Clustering Of Hepatocellular Carcinoma Research Articles

Preoperative CT features for characterization of vessels that encapsulate tumor clusters in hepatocellular carcinoma

PurposeTo explore the capability of preoperative CT imaging features, in combination with clinical indicators, for predicting vessels that encapsulate tumor clusters (VETC) pattern and prognosis in hepatocellular carcinoma (HCC). Materials and methodsFrom January 2015 to May 2022, patients with HCC who underwent curative resection and preoperative enhanced CT were retrospectively included. Clinical indicators and imaging featuresassociated with the VETC pattern were determined by logistic regression analyses. The early recurrence (ER) rate was determined using the Kaplan-Meier survival curve. Factors associated with ER after surgical resection were identified by Cox regression analyses. ResultA total of 243 patients with HCCwere evaluated. The total bilirubin > 17.1 μmol/L (odds ratio [OR] 3.43, 95 % Confidence Interval [CI] 1.70, 6.91, p = 0.001), serum α-fetoprotein > 100 ng/mL (OR 2.41, 95 % CI 1.25, 4.67, p = 0.009), intratumor artery (IA) (OR 2.00, 95 % CI 1.04, 3.86,p = 0.039) and arterial peritumoral enhancement (OR 2.60, 95 % CI 1.13, 5.96, p = 0.025) were independent risk factors for VETC+–HCC. The VETC+status andCT feature ofIA were associated with an increased risk of recurrence, with a shorter median RFS, compared to those without these factors (p < 0.001 and p = 0.019, respectively). In multivariable Cox regression analysis, the VETC+(hazard ratio [HR] 2.60, 95 % CI 1.66, 4.09, p < 0.001), morphological patterns of confluent multinodular growth (HR 1.79, 95 % CI 1.10, 2.91,p = 0.019), the number of the tumors (≥2) (HR 2.69, 95 % CI 1.56, 4.65, p < 0.001), and the IA (HR 1.73, 95 % CI 1.12, 2.66, p = 0.013) were independent predictors of ER in patients with HCC after surgical resection. ConclusionPreoperative CT features combined with clinical indicators could predict VETC pattern, and the CT features, along with VETC status, were of prognostic significance for early postoperative recurrence in patients with HCC. Clinical Relevance StatementPreoperative CT features combined with clinical indicators could predict VETC pattern, and the CT features, along with VETC status, were of prognostic significance for early recurrence in patients with HCC after surgical resection.

Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.

To establish and validate a non-invasive deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict vessels encapsulating tumor clusters (VETC) patterns in hepatocellular carcinoma (HCC). This retrospective study included consecutive HCC patients with preoperative CEUS images and available tissue specimens. Patients were randomly allocated into the training and test cohorts. CEUS images were analyzed using the ResNet-18 convolutional neural network for the development and validation of the VETC predictive model. The predictive value for postoperative early recurrence (ER) of the proposed model was further evaluated. A total of 242 patients were enrolled finally, including 195 in the training cohort (54.6 ± 11.2 years, 178 males) and 47 in the test cohort (55.1 ± 10.6 years, 40 males). The DL model (DL signature) achieved favorable performance in both the training cohort (area under the receiver operating characteristics curve [AUC]: 0.92, 95% confidence interval [CI]: 0.88-0.96) and test cohort (AUC: 0.90, 95% CI: 0.82-0.99). The stratified analysis demonstrated good discrimination of DL signature regardless of tumor size. Moreover, the DL signature was found independently correlated with postoperative ER (hazard ratio [HR]: 1.99, 95% CI: 1.29-3.06, p = 0.002). C-indexes of 0.70 and 0.73 were achieved when the DL signature was used to predict ER independently and combined with clinical features. The proposed DL signature provides a non-invasive and practical method for VETC-HCC prediction, and contributes to the identification of patients with high risk of postoperative ER. This DL model based on contrast-enhanced US displayed an important role in non-invasive diagnosis and prognostication for patients with VETC-HCC, which was helpful in individualized management. Preoperative biopsy to determine VETC status in HCC patients is limited. The contrast-enhanced DL model provides a non-invasive tool for the prediction of VETC-HCC. The proposed deep-learning signature assisted in identifying patients with a high risk of postoperative ER.

