Vessels That Encapsulate Tumor Clusters (VETC) Pattern Is a Predictor of Sorafenib Benefit in Patients with Hepatocellular Carcinoma

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

PURPOSE

To 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 METHODS

From 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.

RESULT

A 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.

CONCLUSION

Preoperative 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 STATEMENT

Preoperative 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.

Optimizing predictions: improved performance of preoperative gadobenate-enhanced MRI hepatobiliary phase features in predicting vessels encapsulating tumor clusters in hepatocellular carcinoma-a multicenter study

BACKGROUND

Vessels Encapsulating Tumor Clusters (VETC) are now recognized as independent indicators of recurrence and overall survival in hepatocellular carcinoma (HCC) patients. However, there has been limited investigation into predicting the VETC pattern using hepatobiliary phase (HBP) features from preoperative gadobenate-enhanced MRI.

METHODS

This study involved 252 HCC patients with confirmed VETC status from three different hospitals (Hospital 1: training set with 142 patients; Hospital 2: test set with 64 patients; Hospital 3: validation set with 46 patients). Independent predictive factors for VETC status were determined through univariate and multivariate logistic analyses. Subsequently, these factors were used to construct two distinct VETC prediction models. Model 1 included all independent predictive factors, while Model 2 excluded HBP features. The performance of both models was assessed using the Area Under the Curve (AUC), Decision Curve Analysis, and Calibration Curve. Prediction accuracy between the two models was compared using Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI).

RESULTS

CA199, IBIL, shape, peritumoral hyperintensity on HBP, and arterial peritumoral enhancement were independent predictors of VETC. Model 1 showed robust predictive performance, with AUCs of 0.836 (training), 0.811 (test), and 0.802 (validation). Model 2 exhibited moderate performance, with AUCs of 0.813, 0.773, and 0.783 in the respective sets. Calibration and decision curves for both models indicated consistent predictions between predicted and actual VETC, benefiting HCC patients. NRI showed Model 1 increased by 0.326, 0.389, and 0.478 in the training, test, and validation sets compared to Model 2. IDI indicated Model 1 increased by 0.036, 0.028, and 0.025 in the training, test, and validation sets compared to Model 2.

CONCLUSION

HBP features from preoperative gadobenate-enhanced MRI can enhance the predictive performance of VETC in HCC.

Advances in Understanding Hepatocellular Carcinoma Vasculature: Implications for Diagnosis, Prognostication, and Treatment

Hepatocellular carcinoma (HCC) progresses through multiple stages of hepatocarcinogenesis, with each stage characterized by specific changes in vascular supply, drainage, and microvascular structure. These vascular changes significantly influence the imaging findings of HCC, enabling non-invasive diagnosis. Vascular changes in HCC are closely related to aggressive histological characteristics and treatment responses. Venous drainage from the tumor toward the portal vein in the surrounding liver facilitates vascular invasion, and the unique microvascular pattern of vessels that encapsulate the tumor cluster (known as a VETC pattern) promotes vascular invasion and metastasis. Systemic treatments for HCC, which are increasingly being used, primarily target angiogenesis and immune checkpoint pathways, which are closely intertwined. By understanding the complex relationship between histopathological vascular changes in hepatocarcinogenesis and their implications for imaging findings, radiologists can enhance the accuracy of imaging diagnosis and improve the prediction of prognosis and treatment response. This, in turn, will ultimately lead to better patient care.

Deep Learning Classification and Quantification of Pejorative and Nonpejorative Architectures in Resected Hepatocellular Carcinoma from Digital Histopathologic Images

Liver resection is one of the best treatments for small hepatocellular carcinoma (HCC), but post-resection recurrence is frequent. Biotherapies have emerged as an efficient adjuvant treatment, making the identification of patients at high risk of recurrence critical. Microvascular invasion (mVI), poor differentiation, pejorative macrotrabecular architectures, and vessels encapsulating tumor clusters architectures are the most accurate histologic predictors of recurrence, but their evaluation is time-consuming and imperfect. Herein, a supervised deep learning-based approach with ResNet34 on 680 whole slide images (WSIs) from 107 liver resection specimens was used to build an algorithm for the identification and quantification of these pejorative architectures. This model achieved an accuracy of 0.864 at patch level and 0.823 at WSI level. To assess its robustness, it was validated on an external cohort of 29 HCCs from another hospital, with an accuracy of 0.787 at WSI level, affirming its generalization capabilities. Moreover, the largest connected areas of the pejorative architectures extracted from the model were positively correlated to the presence of mVI and the number of tumor emboli. These results suggest that the identification of pejorative architectures could be an efficient surrogate of mVI and have a strong predictive value for the risk of recurrence. This study is the first step in the construction of a composite predictive algorithm for early post-resection recurrence of HCC, including artificial intelligence-based features.

Preoperative Gadoxetic Acid-Enhanced MRI Features for Evaluation of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma: Creating Nomograms for Risk Assessment

BACKGROUND

Vessels encapsulating tumor cluster (VETC) and microvascular invasion (MVI) have a synergistic effect on prognosis assessment and treatment selection of hepatocellular carcinoma (HCC). Preoperative noninvasive evaluation of VETC and MVI is important.

PURPOSE

To explore the diagnosis value of preoperative gadoxetic acid (GA)-enhanced magnetic resonance imaging (MRI) features for MVI, VETC, and recurrence-free survival (RFS) in HCC.

STUDY TYPE

Retrospective.

POPULATION

240 post-surgery patients with 274 pathologically confirmed HCC (allocated to training and validation cohorts with a 7:3 ratio) and available tumor marker data from August 2014 to December 2021.

FIELD STRENGTH/SEQUENCE

3-T, T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging.

ASSESSMENT

Three radiologists subjectively reviewed preoperative MRI, evaluated clinical and conventional imaging features associated with MVI+, VETC+, and MVI+/VETC+ HCC. Regression-based nomograms were developed for HCC in the training cohort. Based on the nomograms, the RFS prognostic stratification system was further. Follow-up occurred every 3-6 months.

STATISTICAL TESTS

Chi-squared test or Fisher's exact test, Mann-Whitney U-test or t-test, least absolute shrinkage and selection operator-penalized, multivariable logistic regression analyses, receiver operating characteristic analysis, Harrell's concordance index (C-index), Kaplan-Meier plots. Significance level: P < 0.05.

RESULTS

In the training group, 44 patients with MVI+ and 74 patients with VETC+ were histologically confirmed. Three nomograms showed good performance in the training (C-indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.892 vs. 0.848 vs. 0.910) and validation (C-indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.839 vs. 0.810 vs. 0.855) cohorts. The median follow-up duration for the training cohort was 43.6 (95% CI, 35.0-52.2) months and 25.8 (95% CI, 16.1-35.6) months for the validation cohort. Patients with either pathologically confirmed or nomogram-estimated MVI, VETC, and MVI+/VETC+ suffered higher risk of recurrence.

DATA CONCLUSION

GA-enhanced MRI and clinical variables might assist in preoperative estimation of MVI, VETC, and MVI+/VETC+ in HCC.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

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

Purpose

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).

Methods

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.

Results

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.

Conclusion

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.

A nomogram for preoperative prediction of vessels encapsulating tumor clusters (VETC) pattern and prognosis of hepatocellular carcinoma

BACKGROUND

Vessels encapsulating tumor clusters (VETC) pattern of hepatocellular carcinoma (HCC) are associated with unfavorable prognosis. This study aimed to establish a nomogram model to predict VETC patterns based on preoperative CT imaging features.

PATIENTS AND METHODS

Patients who underwent surgical resection between January 1, 2016 and August 31, 2022 were retrospectively included. Predictors associated with VETC pattern were determined by using logistic regression analyses, and a nomogram model was constructed. Prognostic factors associated with recurrence-free survival (RFS) after surgical resection were identified by using Cox regression analyses.

