Differential Diagnosis Of Hepatocellular Carcinoma Research Articles

Dynamic contrast enhanced ultrasound in differential diagnosis of hepatocellular carcinoma: A systematic review and meta-analysis.

Non-invasive differential diagnosis between hepatocellular carcinoma (HCC) and other liver cancer (i.e. cholangiocarcinoma or metastasis) is highly challenging and definitive diagnosis still relies on histological exam. The patterns of enhancement and wash-out of liver nodules can be used to stratify the risk of malignancy only in cirrhotic patients and HCC frequently shows atypical features. Dynamic contrast-enhanced ultrasound (DCEUS) with standardized software could help to overcome these obstacles, providing functional and quantitative parameters and potentially improving accuracy in the evaluation of tumor perfusion. To explore clinical evidence regarding the application of DCEUS in the differential diagnosis of liver nodules. A comprehensive literature search of clinical studies was performed to identify the parameters of DCEUS that could relate to histological diagnosis. In accordance with the study protocol, a qualitative and quantitative analysis of the evidence was planned. Rise time was significantly higher in HCC patients with a standardized mean difference (SMD) of 0.83 (95%CI: 0.48-1.18). Similarly, other statistically significant parameters were mean transit time local with a SMD of 0.73 (95%CI: 0.20-1.27), peak enhancement with a SMD of 0.37 (95%CI: 0.03-0.70), area wash-in area under the curve with a SMD of 0.47 (95%CI: 0.13-0.81), wash-out area under the curve with a SMD of 0.55 (95%CI: 0.21-0.89) and wash-in and wash-out area under the curve with SMD of 0.51 (95%CI: 0.17-0.85). SMD resulted not significant in fall time and wash-in rate, but the latter presented a trend towards greater values in HCC compared to intrahepatic cholangiocarcinoma. DCEUS could improve non-invasive diagnosis of HCC, leading to less liver biopsy and early treatment. This quantitative analysis needs to be applied on larger cohorts to confirm these preliminary results.

Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma

ObjectiveTo investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and non-hepatocellular carcinoma (non-HCC) based on CT and MR multiphase radiomics combined with different machine learning models and compare the diagnostic efficacy between different radiomics models.BackgroundPrimary liver cancer is one of the most common clinical malignancies, hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, accounting for approximately 90% of cases. A clear diagnosis of HCC is important for the individualized treatment of patients with HCC. However, more sophisticated diagnostic modalities need to be explored.MethodsThis retrospective study included 211 patients with liver lesions: 97 HCC and 124 non-hepatocellular carcinoma (non-HCC) who underwent CT and MRI. Imaging data were used to obtain imaging features of lesions and radiomics regions of interest (ROI). The extracted imaging features were combined to construct different radiomics models. The clinical data and imaging features were then combined with radiomics features to construct the combined models. Support Vector Machine (SVM), K-nearest Neighbor (KNN), RandomForest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP) six machine learning models were used for training. Five-fold cross-validation was used to train the models, and ROC curves were used to analyze the diagnostic efficacy of each model and calculate the accuracy rate. Model training and efficacy test were performed as before.ResultsStatistical analysis showed that some clinical data (gender and concomitant cirrhosis) and imaging features (presence of envelope, marked enhancement in the arterial phase, rapid contouring in the portal phase, uniform density/signal and concomitant steatosis) were statistical differences (P < 0.001). The results of machine learning models showed that KNN had the best diagnostic efficacy. The results of the combined model showed that SVM had the best diagnostic efficacy, indicating that the combined model (accuracy 0.824) had better diagnostic efficacy than the radiomics-only model.ConclusionsOur results demonstrate that the radiomic features of CT and MRI combined with machine learning models enable differential diagnosis of HCC and non-HCC (malignant, benign). The diagnostic model with dual radiomic had better diagnostic efficacy. The combined model was superior to the radiomic model alone.

Differential diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma by ultrasonography combined with multiphase enhanced computed tomography.