Intraindividual comparison of CT and MRI for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.

To establish and validate scoring models for predicting vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) using computed tomography (CT) and magnetic resonance imaging (MRI), and to intra-individually compare the predictive performance between the two modalities. We retrospectively included 324 patients with surgically confirmed HCC who underwent preoperative dynamic CT and MRI with extracellular contrast agent between June 2019 and August 2020. These patients were then divided into a discovery cohort (n = 227) and a validation cohort (n = 97). Imaging features and Liver Imaging Reporting and Data System (LI-RADS) categories of VETC-positive HCCs were evaluated. Logistic regression analyses were conducted on the discovery cohort to identify clinical and imaging predictors associated with VETC-positive cases. Subsequently, separate CT-based and MRI-based scoring models were developed, and their diagnostic performance was compared using generalized estimating equations. On both CT and MRI, VETC-positive HCCs exhibited a higher frequency of size > 5.0 cm, necrosis or severe ischemia, non-smooth tumor margin, targetoid appearance, intratumor artery, and heterogeneous enhancement with septations or irregular ring-like structure compared to VETC-negative HCCs (all p < 0.05). Regarding LI-RADS categories, VETC-positive HCCs were more frequently categorized as LR-M than VETC-negative cases (all p < 0.05). In the validation cohort, the CT-based model showed similar sensitivity (76.7% vs. 86.7%, p = 0.375), specificity (83.6% vs. 74.6%, p = 0.180), and area under the curve value (0.80 vs. 0.81, p = 0.910) to the MRI-based model in predicting VETC-positive HCCs. Preoperative CT and MRI demonstrated comparable performance in the identification of VETC-positive HCCs, thus displaying promising predictive capabilities. Both computed tomography and magnetic resonance imaging demonstrated promise in preoperatively identifying the vessel-encapsulating tumor cluster pattern in hepatocellular carcinoma, with no statistically significant difference between the two modalities, potentially adding additional prognostic value. Computed tomography (CT) and magnetic resonance imaging (MRI) show promise in the preoperative identification of vessels encapsulating tumor clusters-positive hepatocellular carcinoma (HCC). HCC with vessels encapsulating tumor cluster patterns were more frequently LR-M compared to those without. These CT and MRI models showed comparable ability in identifying vessels encapsulating tumor clusters-positive HCC.

Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography.

Recently, vessels encapsulating tumor clusters (VETC) was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner, and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC). To develop and validate a preoperative nomogram using contrast-enhanced computed tomography (CECT) to predict the presence of VETC+ in HCC. We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers. Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase. Radiomics features, essential for identifying VETC+ HCC, were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set. The model's performance was validated on two separate test sets. Receiver operating characteristic (ROC) analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets. The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features. ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features, the radiomics features and the radiomics nomogram. The study included 190 individuals from two independent centers, with the majority being male (81%) and a median age of 57 years (interquartile range: 51-66). The area under the curve (AUC) for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825, 0.788, and 0.680 in the training set and the two test sets. A total of 13 features were selected to construct the Rad-score. The nomogram, combining clinical-radiological and combined radiomics features could accurately predict VETC+ in all three sets, with AUC values of 0.859, 0.848 and 0.757. Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models. This study demonstrates the potential utility of a CECT-based radiomics nomogram, incorporating clinical-radiological features and combined radiomics features, in the identification of VETC+ HCC.

Radiomics analysis of gadoxetic acid-enhanced MRI for evaluating vessels encapsulating tumour clusters in hepatocellular carcinoma.