RESULTS

A total of 84 patients were included for CT analysis. All patients underwent radical surgical resection. AST/ALT >1.07(odds ratio [OR], 4.91; 95 ​% CI: 1.11, 21.68; P ​< ​0.05), intratumoral necrosis (OR, 4.99; 95 ​% CI: 1.25, 19.99; P ​< ​0.05) and enhancing capsule (OR, 3.32; 95 ​% CI: 1.27, 8.94; P ​< ​0.05) were independent predictors of VETC pattern. These features were used for the construction of nomogram model, which showed comparable prediction performance, with AUC value of 0.767 (95%CI [0.662, 0.852]). CK19 status (Hazard ratio [HR], 2.02; 95 ​% CI: 1.06, 3.86; P ​< ​0.05), the number of tumors (HR, 3.31; 95 ​% CI: 1.47, 7.45; P ​< ​0.05) and VETC pattern (HR, 2.52; 95 ​% CI: 1.31, 4.86; P ​< ​0.05) were independent predictors of postoperative RFS.

CONCLUSION

A nomogram model based on preoperative CT imaging features could be used for the characterization of VETC pattern, and has prognostic significance for postoperative RFS in patients with HCC.

The prognostic value of vessels encapsulating tumor clusters (VETC) in patients with hepatocellular carcinoma: a systematic review and meta-analysis

BACKGROUND

Studies have suggested that vessels encapsulating tumor clusters (VETC) is a strong predictor of prognosis in patients with hepatocellular carcinoma (HCC).

METHODS

A systematic search was conducted in PubMed, Embase, Web of Science, and Scopus databases. Overall survival (OS) and tumor efficacy (TE) were two outcome measures used to evaluate the relationship between VETC and HCC prognosis. Hazard ratios (HR) and their 95% confidence intervals (CI) were used.

RESULTS

Thirteen studies with 4429 patients were included in the meta-analysis. The results showed that VETC was significantly associated with both OS (HR 2.00; 95% CI 1.64-2.45) and TE (HR 1.70; 95% CI 1.44-1.99) in HCC patients. Furthermore, recurrence-free survival (RFS) was a stronger indicator of tumor efficacy (HR 1.73; 95% CI 1.44-2.07) than disease-free survival (DFS) (HR 1.69; 95% CI 1.22-2.35). This suggests that VETC-positive HCC has a higher risk of recurrence and a lower survival rate.

CONCLUSION

In conclusion, the meta-analysis suggests that VETC is a significant predictor of overall survival and tumor efficacy in HCC patients and may be a valid prognostic indicator.

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

OBJECTIVES

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).

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

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.

CLINICAL RELEVANCE STATEMENT

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.

KEY POINTS

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.

Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma

RATIONALE AND OBJECTIVES

To develop and validate a clinical-radiomics model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of Vessels encapsulating tumor clusters (VETC)- microvascular invasion (MVI) and prognosis of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

219 HCC patients from Institution 1 were split into internal training and validation groups, with 101 patients from Institution 2 assigned to external validation. Histologically confirmed VETC-MVI pattern categorizing HCC into VM-HCC+ (VETC+/MVI+, VETC-/MVI+, VETC+/MVI-) and VM-HCC- (VETC-/MVI-). The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI. Six radiomics models (intratumor and peritumor in AP, PP, and DP of DCE-MRI) and one clinical model were developed for assessing VM-HCC. Establishing intra-tumoral and peri-tumoral models through combining intratumor and peritumor features. The best-performing radiomics model and the clinical model were then integrated to create a Combined model.

RESULTS

In institution 1, pathological VM-HCC+ were confirmed in 88 patients (training set: 61, validation set: 27). In internal testing, the Combined model had an AUC of 0.85 (95% CI: 0.76-0.93), which reached an AUC of 0.75 (95% CI: 0.66-0.85) in external validation. The model's predictions were associated with early recurrence and progression-free survival in HCC patients.

CONCLUSIONS

The clinical-radiomics model offers a non-invasive approach to discern VM-HCC and predict HCC patients' prognosis preoperatively, which could offer clinicians valuable insights during the decision-making phase.

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

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSION

Preoperative CT and MRI demonstrated comparable performance in the identification of VETC-positive HCCs, thus displaying promising predictive capabilities.

CLINICAL RELEVANCE STATEMENT

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.

KEY POINTS

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.

Establishment of nomogram prediction model of contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for vessels encapsulating tumor clusters pattern of hepatocellular carcinoma

To establish clinical prediction models of vessels encapsulating tumor clusters (VETC) pattern using preoperative contrast-enhanced ultrasound (CEUS) and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid magnetic resonance imaging (EOB-MRI) in patients with hepatocellular carcinoma (HCC). A total of 111 resected HCC lesions from 101 patients were included. Preoperative imaging features of CEUS and EOB-MRI, postoperative recurrence, and survival information were collected from medical records. The best subset regression and multivariable Cox regression were used to select variables to establish the prediction model. The VETC-positive group had a statistically lower survival rate than the VETC-negative group. The selected variables were peritumoral enhancement in the arterial phase (AP), hepatobiliary phase (HBP) on EOB-MRI, intratumoral branching enhancement in the AP of CEUS, intratumoral hypoenhancement in the portal phase of CEUS, incomplete capsule, and tumor size. A nomogram was developed. High and low nomogram scores with a cutoff value of 168 points showed different recurrence-free survival rates and overall survival rates. The area under the curve (AUC) and accuracy were 0.804 and 0.820, respectively, indicating good discrimination. Decision curve analysis showed a good clinical net benefit (threshold probability > 5%), while the Hosmer-Lemeshow test yielded excellent calibration (P = 0.6759). The AUC of the nomogram model combining EOB-MRI and CEUS was higher than that of the models with EOB-MRI factors only (0.767) and CEUS factors only (0.7). The nomogram verified by bootstrapping showed AUC and calibration curves similar to those of the nomogram model. The Prediction model based on CEUS and EOB-MRI is effective for preoperative noninvasive diagnosis of VETC.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma

BACKGROUND

The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status.

PURPOSE

This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients.

STUDY TYPE

Retrospective.

POPULATION

398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40).

FIELD STRENGTH/SEQUENCE

3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP).

ASSESSMENT

Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022.

STATISTICAL TESTS

Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant.

RESULTS

The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months).

DATA CONCLUSION

MR deep learning model based on VETC and MVI provides a potential tool for survival assessment.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

A multi-parametric prognostic model based on clinicopathologic features: vessels encapsulating tumor clusters and hepatic plates predict overall survival in hepatocellular carcinoma patients

BACKGROUND

Vessels encapsulating tumor clusters (VETC) is a newly described vascular pattern that is distinct from microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Despite its importance, the current pathological diagnosis report does not include information on VETC and hepatic plates (HP). We aimed to evaluate the prognostic value of integrating VETC and HP (VETC-HP model) in the assessment of HCC.

METHODS

A total of 1255 HCC patients who underwent radical surgery were classified into training (879 patients) and validation (376 patients) cohorts. Additionally, 37 patients treated with lenvatinib were studied, included 31 patients in high-risk group and 6 patients in low-risk group. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish a prognostic model for the training set. Harrell's concordance index (C-index), time-dependent receiver operating characteristics curve (tdROC), and decision curve analysis were utilized to evaluate our model's performance by comparing it to traditional tumor node metastasis (TNM) staging for individualized prognosis.

RESULTS

A prognostic model, VETC-HP model, based on risk scores for overall survival (OS) was established. The VETC-HP model demonstrated robust performance, with area under the curve (AUC) values of 0.832 and 0.780 for predicting 3- and 5-year OS in the training cohort, and 0.805 and 0.750 in the validation cohort, respectively. The model showed superior prediction accuracy and discrimination power compared to TNM staging, with C-index values of 0.753 and 0.672 for OS and disease-free survival (DFS) in the training cohort, and 0.728 and 0.615 in the validation cohort, respectively, compared to 0.626 and 0.573 for TNM staging in the training cohort, and 0.629 and 0.511 in the validation cohort. Thus, VETC-HP model had higher C-index than TNM stage system(p < 0.01).Furthermore, in the high-risk group, lenvatinib alone appeared to offer less clinical benefit but better disease-free survival time.