Purpose: Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are primary liver cancers with different therapeutic methods and prognoses. This study aims to investigate the ultrasonography and enhanced computed tomography (CT) features of these cancers and improve the early diagnosis rate. Methods: We retrospectively analyzed the clinical and imaging data of 319 patients diagnosed with HCC and 124 patients diagnosed with ICC, confirmed by pathology. Results: A total of 443 patients were eligible in this study. From the perspective of clinical data, between HCC and ICC patients existed significant differences in age, gender, hepatic background, serum tumor markers of AFP and CA19.9, chronic hepatitis B/C and lymph node infiltration (p<0.05), but not in tumor size, microvascular invasion, serum tumor markers of CEA and CA125 (P>0.05). With respect to ultrasonography features, HCC patients had a higher proportion than ICC patients in splenomegaly (p=0.001), while ICC patients had a higher proportion than HCC patients in absence/not rich vascularity and intrahepatic bile duct dilatation (p<0.05). With respect to CT features, HCC patients were significantly different from ICC patients in the three-phase enhanced CT value mean, enhanced intensity and homogeneity of nodules (P<0.05). A multivariate logistic regression analysis was performed to further clarify the correlation of these indices. However, only age≤60 years (OR=1.861, P=0.045), male (OR=3.850, P<0.001), AFP>7ng/ml (OR=0.119, P<0.001), lymph node infiltration (OR=5.968, P<0.001), intrahepatic bile duct dilatation (OR=2.414, P=0.04), splenomegaly (OR=0.081, P<0.001), rim APHE (OR=3.109, P=0.002), and iso- or hyper enhancement (OR=0.188, P<0.001) were independent risk factors. Conclusions: While there are overlapping ultrasonography and CT features between HCC and ICC, the integration of tumor markers and specific imaging characteristics can be beneficial in distinguishing between the two.

The Clinical Value of Multimodal Ultrasound for the Differential Diagnosis of Hepatocellular Carcinoma from Other Liver Tumors in Relation to Histopathology.

Recent advances in the field of ultrasonography offer promising tools for the evaluation of liver tumors. We aim to assess the value of multimodal ultrasound in differentiating hepatocellular carcinomas (HCCs) from other liver lesions. We prospectively included 66 patients with 72 liver tumors. The histological analysis was the reference standard for the diagnosis of malignant liver lesions, and partially for benign tumors. All liver lesions were assessed by multiparametric ultrasound: standard ultrasound, contrast-enhanced ultrasound (CEUS), the point shear wave elastography (pSWE) using shear wave measurement (SWM) method and real-time tissue elastography (RTE). To diagnose HCCs, CEUS achieved a sensitivity, specificity, accuracy and positive predictive value (PPV) of 69.05%, 92.86%, 78.57% and 93.55%, respectively. The mean shear-wave velocity (Vs) value in HCCs was 1.59 ± 0.29 m/s, which was lower than non-HCC malignancies (p < 0.05). Using a cut-off value of 1.58 m/s, SWM achieved a sensitivity of 54.76%, and 82.35% specificity, for differentiating HCCs from other malignant lesions. The combination of SWM and CEUS showed higher sensitivity (79.55%) compared with each technique alone, while maintaining a high specificity (89.29%). In RTE, most HCCs (61.53%) had a mosaic pattern with dominant blue areas corresponding to type "c" elasticity. Elasticity type "c" was 70.59% predictive for HCCs. In conclusion, combining B-mode ultrasound, CEUS, pSWE and RTE can provide complementary diagnostic information and potentially decrease the requirements for other imaging modalities.

Applications of Dynamic Contrast-Enhanced Ultrasound in Differential Diagnosis of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma in Non-cirrhotic Liver.

