Histological Grade Of Hepatocellular Carcinoma Research Articles

Prediction of histologic grade of hepatocellular carcinoma using dual-layer spectral-detector computed tomography (CT): comparison of two region of interest plotting methods

Prediction of histologic grade of hepatocellular carcinoma using dual-layer spectral-detector computed tomography (CT): comparison of two region of interest plotting methods

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.

Lnc-uc.147 Is Associated with Disease Stage of Liver, Gastric, and Renal Cancer.

Lnc-uc.147, a long non-coding RNA derived from a transcribed ultraconserved region (T-UCR), was previously evidenced in breast cancer. However, the role of this region in other tumor types was not previously investigated. The present study aimed to investigate lnc-uc.147 in different types of cancer, as well as to suggest lnc-uc.147 functional and regulation aspects. From solid tumor datasets analysis of The Cancer Genome Atlas (TCGA), deregulated lnc-uc.147 expression was associated with the histologic grade of hepatocellular carcinoma, and with the tumor stage of clear cell renal and gastric adenocarcinoma. Considering the epidemiologic relevance of liver cancer, silencing lnc-uc.147 reduced the viability and clonogenic capacity of HepG2 cell lines. Additionally, we suggest a relation between the transcription factor TEAD4 and lnc-uc.147 in liver and breast cancer cells.

DDX1 is a prognostic biomarker and correlates with immune infiltrations in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading lethal malignant tumors worldwide. DEAD-box (DDX) family helicases are implicated in numerous human cancers. However, the role of DDX1 in HCC has not yet been fully elucidated. We downloaded gene expression data and clinical information data of HCC from The Cancer Genome Atlas and International Cancer Genome Consortium (ICGC) database and conducted subsequent analyses using the R package and online portal. The results revealed that HCC tissues had higher DDX1 expression compared with either paired or unpaired normal tissues. The increased DDX1 expression was closely related to the advanced pathological grade and histologic grade of HCC. Further analysis suggested that patients with high DDX1 expression contributed to poor prognosis The Cox regression analysis revealed that the expression level of DDX1 was an independent prognostic factor for HCC. In addition, an ICGC cohort was used for external validation. The cBio-Portal, MethSurv, and UALCAN database were used for evaluating the genomic mechanism. Moreover, the Tumor Immune Estimation Resource dataset and QUANTISEQ algorithm revealed that DDX1 expression positively correlates with immune infiltrating cells. We also identified the DDX1-related differentially expressed genes (DEGs) and explored their biological functions by GO, KEGG, and GSEA analyses, which indicated that DDX1 may regulate the progression of HCC. In general, increased DDX1 expression predicts a poor prognosis and drives the progression of HCC.

Kupffer Phase Radiomics Signature in Sonazoid-Enhanced Ultrasound is an Independent and Effective Predictor of the Pathologic Grade of Hepatocellular Carcinoma.

We conduct this study to investigate the value of Kupffer phase radiomics signature of Sonazoid-enhanced ultrasound images (SEUS) for the preoperative prediction of hepatocellular carcinoma (HCC) grade. From November 2019 to October 2021, 68 pathologically confirmed HCC nodules from 54 patients were included. Quantitative radiomic features were extracted from grayscale images and arterial and Kupffer phases of SEUS of HCC lesions. Univariate logistic regression and the maximum relevance minimum redundancy (MRMR) method were applied to select radiomic features best corresponding to pathological results. Prediction radiomic signature was calculated using each of the image types. A predictive model was validated using internal leave-one-out cross validation (LOOCV). For discrimination between poorly differentiated HCC (p-HCC) and well-differentiated HCC/moderately differentiated HCC (w/m-HCC), the Kupffer phase radiomic score (KPRS) achieved an excellent area under the curve (AUC = 0.937), significantly higher than the other two radiomic signatures. KPRS was the best radiomic score based on the highest AUC (AUC = 0.878), which is prior to gray and arterial RS for differentiation between w-HCC and m/p-HCC. Univariate and multivariate analysis incorporating all radiomic signatures and serological variables showed that KPRS was the only independent predictor in both predictions of HCC lesions (p-HCC vs. w/m-HCC, log OR 15.869, P < 0.001, m/p-HCC vs. w-HCC, log OR 12.520, P < 0.05). We conclude that radiomics signature based on the Kupffer phase imaging may be useful for identifying the histological grade of HCC. The Kupffer phase radiomic signature may be an independent and effective predictor in discriminating w-HCC and p-HCC.

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.