CONCLUSIONS

The VETC-HP model enhances DFS and OS prediction in HCC compared to traditional TNM staging systems. This model enables personalized temporal survival estimation, potentially improving clinical decision-making in surveillance management and treatment strategies.

Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma ≤ 3 cm based on enhanced magnetic resonance imaging

BACKGROUND

Vessels encapsulating tumor clusters (VETC) represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma (HCC). However, it seems that no one have focused on predicting VETC status in small HCC (sHCC). This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC (≤ 3 cm) patients.

AIM

To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients.

METHODS

A total of 309 patients with sHCC, who underwent segmental resection and had their VETC status confirmed, were included in the study. These patients were recruited from three different hospitals: Hospital 1 contributed 177 patients for the training set, Hospital 2 provided 78 patients for the test set, and Hospital 3 provided 54 patients for the validation set. Independent predictors of VETC were identified through univariate and multivariate logistic analyses. These independent predictors were then used to construct a VETC prediction model for sHCC. The model's performance was evaluated using the area under the curve (AUC), calibration curve, and clinical decision curve. Additionally, Kaplan-Meier survival analysis was performed to confirm whether the predicted VETC status by the model is associated with early recurrence, just as it is with the actual VETC status and early recurrence.

RESULTS

Alpha-fetoprotein_lg10, carbohydrate antigen 199, irregular shape, non-smooth margin, and arterial peritumoral enhancement were identified as independent predictors of VETC. The model incorporating these predictors demonstrated strong predictive performance. The AUC was 0.811 for the training set, 0.800 for the test set, and 0.791 for the validation set. The calibration curve indicated that the predicted probability was consistent with the actual VETC status in all three sets. Furthermore, the decision curve analysis demonstrated the clinical benefits of our model for patients with sHCC. Finally, early recurrence was more likely to occur in the VETC-positive group compared to the VETC-negative group, regardless of whether considering the actual or predicted VETC status.

CONCLUSION

Our novel prediction model demonstrates strong performance in predicting VETC positivity in sHCC (≤ 3 cm) patients, and it holds potential for predicting early recurrence. This model equips clinicians with valuable information to make informed clinical treatment decisions.

Infiltrative Vessel Co-optive Growth Pattern Induced by IQGAP3 Overexpression Promotes Microvascular Invasion in Hepatocellular Carcinoma

PURPOSE

Microvascular invasion (MVI) is a major unfavorable prognostic factor for intrahepatic metastasis and postoperative recurrence of hepatocellular carcinoma (HCC). However, the intervention and preoperative prediction for MVI remain clinical challenges due to the absent precise mechanism and molecular marker(s). Herein, we aimed to investigate the mechanisms underlying vascular invasion that can be applied to clinical intervention for MVI in HCC.

EXPERIMENTAL DESIGN

The histopathologic characteristics of clinical MVI+/HCC specimens were analyzed using multiplex immunofluorescence staining. The liver orthotopic xenograft mouse model and mechanistic experiments on human patient-derived HCC cell lines, including coculture modeling, RNA-sequencing, and proteomic analysis, were used to investigate MVI-related genes and mechanisms.

RESULTS

IQGAP3 overexpression was correlated significantly with MVI status and reduced survival in HCC. Upregulation of IQGAP3 promoted MVI+-HCC cells to adopt an infiltrative vessel co-optive growth pattern and accessed blood capillaries by inducing detachment of activated hepatic stellate cells (HSC) from the endothelium. Mechanically, IQGAP3 overexpression contributed to HCC vascular invasion via a dual mechanism, in which IQGAP3 induced HSC activation and disruption of the HSC-endothelial interaction via upregulation of multiple cytokines and enhanced the trans-endothelial migration of MVI+-HCC cells by remodeling the cytoskeleton by sustaining GTPase Rac1 activity. Importantly, systemic delivery of IQGAP3-targeting small-interfering RNA nanoparticles disrupted the infiltrative vessel co-optive growth pattern and reduced the MVI of HCC.

CONCLUSIONS

Our results revealed a plausible mechanism underlying IQGAP3-mediated microvascular invasion in HCC, and provided a potential target to develop therapeutic strategies to treat HCC with MVI.

A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma

PURPOSE

This study aimed to build and evaluate a deep learning (DL) model to predict vessels encapsulating tumor clusters (VETC) and prognosis preoperatively in patients with hepatocellular carcinoma (HCC).

METHODS

320 pathologically confirmed HCC patients (58 women and 262 men) from two hospitals were included in this retrospective study. Institution 1 (n = 219) and Institution 2 (n = 101) served as the training and external test cohorts, respectively. Tumors were evaluated three-dimensionally and regions of interest were segmented manually in the arterial, portal venous, and delayed phases (AP, PP, and DP). Three ResNet-34 DL models were developed, consisting of three models based on a single sequence. The fusion model was developed by inputting the prediction probability of the output from the three single-sequence models into logistic regression. The area under the receiver operating characteristic curve (AUC) was used to compare performance, and the Delong test was used to compare AUCs. Early recurrence (ER) was defined as recurrence within two years of surgery and early recurrence-free survival (ERFS) rate was evaluated by Kaplan-Meier survival analysis.

RESULTS

Among the 320 HCC patients, 227 were VETC- and 93 were VETC+ . In the external test cohort, the fusion model showed an AUC of 0.772, a sensitivity of 0.80, and a specificity of 0.61. The fusion model-based prediction of VETC high-risk and low-risk categories exhibits a significant difference in ERFS rates, akin to the outcomes observed in VETC + and VETC- confirmed through pathological analyses (p < 0.05).

CONCLUSIONS

A DL framework based on ResNet-34 has demonstrated potential in facilitating non-invasive prediction of VETC as well as patient prognosis.

Diagnostic and Prognostic Ability of Contrast-Enhanced Unltrasound and Biomarkers in Hepatocellular Carcinoma Subtypes

OBJECTIVE

To investigate the diagnostic and prognostic value of contrast-enhanced ultrasound (CEUS) and clinical indicators of the vessels encapsulating tumor clusters (VETC) pattern and macrotrabecular-massive subtype in hepatocellular carcinoma (MTM-HCC).

METHODS

This retrospective study included patients who underwent preoperative CEUS and hepatectomy for HCC between August 2018 and August 2021. Multivariable logistic regression was performed to select independent correlated factors of VETC-HCC and MTM-HCC to develop nomogram models. The association between model outcomes and early postoperative HCC recurrence was assessed using Kaplan-Meier curve and Cox regression analysis.

RESULTS

The training cohort included 182 patients (54.3 ± 11.3 years, 168 males) and the validation cohort included 91 patients (54.8 ± 10.6 years, 81 males). Multivariate logistic regression analysis revealed that α-fetoprotein (AFP) levels (odds ratio [OR]: 2.26, 95% confidence interval [CI]: 1.49-3.42, p < 0.001), intratumoral nonenhancement (OR: 2.40, 95% CI: 1.02-5.64, p = 0.044), and the perfusion pattern in the CEUS arterial phase (OR: 2.27, 95% CI: 1.05-4.91, p = 0.038) were independent predictors of VETC-HCC. Besides, the former two were also independently associated with MTM-HCC (AFP level: OR: 2.36, 95% CI: 1.36-4.09, p = 0.002; intratumoral nonenhancement: OR: 3.72, 95% CI: 1.02-13.56, p = 0.046). Nomogram models were constructed based on the aforementioned indicators. Kaplan-Meier curve analysis indicated that predicted VETC-HCC or MTM-HCC exhibited higher rates of early recurrence (log-rank p < 0.001 and p = 0.002, respectively). Cox regression analysis showed that a high risk of VETC-HCC was independently correlated with early recurrence (p = 0.011).

CONCLUSION

CEUS combined with AFP levels can predict VETC-HCC/MTM-HCC and prognosis preoperatively.