The aim of the work described here was to investigate the value of dynamic contrast enhanced ultrasound (DCE-US) and quantitative analysis in pre-operative differential diagnosis of intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) in non-cirrhotic liver. In this retrospective study, patients with histopathologically proven ICC and HCC lesions in non-cirrhotic liver were included. All patients underwent contrast-enhanced ultrasound (CEUS) examinations with an Acuson Sequoia unit (Siemens Healthineers, Mountain View, CA, USA) unit or LOGIQ E20 (GE Healthcare, Milwaukee, WI, USA) within 1 wk before surgery. SonoVue (Bracco, Milan, Italy) was used as the contrast agent. B-mode ultrasound (BMUS) features and CEUS enhancement patterns were analyzed. DCE-US analysis was performed by VueBox software (Bracco). Two regions of interest (ROIs) were set in the center of the focal liver lesions and their surrounding liver parenchyma. Time-intensity curves (TICs) were generated, and quantitative perfusion parameters were obtained and compared between the ICC and HCC groups using the Student t-test or Mann-Whitney U-test. From November 2020 to February 2022, patients with histopathologically confirmed ICC (n=30) and HCC (n=24) lesions in non-cirrhotic liver were included. During the arterial phase (AP) of CEUS, ICC lesions exhibited heterogeneous hyperenhancement (13/30, 43.3%), heterogeneous hypo-enhancement (2/30, 6.7 %) and rim-like hyperenhancement (15/30, 50.0%), whereas all HCC lesions exhibited heterogeneous hyperenhancement (24/24, 100.0%) (p < 0.05). Subsequently, most of the ICC lesions exhibited AP wash-out (83.3%, 25/30), whereas a few cases exhibited wash-out in the portal venous phase (PVP) (15.7%, 5/30). In contrast, HCC lesions exhibited AP wash-out (41.7%, 10/24), PVP wash-out (41.7%, 10/24) and a small part of late phase wash-out (16.7%, 4/24) (p < 0.05). Compared with those of HCC lesions, TICs of ICCs revealed earlier and lower enhancement during the AP, faster decline during the PVP and reduced area under the curve. The combined area under the receiver operating characteristic curve (AUROC) of all significant parameters was 0.946, with 86.7% sensitivity, 95.8% specificity and 90.7% accuracy in differential diagnosis between ICC and HCC lesions in non-cirrhotic liver, which improved the diagnostic efficacy of CEUS (58.3% sensitivity, 90.0% specificity and 75.9% accuracy). ICC and HCC lesions in non-cirrhotic liver might exhibit some overlap of CEUS features in diagnosis. DCE-US with quantitative analysis would be helpful in pre-operative differential diagnosis.

Differential diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on spatial and channel attention mechanisms.

The pre-operative non-invasive differential diagnosis of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) mainly depends on imaging. However, the accuracy of conventional imaging and radiomics methods in differentiating between the two carcinomas is unsatisfactory. In this study, we aimed to establish a novel deep learning model based on computed tomography (CT) images to provide an effective and non-invasive pre-operative differential diagnosis method for HCC and ICC. We retrospectively investigated the CT images of 395 HCC patients and 99 ICC patients who were diagnosed based on pathological analysis. To differentiate between HCC and ICC we developed a deep learning model called CSAM-Net based on channel and spatial attention mechanisms. We compared the proposed CSAM-Net with conventional radiomic models such as conventional logistic regression, least absolute shrinkage and selection operator regression, support vector machine, and random forest models. With respect to differentiating between HCC and ICC, the CSAM-Net model showed area under the receiver operating characteristic curve (AUC) values of 0.987 (accuracy = 0.939), 0.969 (accuracy = 0.914), and 0.959 (accuracy = 0.912) for the training, validation, and test sets, respectively, which were significantly higher than those of the conventional radiomics models (0.736-0.913 [accuracy = 0.735-0.912], 0.602-0.828 [accuracy = 0.647-0.818], and 0.638-0.845 [accuracy = 0.618-0.849], respectively. The decision curve analysis showed a high net benefit of the CSAM-Net model, which suggests potential efficacy in differentiating between HCC and ICC in the diagnosis of liver cancers. The proposed CSAM-Net model based on channel and spatial attention mechanisms provides an effective and non-invasive tool for the differential diagnosis of HCC and ICC on CT images, and has potential applications in diagnosis of liver cancers.