Histopathology of hepatocellular carcinoma - when and what

When do you need to take biopsies of the liver, and what information will you get is the topic of this review on hepatocellular carcinoma (HCC). If, clinically, the differential diagnosis of HCC after imaging is suggested, a biopsy has become obligatory as a diagnostic confirmation of HCC in the non-cirrhotic liver prior to definitive therapeutic interventions, as well as in a palliative therapy concept. In the case of hepatic lesions with an uncharacteristic contrast uptake, a biopsy should be performed immediately to confirm the diagnosis of HCC. After diagnosing HCC, a treatment strategy is evaluated. Further, the biopsy, or in case of surgical treatment, the resected tissue, shows us the different subtypes of HCC, with the steatohepatitic subtype being the most common and the lymphocyte-rich subtype being the least common. Further, the histological grade of HCC is determined according to the grading system of the WHO or the Edmonson and Steiner System. Through biopsies, HCC can be differentiated from intrahepatic cholangiocarcinoma or combined hepatocellular-cholangiocarcinoma or metastases of other malignant tumors, especially metastases of the gastrointestinal tract. In summary, biopsies are fundamental in the diagnosis of HCC.

Identification and Validation of Hub Genes Predicting Prognosis of Hepatocellular Carcinoma

Introduction: The aim of this study is selecting the hub genes associated with hepatocellular carcinoma (HCC) to construct a Cox regression model for predicting prognosis in HCC patients. Methods: Using HCC patient data from the ICGC and TCGA databases, screened for 40 core genes highly correlated with histological grade of HCC. Univariate and multivariate Cox regression analyses were performed on the genes highly associated with HCC prognosis, and the model was established. The expression of those genes was measured by immunohistochemistry in 110 HCC patients who underwent the surgery in the First Affiliated Hospital of Wenzhou Medical University. The survival of HCC patients was analyzed by the Kaplan-Meier method. Results: Eight genes (CDC45, CENPA, MCM10, MELK, CDC20, ASF1B, FANCD2, and NCAPH) were correlated with prognosis, and the same result was observed in 110 HCC patients. Using the regression model, the HCC patients in the training set were classified as high- and low-risk groups. The overall survival of patients in the high-risk group was shorter than that in the low-risk group, and the same results were obtained in the verification set. Conclusion: This study found that the risk model according to these 8 genes can be used as a predictor of prognosis in HCC. These genes may become alternative biomarkers and therapeutic targets and provide new therapeutic strategies for HCC.

A Reliable Prognostic Model for HCC Using Histological Grades and the Expression Levels of Related Genes.

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and is a leading cause of cancer-related death worldwide. This study aimed to establish a reliable prognostic model for HCC using histological grades and the expression levels of related genes. The histological grade of a tumor provides prognostic information. The expression data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) database. We employed the univariate and multivariate Cox regression analyses, as well as the least absolute shrinkage and selection operator (LASSO) regression to establish the prognostic model. After verification of the proposed model using data downloaded from the International Cancer Genome Consortium (ICGC) database, we found that the model was highly reliable, and it was revealed that the prognosis in the high-risk group was significantly worse than that in the low-risk group. Next, we explored the correlation of RiskScore with patients' clinicopathological characteristics, and we found that the RiskScore could be used as an independent prognostic factor, which further confirmed the reliability of our model. In summary, the proposed model could accurately predict the prognosis of HCC patients, assisting clinicians to study the roles of different histological grades of HCC.

Diagnostic accuracy of 3D magnetic resonance elastography for assessing histologic grade of hepatocellular carcinoma: comparison of three methods for positioning region of interest.

To assess the influence of region of interest (ROI) placement on the predictive value of 3D MRE in differentiating the histologic grade of HCC. 85 patients with pathologically confirmed HCCs were analyzed using 3D MRE imaging, two radiologists measured the tumor stiffness with three different ROI positioning methods. Intraclass correlation coefficient (ICC) was expressed in terms of inter- and intra-observer agreements. Kruskal-Wallis rank test or one-way ANOVA was used to compare the difference in MRE stiffness across the three-ROI positioning methods. Receiver operating characteristic curve analysis (ROC) was performed, and the area under curve (AUC) was measured to evaluate the diagnostic performance. There were 64 (75%) well-or-moderately differentiated HCCs and 21(25%) poorly differentiated HCCs included finally. Almost excellent inter- and intra-observer agreements (all ICC > 0.82) were observed for all three-ROI methods, the volumetric method has the highest values (inter-observer ICC 0.967, intra-observer ICC 0.919, 0.926, respectively). The mean stiffnesses of poorly differentiated HCC obtained by two readers were significantly higher than well-or-moderately differentiated HCC with volumetric method (7.07 ± 1.57 Kpa, 5.00 ± 1.49 Kpa, and 6.85 ± 1.49 Kpa, 4.94 ± 1.48 Kpa, respectively) and three-ROI method (6.14 ± 1.71 Kpa, 4.91 ± 1.56 Kpa and 5.94 ± 1.61 Kpa, 4.84 ± 1.54 Kpa, respectively) but not on single-ROI method (p > 0.005), for the diagnostic performance, the highest area under the curve (AUC) with a value of 0.837, 0.812 by using the volumetric method, followed by the three-ROI method (0.713, 0.754) and single-ROI method. Different ROI positioning methods significantly affect HCC tumor stiffness measurements. The whole tumor volumetric analysis is superior to ROI-based methods for predicting the grade of HCC.