Prediction of vessels encapsulating tumor clusters pattern and prognosis of hepatocellular carcinoma based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid magnetic resonance imaging

BACKGROUND

Vessels encapsulating tumor clusters (VETC) is a novel vascular pattern distinct from microvascular invasion that is significantly associated with poor prognosis in patients with hepatocellular carcinoma (HCC). This study aimed to predict the VETC pattern and prognosis of patients with HCC based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) magnetic resonance imaging (MRI).

METHODS

Patients with HCC who underwent surgical resection and preoperative Gd-EOB-DTPA MRI between January 1, 2016 and August 31, 2022 were retrospectively included. The variables associated with VETC were evaluated using logistic regression. A nomogram model was constructed on the basis of independent risk factors. COX regression was used to determine the variables associated with recurrence-free survival (RFS).

RESULTS

A total of 98 patients with HCC were retrospectively included. Peritumoral hypointensity on the hepatobiliary phase (HBP) (odd ratio [OR], 2.58; 95% CI, 1.05-6.33; P = .04), tumor-to-liver signal intensity ratio on HBP of ≤0.75 (OR, 27.80; 95% CI, 1.53-502.91; P = .02), and tumor-to-liver apparent diffusion coefficient ratio of ≤1.23 (OR, 4.65; 95% CI, 1.01-21.38; P = .04) were independent predictors of VETC pattern. A nomogram was constructed by combining the aforementioned 3 significant variables. The accuracy, sensitivity, and specificity were 69.79%, 71.74%, and 68.00%, respectively, with an area under the receiver operating characteristic curve of 0.75 (95% CI, 0.65-0.83). The variables significantly associated with RFS of patients with HCC after surgery were Barcelona Clinic Liver Cancer stage (hazard ratio [HR], 2.15; 95% CI, 1.09-4.22; P = .03) and VETC pattern (HR, 2.28; 95% CI, 1.29-4.02; P = .004).

CONCLUSION

The preoperative imaging features based on Gd-EOB-DTPA MRI can be used to predict the VETC pattern, which has prognostic significance for postoperative RFS of patients with HCC.

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

BACKGROUND

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).

AIM

To develop and validate a preoperative nomogram using contrast-enhanced computed tomography (CECT) to predict the presence of VETC+ in HCC.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma

BACKGROUND

Vessels encapsulating tumor cluster (VETC) is a critical prognostic factor and therapeutic predictor of hepatocellular carcinoma (HCC). However, noninvasive evaluation of VETC remains challenging.

PURPOSE

To develop and validate a deep learning radiomic (DLR) model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of VETC and prognosis of HCC.

STUDY TYPE

Retrospective.

POPULATION

A total of 221 patients with histologically confirmed HCC and stratified this cohort into training set (n = 154) and time-independent validation set (n = 67).

FIELD STRENGTH/SEQUENCE

A 1.5 T and 3.0 T; DCE imaging with T1-weighted three-dimensional fast spoiled gradient echo.

ASSESSMENT

Histological specimens were used to evaluate VETC status. VETC+ cases had a visible pattern (≥5% tumor area), while cases without any pattern were VETC-. The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI and reproducibility of segmentation was evaluated. Deep neural network and machine learning (ML) classifiers (logistic regression, decision tree, random forest, SVM, KNN, and Bayes) were used to develop nine DLR, 54 ML and clinical-radiological (CR) models based on AP, PP, and DP of DCE-MRI for evaluating VETC status and association with recurrence.

STATISTICAL TESTS

The Fleiss kappa, intraclass correlation coefficient, receiver operating characteristic curve, area under the curve (AUC), Delong test and Kaplan-Meier survival analysis. P value <0.05 was considered as statistical significance.

RESULTS

Pathological VETC+ were confirmed in 68 patients (training set: 46, validation set: 22). In the validation set, DLR model based on peritumor PP (peri-PP) phase had the best performance (AUC: 0.844) in comparison to CR (AUC: 0.591) and ML (AUC: 0.672) models. Significant differences in recurrence rates between peri-PP DLR model-predicted VETC+ and VETC- status were found.

DATA CONCLUSIONS

The DLR model provides a noninvasive method to discriminate VETC status and prognosis of HCC patients preoperatively.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Clinical significance of mechanistic target of rapamycin expression in vessels that encapsulate tumor cluster-positive hepatocellular carcinoma patients who have undergone living donor liver transplantation

Background

There is limited published information regarding the expression of mechanistic target of rapamycin (mTOR) in vessels that encapsulate tumor cluster (VETC)-positive hepatocellular carcinoma (HCC). The mTOR inhibitor, everolimus, has been approved as an immunosuppressant for use in HCC patients after living donor liver transplantation (LDLT).

Methods

Using a database of 214 patients who underwent LDLT for HCC, we examined the mTOR protein and angiopoietin-2 (Ang-2) in VETC-positive HCC by immunohistochemical staining. The presence of VETC and mTOR expression were evaluated in both primary and recurrent HCC lesions.

Results

Forty-three of the 214 patients (20.1%) were VETC-positive, and 29 of these 43 patients (67.4%) expressed mTOR. Relative Ang-2 expression was significantly higher in the mTOR-positive than in the mTOR-negative group (p = 0.037). Thirty-four of the 214 patients experienced HCC recurrence after LDLT; 20 of these were operable. The primary lesions of six of these 20 patients were VETC-positive; five of these six patients also had VETC-positive recurrent lesions (p < 0.001). The expression of mTOR was significantly higher in the VETC-positive lesions (p = 0.0018).

Conclusions

We showed that mTOR expression was higher in the VETC-positive primary and recurrent lesions than in the VETC-negative ones.

Vessels encapsulating tumor clusters: a novel efficacy predictor of hepatic arterial infusion chemotherapy in unresectable hepatocellular carcinoma

PURPOSE

Vessels encapsulating tumor clusters (VETC) is a novel vascular pattern structurally and functionally distinct from microvascular invasion (MVI) in hepatocellular carcinoma (HCC). This study aims to explore the prognostic value of VETC in patients receiving hepatic arterial infusion chemotherapy (HAIC) for unresectable HCC.

METHODS

From January 2016 to December 2017, 145 patients receiving HAIC as the initial treatment for unresectable HCC were enrolled and stratified into two groups according to their VETC status. Overall survival (OS), progression-free survival (PFS), overall response rate (ORR), and disease control rate (DCR) were evaluated.

RESULTS

The patients were divided into two groups: VETC+ (n = 31, 21.8%) and VETC- (n = 114, 78.2%). The patients in the VETC+ group had worse ORR and DCR than those in the VETC- group (RECIST: ORR: 25.8% vs. 47.4%, P = 0.031; DCR: 56.1% vs. 76.3%, P = 0.007; mRECIST: ORR: 41.0% vs. 52.6%, P = 0.008; DCR: 56.1% vs. 76.3%, P = 0.007). Patients with VETC+ had significantly shorter OS and PFS than those with VETC- (median OS: 10.2 vs. 21.6 months, P < 0.001; median PFS: 3.3 vs. 7.2 months, P < 0.001). Multivariate analysis revealed VETC status as an independent prognostic factor for OS (HR: 2.40; 95% CI: 1.46-3.94; P = 0.001) and PFS (HR: 1.97; 95% CI: 1.20-3.22; P = 0.007).

CONCLUSION

VETC status correlates remarkably well with the tumor response and long-term survival in patients undergoing HAIC. It may be a promising efficacy predictor and help identify patients who will benefit from HAIC.

Preoperative MRI features to predict vessels that encapsulate tumor clusters and microvascular invasion in hepatocellular carcinoma

OBJECTIVE

To estimate the potential of preoperative MRI features in the prediction of the integration patterns of vessels that encapsulate tumor clusters (VETC) and microvascular invasion (MVI) (VM) patterns in hepatocellular carcinoma (HCC) patients after resection and to assess the prognostic value of VM patterns.