A correlative study between IVIM-DWI parameters and VEGF and MMPs expression in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related death worldwide. Angiogenic factors may be valuable indices of tumor recurrence and treatment and potentially useful markers for predicting the response to antiangiogenesis therapy. Vascular endothelial growth factor (VEGF) and matrix metalloproteinases (MMPs) are major drivers of tumor angiogenesis. Preoperatively predicting the expression of VEGF and MMPs is crucial for treating HCC. Intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has been successfully used in the differential diagnosis of HCC, pathological grading, and treatment response evaluation. However, the correlations between IVIM-DWI parameters and VEGF and MMP expression have not been reported. This study provides a preliminary analysis of the correlation between IVIM-DWI parameters and the expression of VEGF, MMP-2, and MMP-9 to investigate the value of IVIM-DWI in the noninvasive evaluation of angiogenesis in HCC. IVIM-DWI was performed in 61 patients with HCC 1 week before they underwent surgical resection. VEGF, MMP-2, and MMP-9 expression was detected using immunohistochemistry staining. Spearman correlation analysis was used to analyze the correlations between the IVIM-DWI parameters and VEGF, MMP-2, and MMP-9 expression in HCC. The fast apparent diffusion coefficient fraction (f) value was positively correlated with the expression of VEGF (P<0.001), MMP-2 (P=0.002), and MMP-9 (P<0.001). The fast apparent diffusion coefficient (D*) was positively correlated with VEGF (P<0.001) and MMP-9 (P<0.001) expression but was not correlated with MMP-2 (P=0.659) expression. The apparent diffusion coefficient (ADC) and slow apparent diffusion coefficient (D) values were not significantly correlated with the expression of VEGF (P=0.103 and P=0.543, respectively), MMP-2 (P=0.596 and P=0.338, respectively), or MMP-9 (P=0.102 and P=0.660, respectively). IVIM-DWI can be used to noninvasively evaluate angiogenesis in HCC.

The Value of Contrast-Enhanced Magnetic Resonance Imaging Enhancement in the Differential Diagnosis of Hepatocellular Carcinoma and Combined Hepatocellular Cholangiocarinoma.

Background The distinction between combined hepatocellular-cholangiocarcinoma (cHCC-CC) and hepatocellular carcinoma (HCC) before the operation has an important clinical significance for optimizing the treatment plan and predicting the prognosis of patients. Magnetic resonance imaging (MRI) has been widely used in the preoperative diagnosis and evaluation of primary liver malignant tumors. Purpose The aim is to study the value of preoperative clinical data and enhanced MRI in the differential diagnosis of HCC and cHCC-CC and obtain independent risk factors for predicting cHCC-CC. Study type. Retrospective. Population. The clinical and imaging data of 157 HCC and 59 cHCC-CC patients confirmed by pathology were collected. Field Strength/Sequence. 1.5T; cross-sectional T1WI (gradient double echo sequence); cross-sectional T2WI (fast spin echo sequence, fat suppression); enhancement (3D LAVA technology). Assessment. The differences between the HCC and cHCC-CC patients were compared. Statistic Tests. Using the t-test, chi-square test, and logistic regression analysis, P < 0.05 was considered statistically significant. Result 1. CHCC-CC was more likely to show multiple lesions than HCC (28.81% vs. 10.83%, P = 0.001) and more prone to microvascular invasion (MVI) (36.31% vs. 61.02%, P < 0.001). However, HCC had a higher incidence of liver cirrhosis than cHCC-CC (50.85% vs. 72.61%, P = 0.003). 2. The incidence of nonsmooth margin was higher in the cHCC-CC group (84.75% vs. 52.23%, P < 0.001). The incidence of peritumor enhancement in the arterial phase was higher in the cHCC-CC group (11.46% vs. 62.71%, P < 0.001) 3. According to the multivariate analysis, arterial peritumor enhancement (OR = 8.833,95%CI:4.033,19.346, P < 0.001) was an independent risk factor for cHCC-CC (P < 0.001)). It had high sensitivity (62.71%) and specificity (88.54%) in the diagnosis of cHCC-CC. Date Conclusions. Liver cirrhosis and the imaging findings of GD-DTPA-enhanced MRI are helpful for the differential diagnosis of HCC and cHCC-CC. In addition, the imaging sign of peritumoral enhancement in the arterial phase has high sensitivity and specificity for the diagnosis of cHCC-CC.