Value of perfusion parameters and histogram analysis of triphasic computed tomography in pre-operative prediction of histological grade of hepatocellular carcinoma.

Background:Pre-operative non-invasive histological evaluation of hepatocellular carcinoma (HCC) remains a challenge. Tumor perfusion is significantly associated with the development and aggressiveness of HCC. The purpose of the study was to evaluate the clinical value of quantitative liver perfusion parameters and corresponding histogram parameters derived from traditional triphasic enhanced computed tomography (CT) scans in predicting histological grade of HCC.Methods:Totally, 52 patients with HCC were enrolled in this retrospective study and underwent triple-phase enhanced CT imaging. The blood perfusion parameters were derived from triple-phase CT scans. The relationship of liver perfusion parameters and corresponding histogram parameters with the histological grade of HCC was analyzed. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal ability of the parameters to predict the tumor histological grade.Results:The variance of arterial enhancement fraction (AEF) was significantly higher in HCCs without poorly differentiated components (NP-HCCs) than in HCCs with poorly differentiated components (P-HCCs). The difference in hepatic blood flow (HF) between total tumor and total liver flow (ΔHF = HFtumor − HFliver) and relative flow (rHF = ΔHF/HFliver) were significantly higher in NP-HCCs than in P-HCCs. The difference in portal vein blood supply perfusion (PVP) between tumor and liver tissue (ΔPVP) and the ΔPVP/liver PVP ratio (rPVP) were significantly higher in patients with NP-HCCs than in patients with P-HCCs. The area under ROC (AUC) of ΔPVP and rPVP were both 0.697 with a high sensitivity of 84.2% and specificity of only 56.2%. The ΔHF and rHF had a higher specificity of 87.5% with an AUC of 0.681 and 0.673, respectively. The combination of rHF and rPVP showed the highest AUC of 0.732 with a sensitivity of 57.9% and specificity of 93.8%. The combined parameter of ΔHF and rPVP, rHF and rPVP had the highest positive predictive value of 0.903, and that of rPVP and ΔPVP had the highest negative predictive value of 0.781.Conclusion:Liver perfusion parameters and corresponding histogram parameters (including ΔHF, rHF, ΔPVP, rPVP, and AEFvariance) in patients with HCC derived from traditional triphasic CT scans may be helpful to non-invasively and pre-operatively predict the degree of the differentiation of HCC.

Protein kinase N1 promotes proliferation and invasion of liver cancer.

Protein kinase (PK) N1, also called PKC-related protein 1, participates in the proliferation, invasion and metastasis of various malignant tumors. However, the role of PKN1 in liver cancer remains to be elucidated. The present study investigated the expression of PKN1 using immunohistochemistry in surgical specimens from 36 patients and analyzed the correlation with VEGF, microvascular density (MVD), cell proliferation index (Ki67) and clinicopathological parameters. PKN1 was highly expressed in hepatocellular carcinoma (HCC) and was positively correlated with histological grading of HCC, Ki67 expression and MVD. PKN1 expression in moderately and poorly differentiated HCC was significantly higher compared with highly differentiated HCC. Expression of PKN1 was positively correlated with Ki67 and MVD, and Ki67 expression was positively correlated with MVD. The effects of PKN1 on proliferation, invasion and apoptosis of liver cancer cells were detected in vitro. Cell viability, migration and invasion were reduced and the apoptosis rate was significantly improved when PKN1 expression was silenced in liver cancer cells. Thus, PKN1 serves an important role in the development and progression of liver cancer. Inhibition of PKN1 activity may provide a promising therapeutic target for liver cancer.

Overexpression of GINS4 Is Associated With Tumor Progression and Poor Survival in Hepatocellular Carcinoma.