The aim of this study was to develop an integrated model that combines clinical-radiologic and radiomics features based on gadoxetic acid-enhanced MRI for preoperative evaluating of vessels encapsulating tumour clusters (VETC) patterns in hepatocellular carcinoma (HCC). This retrospective study encompassed 234 patients who underwent surgical resection. Among them, 101 patients exhibited VETC-positive HCC, while 133 patients displayed VETC-negative HCC. Volumes of interest were manually delineated for entire tumour regions in the arterial phase (AP), portal phase (PP), and hepatobiliary phase (HBP) images. Independent predictors for VETC were identified through least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analysis, utilising radiomics-AP, PP, HBP, along with 24 imaging features and 19 clinical characteristics. Subsequently, the clinico-radiologic model, radiomics model, and integrated model were established, with a nomogram visualising the integrated model. The performance for VETC prediction was evaluated using a receiver operating characteristic curve. The integrated model, composed of 3 selected traditional imaging features (necrosis or severe ischemia [OR=2.457], peripheral washout [OR=1.678], LLR_AP (Lesion to liver ratio_AP) [OR=0.433] and radiomics-AP [OR=2.870], radiomics-HBP [OR=2.023], radiomics-PP [OR=1.546]), showcased good accuracy in predicting VETC patterns in both the training (AUC=0.873, 95% confidence interval [CI]: 0.821-0.925)) and validation (AUC=0.869, 95% CI:0.789-0.950) cohorts. This study established an integrated model that combines traditional imaging features and radiomic features from gadoxetic acid-enhanced MRI, demonstrating good performance in predicting VETC patterns.

ASO Visual Abstract: Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels That Encapsulate Tumor Clusters in Hepatocellular Carcinoma.

ASO Visual Abstract: Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels That Encapsulate Tumor Clusters in Hepatocellular Carcinoma.

Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels that Encapsulate Tumor Clusters in Hepatocellular Carcinoma

BackgroundHepatocellular carcinoma (HCC) is the fourth most common cause of cancer death worldwide, and the prognosis remains dismal. In this study, two pivotal factors, microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC) were preoperatively predicted simultaneously to assess prognosis.MethodsA total of 133 HCC patients who underwent surgical resection and preoperative gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) were included. The statuses of MVI and VETC were obtained from the pathological report and CD34 immunohistochemistry, respectively. A three-dimensional convolutional neural network (3D CNN) for single-task learning aimed at MVI prediction and for multitask learning aimed at simultaneous prediction of MVI and VETC was established by using multiphase Gd-EOB-DTPA-enhanced MRI.ResultsThe 3D CNN for single-task learning achieved an area under receiver operating characteristics curve (AUC) of 0.896 (95% CI: 0.797–0.994). Multitask learning with simultaneous extraction of MVI and VETC features improved the performance of MVI prediction, with an AUC value of 0.917 (95% CI: 0.825–1.000), and achieved an AUC value of 0.860 (95% CI: 0.728–0.993) for the VETC prediction. The multitask learning framework could stratify high- and low-risk groups regarding overall survival (p < 0.0001) and recurrence-free survival (p < 0.0001), revealing that patients with MVI+/VETC+ were associated with poor prognosis.ConclusionsA deep learning framework based on 3D CNN for multitask learning to predict MVI and VETC simultaneously could improve the performance of MVI prediction while assessing the VETC status. This combined prediction can stratify prognosis and enable individualized prognostication in HCC patients before curative resection.

Relationship between HLA-DRB1*03/04 alleles and familial clustering of hepatocellular carcinoma in Guangxi

Relationship between HLA-DRB1*03/04 alleles and familial clustering of hepatocellular carcinoma in Guangxi

Role of the miR‐106b‐25 microRNA cluster in hepatocellular carcinoma

MicroRNAs are tiny RNA molecules which serve as important post-transcriptional regulators of gene expression. Dysregulated expression of microRNAs has been observed in human cancers, indicating that microRNAs may function as oncogenes or as tumor suppressors. To date, the microRNAs encoded by the oncogenic miR-17-92 cluster, and its paralog the miR-106b-25 cluster, are among those which are differentially expressed in human cancers. In this study, we examined and confirmed the over-expression of these clusters in hepatocellular carcinoma and in hepatoma-derived cells. At least 50% of the tumor samples showed a greater than two-fold increase in the expression for miR-18 and for the miR-106b-25 cluster when compared with the corresponding paired non-tumor samples. Knock-down studies for the miR-106b-25 cluster, which includes miR-106b, miR-93 and miR-25, showed that the expression of the cluster is necessary for cell proliferation and for anchorage-independent growth. In tumors with high expression of this cluster, reduced expression of the BH3-only protein Bim, a miR-25 target, was observed. We further identified the transcription factor E2F1 as a target gene for miR-106b and miR-93 and it is likely that one of the roles of the miR-106b-25 cluster is to prevent excessively high E2F1 expression, which may then cause apoptosis. We conclude that there is aberrant expression of microRNAs encoded by the oncogenic miR-17-92 cluster and the miR-106b-25 cluster in hepatocellular carcinoma. The consistent overexpression of the miR-106b-25 cluster and its role in cell proliferation and anchorage-independent growth points to the oncogenic potential of this cluster.