MATERIALS AND METHODS

Patients who underwent surgical resection for HCC between July 2019 and July 2020 were retrospectively included in the training cohort and validation cohort. In the training cohort, patients were classified into VM-positive HCC (VM-HCC) and VM-negative HCC (non-VM HCC). Predictors associated with VM-HCC were determined by using logistic regression analyses and used to build a prediction model of VM-HCC. The model was tested in the validation cohort by area under the receiver operating characteristic curve (AUC) analysis. Prognostic factors associated with early recurrence of HCC were evaluated by use of Cox logistic regression analyses.

RESULTS

Alpha-fetoprotein (AFP) level higher than 400 ng/mL (odds ratio [OR] = 8.0; 95% CI: 2.6-25.2; P < 0.001), non-smooth tumor margin (OR = 3.1; 95% CI: 1.4-6.0; P < 0.001) and peritumoral arterial enhancement (OR = 2.9; 95% CI: 1.4-6.2; P = 0.004) were independent predictors of VM-HCC. The AUCs of the prediction model for VM-HCC were 0.81 for the training cohort and 0.79 for the validation cohort. The high risk of VM-HCC predicted by the three preoperative predictors derived from the prediction model (hazard ratio [HR] 2.0; 95% CI: 1.3, 3.2; P = 0.003) were independently associated with early recurrence, while pathologically confirmed VM-HCC (HR 2.8; 95% CI: 1.6, 3.8; P < 0.001) and satellite nodules (HR 1.8; 95% CI: 1.1, 3.1; P = 0.025) were independently associated with early recurrence after surgical resection.

CONCLUSION

The predictive model can be used to predict VM patterns. VM-HCC is associated with increased risk of early recurrence after surgical resection in HCC.

Hepatocellular carcinoma: molecular mechanism, targeted therapy, and biomarkers

Hepatocellular carcinoma (HCC) is a common malignancy and one of the leading causes of cancer-related death. The biological process of HCC is complex, with multiple factors leading to the broken of the balance of inactivation and activation of tumor suppressor genes and oncogenes, the abnormal activation of molecular signaling pathways, the differentiation of HCC cells, and the regulation of angiogenesis. Due to the insidious onset of HCC, at the time of first diagnosis, less than 30% of HCC patients are candidates for radical treatment. Systematic antitumor therapy is the hope for the treatment of patients with middle-advanced HCC. Despite the emergence of new systemic therapies, survival rates for advanced HCC patients remain low. The complex pathogenesis of HCC has inspired researchers to explore a variety of biomolecular targeted therapeutics targeting specific targets. Correct understanding of the molecular mechanism of HCC occurrence is key to seeking effective targeted therapy. Research on biomarkers for HCC treatment is also advancing. Here, we explore the molecular mechanism that are associated with HCC development, summarize targeted therapies for HCC, and discuss potential biomarkers that may drive therapies.

Effect of vessels that encapsulate tumor clusters (VETC) on the prognosis of different stages of hepatocellular carcinoma after hepatectomy

BACKGROUND

Vessels that encapsulate tumor clusters (VETC) is a newly discovered vascular pattern in hepatocellular carcinoma (HCC), representing high biological aggressiveness. However, it remains unclear whether the prognostic impact of VETC differs in patients with different staged HCC. This study aimed to evaluate the effect of VETC on the prognosis of patients with HCC at different stages after hepatectomy.

METHODS

Patients who underwent hepatectomy for HCC between January 2005 and December 2019 were assessed, and stratified according to their Barcelona Clinic Liver Cancer (BCLC) stage. Overall survival (OS) and disease-free survival (DFS) were compared between patients with and without VETC. Independent risk factors of OS and DFS were determined by multivariable Cox regression analyses.

RESULTS

A total of 837 consecutive patients undergoing curative hepatectomy were enrolled, and VETC pattern was found in 339 (40.5%) patients. The incidence of VETC in patients at BCLC-0, BCLC-A, BCLC-B and BCLC-C stage was 17.8%, 40.2%, 53.7% and 66.0%, respectively. In the entire patients, VETC+ patients had significantly lower OS and DFS than VETC- patients. After stratification of patients according to BCLC stage, VETC was associated with worse OS and DFS only in patients at BCLC-A and BCLC-B stages, but not in those at BCLC-0 and BCLC-C stages. Multivariable analyses also revealed that VETC was an independent risk factor for OS and DFS in both the patients at BCLC-A and BCLC-B stages.

CONCLUSIONS

VETC is associated with poor OS and DFS in patients with HCC at BCLC-A and BCLC-B stage after hepatectomy, but it does not affect the survival of patients with HCC at BCLC-0 and BCLC-C stage after hepatectomy.

Vessels that encapsulate tumour clusters vascular pattern in hepatocellular carcinoma

Vessels that encapsulate tumour clusters (VETC) is a distinct histologic vascular pattern associated with a novel mechanism of metastasis. First described in human cancers in 2004, its prevalence and prognostic significance in hepatocellular carcinoma (HCC) has only been appreciated in the past decade with a rapidly increasing body of literature. A robust biomarker of aggressive disease, the VETC pattern is easy to recognise but relies on histologic examination of tumour tissue for its diagnosis. Radiological recognition of the VETC pattern is an area of active research and is becoming increasingly accurate. As a prognostic marker, VETC has consistently proven to be an independent predictor of disease recurrence and overall survival in patients with HCC undergoing resection and liver transplantation. It can also guide treatment by predicting response to other therapies such as transarterial chemoembolisation and sorafenib. Without prospective randomised-controlled trials or routine evaluation of VETC in clinical practice, there are currently no firm treatment recommendations for VETC-positive tumours, although some perspectives are provided in this review based on the latest knowledge of their pathogenesis - a complex interplay between tumour angiogenesis and the immune microenvironment. Nevertheless, VETC has great potential as a future biomarker that could take us one step closer to precision medicine for HCC.

Promising Novel Biomarkers for Hepatocellular Carcinoma: Diagnostic and Prognostic Insights

The systemic therapy landscape for hepatocellular carcinoma is rapidly evolving, as the recent approvals of checkpoint inhibitor-based regimens such as atezolizumab-bevacizumab and durvalumab-tremelimumab in advanced disease have led to an expanding therapeutic armamentarium. The development of biomarkers, however, has not kept up with the approvals of new agents. Nevertheless, biomarker research for hepatocellular carcinoma has recently been growing at a rapid pace. The most active areas of research are biomarkers for early detection and screening, accurate prognostication, and detection of minimal residual disease following curative intent therapies, and, perhaps most importantly, predictive markers to guide selection and sequencing of the individual agents, including tyrosine kinase inhibitors and immunotherapy. In this review, we briefly summarize the recent developments in systemic therapeutics for hepatocellular carcinoma, introduce the key completed and ongoing prospective and retrospective studies evaluating diagnostic, prognostic, and predictive biomarkers with high clinical relevance, highlight several potentially important areas of future research, and share our insights for each biomarker.

Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study

Background

Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis.

Purpose

To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC.

Methods

86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance.

Results

Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively).

Conclusion

DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.

Preoperative MRI features for characterization of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma

PURPOSE

This study aimed to analyze imaging features based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the identification of vessels encapsulating tumor clusters (VETC)-microvascular invasion (MVI) in hepatocellular carcinoma (HCC), VM-HCC pattern.

METHODS

Patients who underwent hepatectomy and preoperative DCE-MRI between January 2015 and March 2021 were retrospectively analyzed. Clinical and imaging features related to VM-HCC (VETC + /MVI-, VETC-/MVI +, VETC + /MVI +) and Non-VM-HCC (VETC-/MVI-) were determined by multivariable logistic regression analyses. Early and overall recurrence were determined using the Kaplan-Meier survival curve. Indicators of early and overall recurrence were identified using the Cox proportional hazard regression model.

RESULTS

In total, 221 patients (177 men, 44 women; median age, 60 years; interquartile range, 52-66 years) were evaluated. The multivariable logistic regression analyses revealed fetoprotein > 400 ng/mL (odds ratio [OR] = 2.17, 95% confidence interval [CI] 1.07, 4.41, p = 0.033), intratumor vascularity (OR 2.15, 95% CI 1.07, 4.31, p = 0.031), and enhancement pattern (OR 2.71, 95% CI 1.17, 6.03, p = 0.019) as independent predictors of VM-HCC. In Kaplan-Meier survival analysis, intratumor vascularity was associated with early and overall recurrence (p < 0.05).