PurposeOur research was aimed to identify the expression, clinical value and biological significance of GINS complex subunit 4 (GINS4) in hepatocellular carcinoma (HCC).Materials and MethodsGINS4 was initially screened through weighted gene co-expression network analysis (WGCNA). The TCGA, GEO, and TIMER databases were applied for analyzing the GINS4 mRNA expression in HCC. GINS4 protein levels were detected via immunohistochemistry (IHC). Receiver operating characteristic (ROC) curve was applied for estimating the diagnostic significance of GINS4 in HCC. Kaplan-Meier plots, Cox model, and nomogram were used to assess the prognostic performance of GINS4 in HCC. Nomogram validation was conducted through time-dependent ROC and decision curve analysis (DCA). The Wanderer, UALCAN, and DiseaseMeth databases were utilized to identify GINS4 methylation levels in HCC. Genes co-expressed with GINS4 in HCC were estimated through the TCGA, cBioPortal, and GEPIA. GO, KEGG, and GSEA unraveled the possible biological mechanisms of GINS4 in HCC.ResultsWGCNA confirmed that GINS4 was one of hub genes significantly associated with histological grade of HCC. Multiple databases confirmed the significant upregulation of GINS4 in HCC tissues compared with non-tumor controls. IHC analysis of 35 HCC patients demonstrated that overexpressed GINS4 positively correlated with advanced TNM stage and poor pathological differentiation. GINS4 could effectively differentiate HCC cases from healthy individuals, with an AUC of 0.865. Increased GINS4 expression predicted unsatisfactory prognosis in HCC patients, especially in age >60 years, histological grade 1, HBV infection-negative, and occurring relapse subgroup. Nomogram incorporating GINS4 level and TNM stage displayed satisfactory predictive accuracy and clinical utility in predicting HCC prognosis. Upregulated GINS4 exhibited hypomethylated levels in HCC. Functional analysis indicated that GINS4 potentially positively modulated cell cycle and PI3K/AKT/mTOR pathway.ConclusionGINS4 is overexpressed in HCC and is correlated with undesirable survival of HCC patients.

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.

Amide proton transfer-weighted MRI for predicting histological grade of hepatocellular carcinoma: comparison with diffusion-weighted imaging.

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver, preoperative grading of HCC is of great clinical significance. Amide proton transfer-weighted (APTw) imaging, as a novel contrast mechanism in the field of molecular imaging, provided new diagnostic ideas for the grading of HCC. Between May 2017 and April 2018, 32 consecutive patients with pathologically confirmed HCC were enrolled, including 19 high-grade HCCs and 13 low-grade HCCs. DWI and APTw scanning was performed on a 3T MRI scanner. Two observers drew regions of interest independently by referring to the axial T2-weighted imaging, and APTw and apparent diffusion coefficient (ADC) values were obtained. Inter- and intra-observer agreements were assessed with the intraclass correlation coefficients (ICCs). The independent sample t test was used to compare the APTw and ADC values between the high- and low-grade HCC tumor parenchyma. The receiver operating characteristic curve was used to analyze the diagnostic efficacy of high- from low-grade HCC tumors. Spearman correlation analysis was used to assess the relationship between APTw and ADC values and HCC histological grades. There were significant differences between the APTw or ADC values for the high- and low-grade HCCs (P=0.034 and 0.010). Both APTw and DWI had good diagnostic performance in differentiating the high- from the low-grade HCCs, with areas under the curves of 0.814 and 0.745, respectively. Moderate correlations existed between APTw values and histological grades (r=0.534; P=0.002), as well as ADC values and histological grades (r=-0.417; P=0.018). The APTw imaging is a useful imaging biomarker that complements DWI for the more accurate and comprehensive HCC characterization.

Role of Intravoxel Incoherent Motion in Discriminating Hepatitis B Virus-Related Intrahepatic Mass-Forming Cholangiocarcinoma from Hepatocellular Carcinoma Based on Liver Imaging Reporting and Data System v2018.

Backgroud: Intravoxel incoherent motion (IVIM) could be used to characterize benign and malignant hepatic lesions and predict the histological grade of hepatocellular carcinoma (HCC). To evaluate IVIM-derived parameters for differentiating between hepatitis B virus (HBV)-related intrahepatic mass-forming cholangiocarcinoma (IMCC) and HCC based on the Liver Imaging Reporting and Data System (LI-RADS) v2018. Materials and Methods: 20 IMCC patients and one-to-one matched control HCC patients were retrospectively assessed. IVIM scanning with 11 b-values (from 0 to 1500 s/mm2) was obtained using a 3.0-T magnetic resonance scanner. Apparent diffusion coefficient (ADC) and IVIM parameters, including diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f), were compared between IMCC and HCC. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performances of ADC, D, f, and D*. The LI-RADS features and a final category were also compared using LI-RADS v2018. Results: ADC and D were significantly higher in IMCC than in HCC (p = 0.012 and p = 0.007, respectively); f was significantly higher in HCC than in IMCC (p = 0.004). The area under the ROC curve values for ADC, D, and f for differentiating HBV-related IMCC from HCC were 0.724, 0.753, and 0.741, respectively. Conclusion: The majority of HBV-related IMCCs can be categorized as LR-M by using LI-RADS. However, atypical IMCCs may be classified as non-LR-M. ADC, D, and f values may be helpful in differentiating HBV-related IMCC from HCC, and similar diagnostic performances were obtained for these values.