Differential diagnosis of hepatocellular carcinoma and hepatic hemangioma based on maximum wavelet-coefficient statistics: Novel radiomics features from plain CT

In computed tomography (CT)-based diagnoses of liver tumors, contrast-enhanced CT may cause renal toxicity and allergic reactions. Regular health examinations prefer plain CT, but subsequent diagnoses significantly depend on subjective experience. Radiomics provides a quantitative, objective, and noninvasive way for diagnosing liver tumors. This study aimed to use plain CT-based radiomics to diagnose hepatocellular (HCC, malignant) and hemangioma (HH, benign) liver tumors. Inspired by the knowledge that HCC and HH exhibit different histopathological characteristics, we developed a novel feature extraction technique (referred to as maximum wavelet-coefficient statistics, MWCS) to highlight the differences in histopathological characteristics by reorganizing and expressing the patterns of wavelet-coefficients that represent local changes. We attempted multiple feature selection algorithms and various machine learning approaches to train classification models and tested these models on an independent test cohort. Experimental results showed that the classification models based on the proposed MWCS-COM (using a statistical method of co-occurrence matrix in MWCS) feature set exhibited performance superior to those based on traditional feature sets. Furthermore, the linear support vector machine (SVM) model achieved state-of-the-art performance in the classification experiments with a test area under receiver operator characteristic curve (AUC) of 0.8734 (95% confidence interval, 0.8666–0.8802). This result indicated that the MWCS-COM features are highly advantageous to the differential diagnosis of HCC and HH from plain CT images. We also explored the potential associations between MWCS-COM features and histopathological characteristics and observed that the MWCS-COM features could potentially enhance radiologists’ diagnostic ability.

Value of albumin RNAscope in situ hybridization in diagnosis and differential diagnosis of hepatocellular carcinoma

Objective: To investigate the utility of albumin RNAscope in situ hybridization in the diagnosis and differential diagnosis of hepatocellular carcinoma and its mimics. Methods: One hundred and fifty-two cases of hepatocellular carcinoma and its mimics and 33 cases of normal tissue were selected from the pathology database of the Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine from January 2013 to December 2019. Tissue microarrays were constructed and RNAscope in situ hybridization was performed to detect the expression of albumin mRNA. Results: No albumin mRNA expression was detected in normal tissues except for the liver. All hepatocellular carcinoma regardless of its degree of differentiation and primary or metastatic nature had detectable albumin mRNA, with strong and diffuse staining in 90.7% (49/54) of cases. While the positive rate of HepPar-1, Arg-1 or one of them by immunohistochemistry was 87.0% (47/54), 85.2% (46/54) and 92.6% (50/54) respectively. The positive rates of albumin mRNA in intrahepatic cholangiocarcinoma and biphenotypic hepatocellular carcinoma were 7/15 and 9/10, respectively. The former showed focal or heterogeneous staining, while the latter showed strong and diffuse staining. The positive rate of hepatoid adenocarcinoma was 8/19, and the albumin expression could be diffuse or focal. Sporadic cases of poorly differentiated gastric adenocarcinoma and metastatic colon adenocarcinoma showed focal staining of albumin mRNA. Conclusions: Detection of albumin mRNA by RNAscope in situ hybridization is of great value for the diagnosis and differential diagnosis of HCC, and the sensitivity may be improved by combining with HepPar-1 and Arg-1. It also offers different diagnostic clues according to different expression patterns.

Cytological Comparison between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma by Image Analysis Software Using Touch Smear Samples of Surgically Resected Specimens.