CONCLUSION

Based on DCE-MRI, intratumor vascularity can be used to characterize VM-HCC and is of prognostic significance for recurrence in patients with HCC.

Vessels That Encapsulate Tumor Clusters (VETC) Predict cTACE Response in Hepatocellular Carcinoma

Background

To investigate the correlation between hepatocellular carcinoma (HCC) pathological types and conventional transarterial chemoembolization (cTACE), and to evaluate the predictive value of the pathological types for efficacy of cTACE.

Methods

We investigated 186 naive HCC patients from 2 hospitals, including 63 patients with recurrence after surgical resection, and 123 unresectable cases, who underwent at least one cTACE procedure as the first treatment. All patients were histologically diagnosed with HCC by surgical resection and/or liver biopsy. Lipiodol deposition rate, ORR (objective response rate), PFS (progression-free survival), OS (overall survival) were compared among different HCC pathological types.

Results

This study evaluated 186 naive HCC patients and 189 tumor nodules. Vessels that encapsulate tumor clusters (VETC), macrotrabecular-massive (MTM), CK19-positive types were identified in 38% (72/189), 40% (76/189), and 28% (53/189) of the whole cohort, respectively. VETC, MTM and CK19-negative HCCs derived significantly better lipiodol deposition rate and ORR. cTACE prolonged the PFS of VETC and CK19-negative HCCs compared with non-VETC and CK19-positive HCCs in the recurrence, liver biopsy and combining whole cohorts, whereas the OSs of different pathological types were not significantly different. Multivariate analysis showed that VETC (OR, 4.671, 95% CI [1.954, 11.166], P<0.001) and CK19-positive type (OR, 0.127, 95% CI [0.044, 0.362], P<0.001) were independent predictive factors for the first cTACE response. However, only VETC type was significantly associated with the second cTACE response in multivariate analysis (OR, 3.31, 95% CI [1.24, 8.83], P=0.017), suggesting that VETC might be a more useful predictor of cTACE response.

Conclusion

Our study suggests that VETC is an effective predictor of cTACE response in patients with HCC.

Immunovascular microenvironment in relation to prognostic heterogeneity of WNT/β-catenin-activated hepatocellular carcinoma

AIM

WNT/β-catenin-activated hepatocellular carcinoma (W/B subclass HCC) is considered a molecularly homogeneous entity and has been linked to resistance to immunotherapy. However, recent studies have indicated possible heterogeneity in the immunovascular microenvironment in this subclass. We set out to test the hypothesis that specific immunovascular features might stratify W/B subclass HCCs into tumors having distinct aggressive natures.

METHODS

In this study, we analyzed 352 resected HCCs including 78 immunohistochemically defined W/B subclass HCCs. The density of tumor-infiltrating CD3⁺ T cells and the area ratio of vessels encapsulating tumor clusters (VETC) were calculated on tissue specimens. The gene expressions of angiogenic factors were measured by quantitative reverse transcription-polymerase chain reaction. Disease-free survival (DFS) was assessed using multivariable Cox regression analyses.

RESULTS

The T-cell density of W/B subclass HCCs was regionally heterogenous within tumor tissues, and focally reduced T-cell density was observed in areas with VETC. VETC-positivity (defined as VETC area ratio greater than 1%) was inversely associated with T-cell infiltration in both W/B subclass and non-W/B subclass HCCs. Fibroblast growth factor 2 (FGF2) gene expression was higher in W/B subclass than in non-W/B subclass HCCs. The VETC-positivity and low T-cell density correlated with increased expression of FGF2 in W/B subclass HCCs. Additionally, VETC-positive HCCs showed significantly shorter DFS in W/B subclass HCCs.

CONCLUSIONS

In conclusion, the immune and vascular microenvironments are interrelated and are also correlated with clinicopathological heterogeneity in W/B subclass HCC. These results could inform clinical practice and translational research on the development of therapeutic stratification of HCCs.

The Vessels That Encapsulate Tumor Clusters (VETC) Pattern Is a Poor Prognosis Factor in Patients with Hepatocellular Carcinoma: An Analysis of Microvessel Density

The outcomes of patients with hepatocellular carcinoma (HCC) are unsatisfactory because of its high recurrence rate. The Vessels that encapsulate tumor clusters (VETC) pattern is a unique vascular structure. In this study, we investigated the clinical−pathological features of HCC patients with the VETC pattern. We retrospectively reviewed patients with HCC who underwent curative hepatectomy at Chang Gung Memorial Hospital between 2007 and 2013. The form of the VETC pattern was established using an anti-CD31 stain. The results were classified into positive (VETC+) and negative (VETC−) patterns. We investigated and compared demographic data between these two groups. Overall, 174 patients were classified into either the VETC+ or VETC− groups. The median followed-up period was 80.5 months. There were significant differences in the number of hepatitis B carriers, the occurrence of vascular invasion, tumor size, TNM staging, microvessel density, and recurrence (all p < 0.05). Regarding the prediction of disease-free survival, after COX regression multivariate analysis, VETC+ remained independently associated with recurrent episodes (p = 0.003). The intra-tumoral microvessel density, demonstrated by CD-31, was the only clinical−pathological feature independently associated with VETC+. Our study demonstrated that the VETC pattern is an independent factor of poor prognosis for DFS. Higher intra-tumoral microvessel density was significantly associated with the VETC pattern. Further studies are needed to validate our findings.

Association between vessels that encapsulate tumour clusters vascular pattern and hepatocellular carcinoma recurrence following liver transplantation

BACKGROUND

Vessels that encapsulate tumor clusters (VETC) is a novel vascular pattern seen on hepatocellular carcinoma (HCC) histology which has been shown to independently predict tumor recurrence and survival after liver resection. Its prognostic value in HCC patients receiving liver transplantation (LT) is unclear.

METHODS

We retrospectively studied consecutive adults who underwent deceased-donor LT with active HCC found on explant between 2010-2019. Tumor tissue was stained for CD34 and quantified for VETC. Primary and secondary endpoints were time to recurrence (TTR) and recurrence-free survival (RFS).

RESULTS

During the study period, 158 patients received LT where HCC was present on explant. VETC pattern was seen in 76.5% of explants. Patients with VETC-positive tumors spent longer on the waitlist (6.4 vs. 4.1 months, P=0.048), had higher median tumor numbers (2 vs. 1, P=0.001) and larger tumor sizes (20mm vs. 13mm, P<0.001) on explant pathology compared to those with VETC-negative tumors. Correspondingly, VETC-positive patients were more likely to be outside of accepted LT criteria for HCC. After 56.4 months median follow-up, 8.2% of patients developed HCC recurrence post-LT. On multivariable Cox regression, presence of VETC pattern did not predict TTR or RFS. However, the number of VETC-positive tumors on explant was an independent predictor of TTR (hazard ratio [HR] 1.411, P=0.001) and RFS (HR 1.267, P=0.014) after adjusting for other significant variables.

CONCLUSION

VETC pattern is commonly observed in HCC patients undergoing LT. The number of VETC-positive tumors, but not its presence, is an independent risk factor for TTR and RFS post-LT.

Nomogram Estimating Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma From Preoperative Gadoxetate Disodium-Enhanced MRI

BACKGROUND

Vessels encapsulating tumor clusters (VETC) pattern is a novel microvascular pattern associated with poor outcomes of hepatocellular carcinoma (HCC). Preoperative estimation of VETC has potential to improve treatment decisions.

PURPOSE

To develop and validate a nomogram based on gadoxetate disodium-enhanced MRI for estimating VETC in HCC and to evaluate whether the estimations are associated with recurrence after hepatic resection.

STUDY TYPE

Retrospective.

POPULATION

A total of 320 patients with HCC and histopathologic VETC pattern assessment from three centers (development cohort:validation cohort = 173:147).

FIELD STRENGTH/SEQUENCE

A3.0  T/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, and 3D T1-weighted gradient-echo sequences.