Hepatocellular carcinoma grading and recurrence prediction using T1 mapping on gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging.

The aim of the present study was to explore the value of T1 mapping on gadolinium-ethoxybenzyl diethylenetriamine pentaacetic (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for grading hepatocellular carcinoma (HCC) and predicting its recurrence rate. A retrospective study was performed that included 75 patients (66 men and 9 women; mean age, 52.89 years; age range, 23–79 years) with HCC who had undergone Gd-EOB-DTPA-enhanced MRI with T1 mapping before surgery. The T1 relaxation time of the 81 lesions and non-tumorous liver parenchyma in 75 patients with HCC were measured before Gd-EOB-DTPA was injected and then at 5, 10 and 20 min after administration, respectively. T1[lesion (L)-hepatic parenchyma (H)]/H (%) was calculated as the increment rate of the T1 value in the lesions relative to the non-tumorous liver parenchyma. One-way analysis of variance and Spearman's correlation analysis was used to compare the differences and relationship of T1 mapping values among the three grades of HCC. A total of 81 lesions were divided into well-differentiated HCC (grades I; n=21), moderately differentiated HCC (grades II; n=40) and poorly differentiated HCC (grades III; n=20) according to the histopathology. The T1(L-H)/H (%) value among grades I, II and III HCC on pre-contrast results and on post-contrast results at the 5-, 10- and 20-min hepatobiliary phase (HBP) were significantly different (P<0.05), and T1(L-H)/H (%) was correlated with the histological grade of HCC at each time point (r=0.637, r=0.554, r=0.499 and r=0.560, respectively, P<0.001). A total of 41 recurrence cases [grade I (n=5), grade II (n=23) and grade III (n=13)] were verified by imaging (CT, MRI or ultrasound) or reoperation. Patients with grade III and grade II HCC had higher recurrence rates compared with that in patients with grade I HCC (P<0.05; median recurrence times were 258 days, 605 days and undefined, respectively). According to the optimal cut-off point for the T1(L-H)/H (%) of the three grades of HCC, patients with HCC in the low T1(L-H)/H (%) value group (≤155.15%) had lower cumulative recurrence rates compared with that in the medium (T1(L-H)/H (%) >155.15% and T1(L-H)/H (%) ≤241.20%) and high (T1(L-H)/H (%) >241.20%) value groups at the 20-min HBP (P<0.05; median recurrence times were undefined, 530 days and 447 days, respectively). These results indicate that the parameters of T1 mapping would be beneficial for predicting the grading and recurrence of HCC.

Whole-lesion histogram analysis metrics of the apparent diffusion coefficient: a correlation study with histological grade of hepatocellular carcinoma.

The study evaluated the relationship between the histological grade of hepatocellular carcinoma (HCC) and the histogram-derived parameters of apparent diffusion coefficient (ADC) obtained from the whole-lesion assessment of diffusion-weighted magnetic resonance (MR) imaging in the liver. A total of 51 patients were included. The parameters were correlated with the Edmondson-Steiner grades by using the Spearman correlation coefficient (ρ). The differences of ADC parameters between different tumor histological grades were compared using the Mann-Whitney U test. The extent to which each parameter aided in differentiating tumors with poor performance (III, IV) and fair performance (I, II) was assessed by using the area under the receiver operating characteristic curve (Az). The 25th percentile ADC exhibits the most negative correlation with histological grade (ρ = - 0.397), followed by the 30th percentile ADC (ρ = - 0.395), the minimum ADC value (ρ = - 0.390) and the 20th percentile ADC (ρ = - 0.385), whereas the minimum ADC value yielded the highest Az (0.763) in the discrimination of tumor foci with poor differentiation from fairly differentiated HCCs. The minimum ADC of 4.15 × 10-3 mm2/s or lower was considered to indicate poorly differentiated performance, and the corresponding sensitivity and specificity were 66.7 and 90.9%, respectively. The 25th percentile ADC showed a stronger correlation with the histological grade of HCC than other ADC parameters, and the minimum ADC value might be an optimal metric for determining poor and fair differentiations of HCC in DWI.