomic stability and responses to DNA-damaging chemotherapy in BRCA1-deficient tumors. However, the role and regulation of DYNLL1 has not been previously studied in the context of HCC. Here we report that DYNLL1 gene is hypomethylated and its expression is upregulated in HCC patients compared to healthy controls. The expression of DYNLL1 changes in a tumor grade- and stage-dependent manner in HCC. In this study, we further show that high DYNLL1 expression results in shorter overall and progression-free survival in hepatocellular carcinoma patients. Similar to DYNLL1, one of its protein interactors, RACK1, also shows decreased CpG-aggregated methylation and increased expression in HCC. RACK1 expression increases from early to late stage and from low to high grade in HCC. We found that high RACK1 expression is significantly associated with increased mortality of HCC patients. The present study shows that the epigenetic regulation of DYNLL1 and its consequent upregulation might be contributing to cancer development and progression in HCC. Its higher expression in late stage or high grade HCC may favor more aggressive disease as pointed by the increased mortality in high expression cohort. A better mechanistic understanding of the role of DYNLL1 in HCC will be needed to develop targeted treatment strategies in the future.

Comparative Study of Amide Proton Transfer Imaging and Intravoxel Incoherent Motion Imaging for Predicting Histologic Grade of Hepatocellular Carcinoma.

Background: Preoperative grading of hepatocellular carcinoma (HCC) is an important factor associated with prognosis after liver resection. The promising prediction of the differentiation of HCC remains a challenge. The purpose of our study was to investigate the value of amide proton transfer (APT) imaging in predicting the histological grade of HCC, compared with the intravoxel incoherent motion (IVIM) imaging.Methods: From September 2018 to February 2020, 88 patients with HCC were enrolled and divided into four groups (G1, G2, G3, and G4) based on the histologic grades. Preoperative APT signal intensity (SI), apparent diffusion coefficient (ADC), true molecular diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f ) of HCC were independently measured by two radiologists. The averaged values of those parameters were compared using an analysis of variance. The Spearman rank analysis was used to compare the correlation between those imaging parameters and the histological grades. Receiver operating characteristic (ROC) curve analysis was used to explore the predictive performance.Results: There were significant differences in APT SI, ADC, D, and f among the four grades of HCC (all P < 0.001). A moderate to good relationship was found between APT SI and the histologic grade of HCC (r = 0.679, P < 0.001). APT SI had an area under the ROC curve (AUC) of 0.890 (95% CI: 0.805–0.947) for differentiating low- from high-grade HCC, and the corresponding sensitivity and specificity were 85.71% and 82.05%, respectively. Comparison of ROC curves demonstrated that the AUC of APT SI was significantly higher than those of IVIM-derived parameter (Z = 2.603, P = 0.0092; Z = 2.099, P = 0.0358; Z = 4.023, P = 0.0001; Z = 2.435, P = 0.0149, compared with ADC, D, D*, and f , respectively). Moreover, the combination of both techniques further improved the diagnostic performance, with an AUC of 0.929 (95% CI: 0.854–0.973).Conclusion: APT imaging may be a potential noninvasive biomarker for the prediction of histologic grading of HCC and complements IVIM imaging for the more accurate and comprehensive characterization of HCC.

Amide Proton Transfer Imaging vs Diffusion Kurtosis Imaging for Predicting Histological Grade of Hepatocellular Carcinoma.

BackgroundTo investigate the value of amide proton transfer (APT) imaging in predicting the histological grade of hepatocellular carcinoma (HCC), compared with diffusion kurtosis imaging (DKI).MethodsA total of 88 patients with HCC were enrolled and divided into four groups (G1, G2, G3, and G4) based on histologic grades. Preoperative APT signal intensity (SI), mean diffusivity (MD), mean kurtosis (MK) of HCC were measured and compared. Those quantitative magnetic resonance imaging (qMRI) parameters were compared using an analysis of variance. The correlations between the qMRI parameters and the histological grades were determined using Spearman's rank analysis. In addition, the predictive performance for differentiating low- (G1 and G2) from high-grade (G3 and G4) HCC was evaluated using receiver operating characteristic (ROC) curve analysis.ResultsSignificant differences were found in APT SIs, MD, and MK among the four groups (P<0.05). Moderate to good relationships were found between the histologic grade of HCC and APT SI and MK (r=0.679, P<0.001 and r=0.539, P<0.001, respectively). The area under the ROC curves (AUCs) of APT SI, MK, and MD for differentiating low- from high-grade HCC were 0.890 (95%CI: 0.805–0.947), 0.765 (95%CI: 0.662–0.849) and 0.717 (95%CI: 0.611–0.808), respectively. Comparison of ROC curves showed a significantly higher AUC of APT SI compared with those of the DKI-derived parameters (P <0.05).ConclusionThe APT imaging may be more accurate than DKI for predicting the histological grade of HCC.

Can LI-RADS imaging features at gadoxetic acid-enhanced MRI predict aggressive features on pathology of single hepatocellular carcinoma?

PurposeTo investigate whether Liver Imaging Reporting and Data System (LI-RADS) imaging features at preoperative gadoxetic acid-enhanced MRI can predict microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) and to evaluate their associations with recurrence after curative resection of single HCC. Materials and methodsFrom July 2015 to September 2018, 111 consecutive patients with pathologically confirmed HCC who underwent gadoxetic acid–enhanced MRI within 1 month before surgery were included in this retrospective study. Significant MRI findings and clinical parameters for predicting MVI, high-grade HCCs and postoperative recurrence were identified by logistic regression model and Cox proportional hazards model. ResultsTwenty-six of 111 (23.4 %) patients had MVI and 36 of 111 (32.4 %) patients had high-grade HCCs, whereas 44 of 95 (46.3 %) patients experienced recurrence. Tumor size > 5 cm (OR = 9.852; p < 0.001) and absence of nodule-in-nodule architecture (OR = 8.302; p = 0.001) were independent predictors of MVI. Enhancing capsule (OR = 4.396; p = 0.004) and corona enhancement (OR = 3.765; p = 0.021) were independent predictors of high-grade HCCs. Blood products in mass (HR = 2.275; p = 0.009), corona enhancement (HR = 4.332; p < 0.001), and serum AFP level > 400 ng/mL (HR = 2.071; p = 0.023) were independent predictors of recurrence. ConclusionLI-RADS imaging features can be used as potential biomarkers for predicting aggressive pathologic features and recurrence of HCC. The identification of prognostic LI-RADS imaging features may facilitate the selection of surgical candidates and optimize the management of HCC patients.