Simple SummaryThis study cytologically compared hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) using image analyzing software. The results indicated that the major/minor axis ratio of ICC was significantly larger than in HCC in Papanicolaou staining. This difference was consistently observed in clinical samples of cytology such as fine-needle aspiration, brushing and ascites. This study indicated a significant difference in the nuclear morphology of HCC (round shape) and ICC (oval shape) in Papanicolaou-stained cytology specimens. This simple and objective finding is considered to be useful for differential cytodiagnosis of HCC and ICC.To investigate useful cytological features for differential diagnosis of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), this study cytologically compared HCC to ICC using image analysis software. Touch smear specimens of surgically resected specimens were obtained from a total of 61 nodules of HCC and 16 of ICC. The results indicated that the major/minor axis ratio of ICC is significantly larger than that of HCC (1.67 ± 0.27 vs. 1.32 ± 0.11, p < 0.0001) in Papanicolaou staining. This result means that the nucleus of HCC is close to round and the nucleus of ICC is close to an oval. This significant difference in the major/minor axis ratio between ICC and HCC was consistently observed by the same analyses using clinical samples of cytology (4 cases of HCC and 13 cases of ICC) such a fine-needle aspiration, brushing and ascites (ICC: 1.45 ± 0.13 vs. HCC: 1.18 ± 0.056, p = 0.004). We also confirmed that nuclear position center-positioned nucleus (p < 0.0001) and granular cytoplasm (p < 0.0001) are typical features of HCC tumor cells compared to ICC tumor cells. The research study found a significant difference in the nuclear morphology of HCC (round shape) and ICC (oval shape) in Papanicolaou-stained cytology specimens. This simple and objective finding will be very useful for the differential cytodiagnosis of HCC and ICC.

First-line chemotherapy plus immunotherapy in biliary tract cancer

Biliary tract cancers have the second highest primary liver tumour incidence after hepatocellular carcinoma.1 They are classified into three subtypes (intrahepatic cholangiocarcinoma, extrahepatic cholangiocarcinoma, and gallbladder carcinoma) on the basis of anatomical location, with specific epidemiological, molecular, and therapeutic features.1 The incidence of intrahepatic cholangiocarcinoma is increasing, particularly in high-income countries, in relation to improvement in the differential diagnosis of hepatocellular carcinoma and liver metastases from other adenocarcinomas, and the rising rates of obesity, diabetes, and alcohol use.

Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma

BackgroundAberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC).MethodsDifferentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples.ResultsBased on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939–0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758–0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905–0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml−1) from high risk population (AUC=0.93; 95% CI 0.892–0.969).ConclusionsWe have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort.Trial registrationThe study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(#NCT04383353).

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.

4210\xa0Da and 1866\xa0Da polypeptides as potential biomarkers of liver disease progression in hepatitis B virus patients

HBV infection is recognized as a serious global health problem, and hepatitis B virus infection is a complicated chronic disease leading to liver cirrhosis (LC) and hepatocellular carcinoma (HCC). New biochemical serum markers could be used to advance the diagnosis and prognosis of HBV-associated liver diseases during the progression of chronic hepatitis B into cirrhosis and HCC. We determined whether the 4210 Da and 1866 Da polypeptides are serum metabolite biomarkers of hepatopathy with hepatitis B virus. A total of 570 subjects were divided into five groups: healthy controls, those with natural clearance, and patients with CHB, LC, and HCC. The 1866 Da and 4210 Da polypeptides were measured by Clin-ToF II MALDI-TOF–MS. There were significant differences in 4210 Da and 1866 Da levels among the five groups (P < 0.001). For the differential diagnosis of CHB from normal liver, the areas under the receiver operating characteristic (ROC) curve of 4210 Da and 1866 Da and their combination via logistic regression were 0.961, 0.849 and 0.967. For the differential diagnosis of LC from CHB, the areas under the ROC curve were 0.695, 0.841 and 0.826. For the differential diagnosis of HCC from CHB, the areas under the ROC curve were 0.744, 0.710 and 0.761, respectively. For the differential diagnosis of HCC from LC, the areas under the ROC curve of 4210 Da and 1866 Da were 0.580 and 0.654. The positive rate of 1866 Da was 45.5% and 69.0% in AFP-negative HCC patients and that of 4210 Da was 60.6% 58.6% in AFP-negative HCC patients of the study HCC vs. CHB and HCC vs. LC. The 4210 Da and 1866 Da polypeptide levels were positively correlated with HBV DNA levels (P < 0.001, r = 0.269; P < 0.001, r = 0.285). The 4210 Da and 1866 Da polypeptides had good diagnostic value for the occurrence and progression of HBV-related chronic hepatitis, liver cirrhosis and hepatocellular carcinoma and could serve to accurately guide treatment management and predict clinical outcomes.