ASSESSMENT

A set of previously reported VETC- and/or prognosis-correlated qualitative and quantitative imaging features were assessed. Clinical and imaging variables were compared based on histopathologic VETC status to investigate factors indicating VETC pattern. A regression-based nomogram was then constructed using the significant factors for VETC pattern. The nomogram-estimated VETC stratification was assessed for its association with recurrence.

STATISTICAL TESTS

Fisher exact test, t-test or Mann-Whitney test, logistic regression analyses, Harrell's concordance index (C-index), nomogram, Kaplan-Meier curves and log-rank tests. P value < 0.05 was considered statistically significant.

RESULTS

Pathological VETC pattern presence was identified in 156 patients (development cohort:validation cohort = 83:73). Tumor size, presence of heterogeneous enhancement with septations or with irregular ring-like structures, and necrosis were significant factors for estimating VETC pattern. The nomogram incorporating these indicators showed good discrimination with a C-index of 0.870 (development cohort) and 0.862 (validation cohort). Significant differences in recurrence rates between the nomogram-estimated high-risk VETC group and low-risk VETC group were found (2-year recurrence rates, 50.7% vs. 30.3% and 49.6% vs. 31.8% in the development and validation cohorts, respectively).

DATA CONCLUSION

The nomogram integrating gadoxetate disodium-enhanced MRI features was associated with VETC pattern preoperatively and with postoperative recurrence in patients with HCC.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Prognostic impact of vessels encapsulating tumor clusters and macrotrabecular patterns in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) shows a high mortality rate. A macrotrabecular (MT) pattern and vessels encapsulating tumor clusters (VETC) pattern have been reported as aggressive histological patterns in HCC. However, their cut-off values have been contentious.

METHOD

Nine hundred eighty-five cases of previously diagnosed HCC were enrolled. The percentage areas of the MT and/or VETC pattern with ≥ 5% at every 10% increment were assessed. Clinicopathological analysis including patients' prognosis was conducted.

RESULT

One hundred fifty-eight and eighty-four cases were accompanied by 5-49% and ≥ 50% MT components, respectively. Two hundred six and twenty-nine cases had 5-49% and ≥ 50% VETC components, respectively. Cases with these histological patterns in common had aggressive characteristics and worse prognosis compared to cases with none of these patterns. The presence of 5-49% VETC pattern was independent worse prognostic factor in overall survival (P = 0.046). HCCs with the MT pattern and the VETC pattern were significantly accompanied by the VETC pattern and the MT pattern (P < 0.001), respectively.

CONCLUSION

As even 5% of the MT pattern and/or VETC pattern affected the prognosis of patients with HCC, the amount of these pattern should be described in pathological reports. This information could be useful in expecting patients' prognosis and providing proper post-operative treatments.

Vessels that encapsulate tumor clusters (VETC) pattern predicts the efficacy of adjuvant TACE in hepatocellular carcinoma

PURPOSE

Postoperative adjuvant trans-catheter arterial chemoembolization (TACE) is regarded as a common strategy for hepatocellular carcinoma (HCC) patients at a high risk of recurrence. However, there are currently no clinically available biomarkers to predict adjuvant TACE response. Vessels that encapsulate tumor clusters (VETC) can be used as an independent predictor of HCC prognosis. In this study, we aimed to explore whether the VETC pattern could predict adjuvant TACE benefit.

METHODS

Vascular pattern and HIF-1α expression were detected in immunohistochemistry. The survival benefit of adjuvant TACE therapy for patients with or without VETC pattern (VETC+ /VETC-) was evaluated.

RESULTS

The adjuvant TACE therapy obviously improved the TTR and OS in VETC+ patients, while adjuvant TACE therapy could not benefit from VETC- patients. Univariate and multivariate analysis revealed that adjuvant TACE therapy significantly improved the TTR and OS in VETC+ patients, but not in VETC- patients. In addition, the VETC+ , but not VETC- , patients could benefit from adjuvant TACE therapy in patients with high-risk factors of vascular invasion, larger tumor or multiple tumor. The mechanistic investigations revealed that the favorable efficacy of adjuvant TACE on VETC+ patients, but not VETC- ones, may be not due to the activation of HIF-1α pathway.

CONCLUSION

The VETC pattern may represent a novel and reliable factor for selecting HCC patients who may benefit from adjuvant TACE therapy, and the combination of VETC pattern and tumor characteristics may help stratify patients' outcomes and responses to adjuvant TACE therapy.

Approaches to spatially resolving the tumour immune microenvironment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common and deadly cancer worldwide. Many factors contribute to mortality and place an individual at high risk of developing HCC, including viral infection, alcohol intake, metabolic-associated disease, autoimmunity and genetic liver disorders. Although there are many therapeutics available, much about this disease remains to be understood. This is most evident when investigating the tumour microenvironment (TME). Both innate and adaptive immune cells have been associated with carcinogenesis within the TME of HCC patients. The ability to interrogate the TME more thoroughly with spatial technologies continues to improve, both at the experimental and analytical stages. This review provides insight into technologies available to investigate the TME, and how such technologies are beneficial for improving our understanding of HCC carcinogenesis.

Implications of genetic heterogeneity in hepatocellular cancer

Hepatocellular carcinoma (HCC) exhibits a remarkable degree of heterogeneity, not only at an inter-patient level but also between and within tumors in the same patient. The advent of next-generation sequencing (NGS)-based technologies has allowed the creation of high-resolution atlases of HCC. This review outlines recent findings from genomic, epigenomic, transcriptomic, and proteomic sequencing that have yielded valuable insights into the spatial and temporal heterogeneity of HCC. The high heterogeneity of HCC has both clinical and therapeutic implications. The challenges in prospectively validating molecular classifications for HCC either for prognostication or for prediction of therapeutic response are partly due to the immense heterogeneity in HCC. Moreover, the heterogeneity of HCC tumors combined with the lack of commonly mutated, druggable targets severely limits treatment options for HCC. Recently, immune checkpoint inhibitors and combination therapies have shown promise for advanced HCC, while T cell therapies and vaccines are currently being investigated. Yet, immunotherapies show benefit only in a limited subset of patients, making it imperative to decipher tumor heterogeneity in HCC in order to enable optimal patient selection. This review summarizes the cutting-edge research on heterogeneity in HCC and explores the implications of heterogeneity on stratifying patients and developing biomarkers and therapies for HCC.

Development and Validation of a Novel Nomogram for Predicting Vessels that Encapsulate Tumor Cluster in Hepatocellular Carcinoma

BACKGROUND

Vessels that encapsulate tumor cluster (VETC) is associated with poor prognosis in hepatocellular carcinoma (HCC). Vessels that encapsulate tumor cluster estimation before initial treatment is helpful for clinical doctors. We aimed to construct a novel predictive model for VETC, using preoperatively accessible clinical parameters and imagine features.

METHODS

Totally, 365 HCC patients who received curative hepatectomy in the Sun Yat-Sen University Cancer Center from 2013 to 2014 were enrolled in this study. Vessels that encapsulate tumor cluster pattern was confirmed by immunochemistry staining. 243 were randomly assigned to the training cohort while the rest was assigned to the validation cohort. Independent predictive factors for VETC estimation were determined by univariate and multivariate logistic analysis. We further constructed a predictive nomogram for VETC in HCC. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curve, and calibration curve. Besides, the decision curve was plotted to evaluate the clinical usefulness. Ultimately, Kaplan-Meier survival curves were utilized to confirm the association between the nomogram and survival.

RESULTS

Immunochemistry staining revealed VETC in 87 patients (23.8%). lymphocyte to monocyte ratio (>7.75, OR = 4.06), neutrophil (>7, OR = 4.48), AST to ALT ratio (AAR > .86, OR = 2.16), ALT to lymphocyte ratio index (BLRI > 21.73, OR = 2.57), alpha-fetoprotein (OR = 1.1), and tumor diameter (OR = 2.65) were independent predictive factors. The nomogram incorporating these predictive factors performed well with an area under the curve (AUC) of .746 and .707 in training and validation cohorts, respectively. Calibration curves indicated the predicted probabilities closely corresponded with the actual VETC status. Moreover, the decision curve proved our nomogram could provide clinical benefits with patients. Finally, low probability of VETC group had significantly longer recurrence free survival (RFS) and overall survival (OS) than the high probability of the VETC group (all P < .001).