Novel endothelial cell markers in hepatocellular carcinoma

Hepatocellular carcinoma is characterized by hypervascularity and a propensity for vascular invasion. Detailed analysis of complementary DNA (cDNA) microarray global gene expression data and further validation on a smaller independent sample set by reverse transcription-polymerase chain reaction established the presence of two endothelial gene clusters in hepatocellular carcinoma. Cluster I, consists of 20 cDNA clones, representing 15 unique genes. Cluster II consists of nine unique genes. The expression of the cluster I genes appeared to be significantly upregulated in hepatocellular carcinoma compared with normal liver, cirrhotic liver, or nontumor liver tissues adjacent to the hepatocellular carcinoma. The pattern of gene expression of cluster I genes correlated positively with the ‘proliferation gene cluster’ and ‘stromal cells cluster 2‘. Expression of cluster II genes, in contrast, was not significantly different between hepatocellular carcinoma and non-neoplastic liver tissues. Studies conducted to localize the protein products of these genes by immunohistochemical staining of tissue arrays with up to 350 cores of tissues, and by in situ hybridization led to the discovery of novel sinusoidal endothelial cell markers in hepatocellular carcinoma: podocalyxin-like and regulator of G protein signaling-5. Our results underscore fundamental differences not only between neoplastic vs non-neoplastic liver cells but also between the hepatic sinusoidal endothelium of hepatocellular carcinoma and normal liver.

Prevalence and characteristics of familial hepatocellular carcinoma caused by chronic hepatitis B infection in Hong Kong

Hepatitis B virus infection is an important aetiological factor for hepatocellular carcinoma. Clusters of hepatocellular carcinoma have been observed in families infected with hepatitis B virus. To investigate the prevalence and characteristics of hepatocellular carcinoma associated with familial hepatitis B virus in Hong Kong. Hepatitis B virus patients were screened for familial hepatocellular carcinoma using a standardized questionnaire. The clinical features of patients with familial hepatocellular carcinoma were compared with those of 118 patients with sporadic hepatocellular carcinoma attending the clinic during the same period. A total of 5080 patients were interviewed. Validation of the questionnaire indicated that the reliability was high. There were 22 families with familial hepatocellular carcinoma, giving a prevalence of 4.3 families/1000 hepatitis B virus carriers. The mean age of onset was 48.5 +/- 13 years in familial hepatocellular carcinoma and 62 +/- 11 years in sporadic hepatocellular carcinoma (P = 0.005). The ages of onset were 59 +/- 11, 40 +/- 10 and 18 +/- 4 years in the first, second and third generations, respectively (P < 0.0001), suggesting an anticipation phenomenon. Familial hepatocellular carcinoma patients were more likely to present with pain (70% vs. 10%, P < 0.0001), but not on routine screening (14% vs. 52%, P < 0.0001), than sporadic hepatocellular carcinoma patients. The prevalence of familial hepatocellular carcinoma is significant in Hong Kong. These patients show specific clinical features when compared with patients with sporadic hepatocellular carcinoma.

Clustering of hepatocellular carcinoma in Alaska Native families.

Alaska Natives have a high incidence of hepatocellular carcinoma (HCC), with regional, ethnic, village, and familial clustering. During 1969-1988, 15 of the 45 incident cases of HCC in Alaska Natives occurred in 5 families. This report presents epidemiologic, virologic, and immunogenetic data on these 5 families. The results of hepatitis B virus (HBV) serologic testing indicates that HBV infection was an important factor in 4 of the 5 families. Human lymphocyte antigen (HLA) testing was performed in 3 families. The HLA results are reviewed in relationship to the familial clustering of HCC.