Circulating Tumor DNA Methylation Markers for Differential Diagnosis of Hepatocellular Carcinoma

Background: An effective blood-based method for hepatocellular carcinoma (HCC) early detection has not yet been developed. Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identified a non-invasive DNA methylation-based diagnostic approach using cell-free DNA for HCC early detection in high risk population. Methods: Differentially methylated DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, cirrhotic and healthy liver tissues using targeted bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated makers using plasma samples from 120 HCC patients, 92 cirrhotic patients and 290 healthy individuals. This model was independently validated in a cohort consisting of 67 HCC patients, 115 cirrhotic patients and 242 healthy individuals. The clinical parameters of misclassified samples were also investigated. Findings: Based on methylation profiling of tissue samples, a total of 2,321 DNA methylation markers were derived, which were subsequently used to construct a cfDNA-based HCC screening model, achieving a sensitivity of 86% and specificity of 97% in the training cohort and a sensitivity of 82% and specificity of 96% in the independent validation cohort. The HCC screening model can effectively discriminate HCC patients from non-HCC patients, including patients with cirrhosis, HBV infection and healthy individuals, achieving an AUC of 0.956; whereas serum alpha-fetoprotein (AFP) achieved an AUC of 0.803. Interpretation: We have developed and validated an HCC screening model which can effectively distinguish early stage HCC patients from cirrhotic patients and symptomatic HBsAg-seropositive individuals. Funding: Key Research and Development Program of Hunan Province, Natural Science Foundation of Hunan Province, China. Declaration of Interest: None to declare. Ethical Approval: The study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). All collection and usage of human sample and clinical data were in accordance with the principles of the Declaration of Helsinki. Written informed content was obtained from every participant for the use of their tissue or plasma samples.

Urea as a By-Product of Ammonia Metabolism Can Be a Potential Serum Biomarker of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is highly malignant; nearly half of the new cases and deaths are in China. The poor prognosis of HCC is mainly due to late diagnosis; many new biomarkers have been developed for HCC diagnosis. However, few markers are quickly translated into clinical practice; early and differential diagnosis of HCC from cirrhosis and/or hepatitis is still a clinical challenge. Metabolomics and biochemical methods were used to reveal specific serum biomarkers of HCC. Most of the elevated metabolites in HCC and HBV patients were overlapped compared with controls. Urea was the specifically elevated serum biomarker of HCC patients. Moreover, urea combined with AFP and CEA can improve the sensitivity of HCC diagnosis. The plasma ammonia of HCC patients was significantly higher than healthy controls. Co-culture cell model revealed normal liver cells cooperated with cancer cells to metabolize ammonia into urea. The urea metabolism in cancer cells marginally depended on the expression of CPS1. However, the expression of CPS1 did not change with ammonium chloride, which might regulate the urea cycle through enzyme activity. The urea cycle could detoxify high concentrations of ammonia to promote cancer cell proliferation. Therefore, urea was a by-product of ammonia metabolism and could be a potential serum biomarker for HCC. The combined application of metabolomics and biochemical methods can discover new biomarkers for the early diagnosis of HCC and be quickly applied to clinical diagnosis.

S100P as a novel biomarker of microvascular invasion and portal vein tumor thrombus in hepatocellular carcinoma.