CONCLUSION

A novel predictive nomogram integrating clinical indicators and image characteristics shows strong predictive VETC performance and might provide standardized net clinical benefits.

Lung and lymph node metastases from hepatocellular carcinoma: Comparison of pathological aspects

BACKGROUND & AIMS

Extrahepatic metastasis from hepatocellular carcinoma (HCC) is a catastrophic event, yet organ-specific pathological characteristics of metastatic HCC remain unclear. We aimed to characterize the pathological aspects of HCC metastases to various organs.

METHODS

We collected intrahepatic HCC (cohort 1, n = 322) and extrahepatic metastatic HCC (cohort 2, n = 130) samples. Clinicopathological evaluation and immunostaining for K19, CD34, αSMA, fibroblast-associated protein (FAP), CAIX, VEGF, PD-L1, CD3, CD8, Foxp3, CD163 and epithelial-mesenchymal transition (EMT)-related markers were performed.

RESULTS

Independent factors for extrahepatic metastasis included BCLC stage B-C, microvascular invasion (MVI), vessels encapsulating tumour clusters (VETC)-HCC, K19 and FAP expression, and CD163+ macrophage infiltration (cohort 1, P < .05 for all). Lung metastases (n = 63) had the highest proportion of VETC-HCC and macrotrabecular-massive (MTM)-HCC. Lymph node metastases (n = 19) showed significantly high rates of EMT-high features, K19 expression, fibrous tumour stroma with αSMA and FAP expression, high immune cell infiltration, PD-L1 expression (combined positive score), CD3+, CD8+, Foxp3+ T cell and CD163+ macrophage infiltration (adjusted P < .05 for all). In both cohorts, EMT-high HCCs showed higher rates of K19 expression, fibrous tumour stroma, high immune cell infiltration, PD-L1 expression and CD3+ T cell infiltration, whereas EMT-low HCCs were more frequent among VETC-HCCs (P < .05 for all). Overall phenotypic features were not significantly different between paired primary-metastatic HCCs (n = 32).

CONCLUSIONS

Metastatic HCCs to various organs showed different pathological features. VETC and MTM subtypes were related to lung metastasis, whereas K19 expression, EMT-high features with fibrous tumour stroma and high immune cell infiltration were related to lymph node metastasis.

[Up-to-date Knowledge on the Pathological Diagnosis of Hepatocellular Carcinoma]

Hepatocellular carcinoma (HCC) has heterogeneous molecular and pathological features and biological behavior. Large-scale genetic studies of HCC were accumulated, and a pathological-molecular classification of HCC was proposed. Approximately 35% of HCCs can be classified into distinct histopathological subtypes according to their molecular characteristics. Among recently identified subtypes, macrotrabecular massive HCC, neutrophil-rich HCC, vessels encapsulating tumor clusters HCC, and progenitor phenotype HCC (HCC with CK19 expression) are associated with a poor prognosis, whereas the lymphocyte-rich HCC subtype is related to a better prognosis. This review provides up-to-date knowledge on the pathological diagnosis of HCC according to the updated World Health Organization Classification of Digestive System Tumors 5th ed.

Dual and opposing roles of the androgen receptor in VETC-dependent and invasion-dependent metastasis of hepatocellular carcinoma

BACKGROUND & AIMS

Contradictory roles of the androgen receptor (AR) in hepatocellular carcinoma (HCC) metastasis have been reported. We have shown that VETC (vessels encapsulating tumor clusters) mediates invasion-independent metastasis, whereas VETC^- HCCs metastasize in an invasion-dependent manner. Herein, we aimed to reveal the roles of AR in HCC metastasis.

METHODS

Mouse xenograft models, clinical samples, and cell models were used.

RESULTS

AR expression was significantly lower in HCCs with a VETC pattern, portal vein tumor thrombus, endothelium-coated microemboli or high recurrence rates. Overexpressing AR in VETC⁺ hepatoma cells suppressed VETC formation and intrahepatic metastasis but promoted pulmonary metastasis of mouse xenografts. AR decreased the transcription of Angiopoietin-2 (Angpt2), a factor essential for VETC formation, by binding to the Angpt2 promoter. The roles of AR in inhibiting VETC formation and intrahepatic metastasis were attenuated by restoring Angpt2 expression, suggesting that AR may repress VETC-dependent intrahepatic metastasis by inhibiting Angpt2 expression and VETC formation. On the other hand, AR upregulated Rac1 expression, promoted lamellipodia formation and increased cell migration/invasion. A Rac1 inhibitor abrogated the AR-mediated promotion of migration/invasion and pulmonary metastasis of VETC⁺ hepatoma cells, but did not affect the AR-mediated inhibition of intrahepatic metastasis. Furthermore, an AR inhibitor decreased Rac1 expression and attenuated both intrahepatic and pulmonary metastasis of VETC^- xenografts, an effect which was abrogated by restoring Rac1 expression. These data indicate that AR may facilitate the lung metastasis of VETC⁺ HCCs and both the liver/lung metastases of VETC^- HCCs by upregulating Rac1 expression and then promoting migration/invasion.

CONCLUSION

AR plays dual and opposing roles in VETC-dependent and invasion-dependent metastasis, which highlights the complex functions of AR and the importance of individualized cancer therapy.

LAY SUMMARY

In this study, we uncovered the dual and opposing roles of the androgen receptor in VETC (vessels encapsulating tumor clusters)-dependent and invasion-dependent metastasis of hepatocellular carcinoma (HCC). We elucidated the underlying mechanisms of these processes, which provided novel insights into the complex regulatory network of the androgen receptor in HCC metastasis and may have important implications for precision medicine.

Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma

OBJECTIVES

The study was to develop a Gd-EOB-DTPA-enhanced MRI radiomics model for preoperative prediction of VETC and patient prognosis in hepatocellular cancer (HCC).

METHODS

The study included 182 (training cohort: 128; validation cohort: 54) HCC patients who underwent preoperative Gd-EOB-DTPA-enhanced MRI. Volumes of interest including intratumoral and peritumoral regions were manually delineated in the hepatobiliary phase images, from which 1316 radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the useful features. Clinical, intratumoral, peritumoral, combined radiomics, and clinical radiomics models were established using machine learning algorithms. The Kaplan-Meier survival analysis was used to assess early recurrence and progression-free survival (PFS) in the VETC + and VETC- patients.

RESULTS

In the validation cohort, the area under the curves (AUCs) of radiomics models were higher than that of the clinical model using random forest (all p < 0.05). The peritumoral radiomics model (AUC = 0.972;95% confidence interval [CI]:0.887-0.998) had significantly higher AUC than intratumoral model (AUC = 0.919; 95% CI: 0.811-0.976) (p = 0.044). There were no significant differences in AUC between intratumoral or peritumoral radiomics model (PR) and combined radiomics model (p > 0.05). Early recurrence and PFS were significantly different between the PR-predicted VETC + and VETC- HCC patients (p < 0.05). PR-predicted VETC was independent predictor of early recurrence (hazard ratio [HR]: 2.08[1.31-3.28]; p = 0.002) and PFS (HR: 1.95[1.20-3.17]; p = 0.007).

CONCLUSIONS

The intratumoral or peritumoral radiomics model may be useful in predicting VETC and patient prognosis preoperatively. The peritumoral radiomics model may yield an incremental value over intratumoral model.

KEY POINTS

• Radiomics models are useful for predicting vessels encapsulating tumor clusters (VETC) and patient prognosis preoperatively. • Peritumoral radiomics model may yield an incremental value over intratumoral model in prediction of VETC. • Peritumoral radiomics-model-predicted VETC was an independent predictor of early recurrence and progression-free survival.