Portal vein tumor thrombus (PVTT) and microvascular invasion (MVI) are types of intrahepatic vascular metastasis of hepatocellular carcinoma (HCC) and are highly correlated with poor prognosis. However, the underlying biomarkers of PVTT and MVI are unclear. We identified a PVTT/MVI-associated gene S100P by cDNA microarray analysis, and assess the potential value of serum S100P measurement in the differential diagnosis of HCC and prediction of MVI status with large retrospective and perspective cohort studies. The mRNA and protein of S100P was increased in HCCs with PVTT or MVI. High S100P immunostaining in tumors was correlated with inferior tumor-free survival. Serum S100P values discriminated patients with HCCs from those with benign liver tumors, and it showed predictive potential of MVI status in both retrospective and perspective cohorts. S100P may regulate HCC tumorigenicity and invasive ability; S100P also was associated with up-regulation of CD44, which may mediate HCC cell adhesion to form PVTT/MVI. Serum S100P may be a novel differential diagnostic marker for HCC and a potential predictor of MVI status pre-surgery for HCC patients. S100P overexpression in HCC is highly correlated with the formation of PVTT and MVI, which may make S100P as a potential therapeutic target for HCC metastasis.

Atypical magnetic resonance imaging features and differential diagnosis of hepatocellular carcinoma

ObjectiveHepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver. We aimed to summarize and analyze the atypical magnetic resonance imaging (MRI) features of HCC to improve its diagnostic accuracy.MethodsWe retrospectively analyzed MRI data for 66 patients with HCC with atypical MRI features confirmed by operation and pathology.ResultsTwelve patients had high signals and 18 patients had significant decreases in opposed phase signals in T1WI plain scans. Nine patients had high signals and six patients had large cystic lesions in apparent diffusion coefficient images. Dynamic enhancement showed progressive enhancement in 15 patients, ring enhancement in three, irregular patchy enhancement in three, ‘nodule-in-nodule’ enhancement in six, delayed central patchy enhancement in six, delayed central ‘star-like aristate scars’ (T2WI revealed high signal intensity) in 21, and poor blood supply in three patients.ConclusionsMRI can make a clear diagnosis of typical HCC, and atypical cases can also be distinguished from other tumors or tumor-like lesions by MRI. The analysis of atypical signs may improve the diagnostic accuracy of MRI for HCC.

Dynamic contrast-enhanced MRI differentiates hepatocellular carcinoma from hepatic metastasis of rectal cancer by extracting pharmacokinetic parameters and radiomic features.

The aim of the present study was to explore how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may differentiate hepatocellular carcinoma (HCC) from hepatic metastasis of rectal cancer (HMRC) by extracting pharmacokinetic parameters and radiomic features. A total of 75 patients, including 41 cases with HCC and 34 cases with HMRC, underwent DCE-MRI examination. Dual-input two-compartment extended Tofts tracer kinetic model attached to a specialized image post-processing software package from OmniKinetics; GE Healthcare was used to calculate the values of the pharmacokinetic parameters and radiomic features, which were extracted from the lesions at the same region of interest. These values were evaluated using Student's t-test and receiver operating characteristic curves, and discriminant models were built to differentiate between HCC and HRMC. The results identified statistically significant differences in the values of the pharmacokinetic parameters hepatic perfusion index (HPI), endothelial transfer constant (Ktrans), initial area under the gadolinium concentration curve during the first 60 sec (IAUC) between the HCC and HRMC groups. In addition, statistically significant differences in 17 radiomic features were observed between the two groups (P<0.05). The areas under the receiver operating characteristic (ROC) curves of the pharmacokinetic parameters Ktrans, IAUC and HPI were 0.73, 0.77 and 0.67, respectively. The range of the areas under the ROC curves of the 17 radiomic features with statistical differences was 0.63-0.79. In addition, when pharmacokinetic parameters and radiomic features were incorporated, the area under the ROC curve was 0.86. The accuracy of Fisher's discriminant analysis model based on radiomic features was 89.3%, and the leave-one-out cross-validation accuracy was 80.0%. In conclusion, DCE-MRI was demonstrated to be useful in the differential diagnosis of HCC and HMRC by extracting pharmacokinetic parameters and radiomic features, and incorporation of the two paths improved the diagnostic efficacy. A discriminant model based on radiomic features further enhanced the identification of HCC and HMRC.