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.

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.

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.

Potential involvement of oncostatin M in the immunosuppressive tumor immune microenvironment in hepatocellular carcinoma with vessels encapsulating tumor clusters.

AIM: Vessels encapsulating tumor clusters (VETC) represents an adverse prognostic morphological feature of hepatocellular carcinoma (HCC), which is associated with an immunosuppressive tumor immune microenvironment (TIM). However, the underlying factors characterizing the TIM in HCC with a VETC pattern (VETC-positive HCC) remain uncertain. Oncostatin M (OSM), a pleiotropic cytokine of the interleukin-6 family, regulates various biological processes, including inflammation, proliferation, and invasiveness of tumor cells. We aimed to test a hypothesis that OSM is associated with the immunosuppressive TIM of VETC-positive HCC. METHODS: A total of 397 consecutive HCC patients with curative-intent hepatectomy were included. OSM-positive cells and inflammatory cells including CD4-, CD8-, CD163-, and FOXP3-positive cells were immunohistochemically evaluated. We compared VETC-positive and VETC-negative HCCs in terms of the number of these cells. RESULTS: We found the VETC pattern in 62 patients (15.6%). Our analysis revealed a significant decrease in the expression of arginase-1, a marker associated with mature hepatocyte differentiation, in VETC-positive HCC (p = 0.046). The number of tumor-infiltrating OSM-positive cells was significantly low in VETC-positive HCC (p = 0.0057). Notably, in VETC-positive HCC, the number of OSM-positive cells was not associated with vascular invasion, whereas in VETC-negative HCC, an increase in the number of OSM-positive cells was associated with vascular invasion (p = 0.042). CONCLUSIONS: We identified an association between a decrease in OSM-positive cells and the VETC pattern. Additionally, our findings indicate that VETC-positive HCC is characterized by low hepatocyte differentiation and OSM-independent vascular invasion. These findings highlight the potential interaction between VETC-positive HCC cells and their TIM through the reduction of OSM-expressing cells.

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.

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

AIM: WNT/ß-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 presence of vessels encapsulating tumor clusters is associated with an immunosuppressive tumor microenvironment in hepatocellular carcinoma.

Recently, a distinct vascular pattern in hepatocellular carcinoma (HCC) called vessels encapsulating tumor-forming clusters (VETC) has received attention because of its association with poor prognosis. However, little is known about the mechanism by which VETC promotes an aggressive phenotype at the molecular level. In our study, the association between differences in stepwise signal intensity in the HB phase and molecular subtypes and somatic mutations associated with the immune microenvironment were investigated using the International Cancer Genome Consortium (ICGC) cohort (66 patients). To our knowledge, this is the first study to analyze the molecular patterns of VETC using RNA-Seq data. The VETC+ HCC group showed significantly lower overall survival and higher cumulative incidence of extrahepatic metastasis after curative hepatic resection than the VETC- HCC group. The VETC+ group exhibited molecular features indicative of lower immune activation than the VETC- group, suggesting that tumor cells in the VETC+ group were more likely to escape from the immune response, which could lead to the shorter OS (Overall survival) and higher risk of metastasis. On the other hand, gene expression levels of fibroblast growth factor receptors were upregulated in VETC+ HCC, suggesting that VETC+ HCC might benefit from lenvatinib treatment. Our results demonstrate that VETC+ HCC was associated with the suppression of tumor immune responses at the molecular level.

Radiomic analysis of Gd-EOB-DTPA-enhanced MRI predicts Ki-67 expression in hepatocellular carcinoma.

BACKGROUND: Nuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC. METHODS: This retrospective study included 151 (training cohort: n = 103; validation cohort: n = 48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility. RESULTS: The AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs. 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC = 0.873 vs. 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30-60%, the combined model added more net benefit compared with the AP radiomics model. CONCLUSIONS: A combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC.

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 â€‹% CI: 1.11, 21.68; P â€‹< â€‹0.05), intratumoral necrosis (OR, 4.99; 95 â€‹% CI: 1.25, 19.99; P â€‹< â€‹0.05) and enhancing capsule (OR, 3.32; 95 â€‹% CI: 1.27, 8.94; P â€‹< â€‹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 â€‹% CI: 1.06, 3.86; P â€‹< â€‹0.05), the number of tumors (HR, 3.31; 95 â€‹% CI: 1.47, 7.45; P â€‹< â€‹0.05) and VETC pattern (HR, 2.52; 95 â€‹% CI: 1.31, 4.86; P â€‹< â€‹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.

A Nomogram Based on Features of Ultrasonography and Contrast-Enhanced CT to Predict Vessels Encapsulating Tumor Clusters Pattern of Hepatocellular Carcinoma.

OBJECTIVE: This study aimed to establish a clinical prediction model for vessels encapsulating tumor clusters (VETC) based on preoperative ultrasonography (US) and contrast-enhanced computed tomography (CECT) imaging in patients with hepatocellular carcinoma (HCC). METHODS: Data were retrospectively collected from 215 patients who underwent hepatectomy for solitary HCC lesions. They were divided into training and validation cohorts at a ratio of 6:4. Preoperative imaging features were extracted (seven from US and nine from CECT imaging) to explore their relationship with VETC. A VETC prediction model was constructed and graphically depicted as a nomogram. Its performance was evaluated via the receiver operating characteristic (ROC) curve, the calibration curve, and decision curve analysis (DCA). RESULTS: The VETC incidence for all the lesions was 37.7%. The final variables included in the nomogram were "peritumoral enhancement in CECT", "alpha-fetoprotein level > 200 ng/Ml," "halo in US," "capsule enhancement in CECT," and "posterior acoustic enhancement in US." The area under the curve (AUC) values for the training and validation cohorts were 0.824 and 0.725, respectively. The Hosmer-Lemeshow fit test showed no statistical difference (p = 0.369 and p = 0.067 for the training and validation cohorts, respectively). DCA demonstrated that our nomogram provided clinical benefits to a wide range of patients. According to the nomogram score, the VETC-positive and -negative groups demonstrated significant differences in both the training (p < 0.001) and validation (p = 0.001) cohorts. CONCLUSION: Our prediction model based on US and CECT imaging features can accurately predict VETC in HCC.

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.

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.

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.

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.

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.

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.

Immunophenotypes and Tumor Immune Microenvironment in Hepatocellular Carcinoma With Macrotrabecular Massive and Vessels Encapsulating Tumor Clusters.

BACKGROUND/AIM: Recently, vessels encapsulating tumor clusters (VETC) pattern and macrotrabecular massive (MTM) pattern of hepatocellular carcinoma (HCC) have been reported as aggressive histological types. These histological patterns showed an immunosuppressive tumor immune microenvironment (TIME). Since there have been no reports on the differences of these two subtypes simultaneously, this study examined the immunophenotypes and TIME of MTM-HCC and VETC-HCC immunohistochemically. PATIENTS AND METHODS: Seventy-four cases of previously diagnosed HCC, including 32 MTM-HCCs, 21 VETC-HCCs, and 21 conventional HCCs, were enrolled in immunohistochemical analysis. We conducted immunohistochemical analysis. RESULTS: We found that MTM-HCC showed less frequent expression of HepPar-1, which is one of the most common hepatocytic markers. In MTM-HCC, the frequency of high expression levels of Keratin19, carbonic anhydrase (CA) IX, and PD-L1 was higher compared to VETC-HCC and conventional HCC. PD-L1 expression was found in 34.4% of MTM-HCC, 0% of VETC-HCC, and 19.0% of conventional HCC. The rate of PD-L1 expression in MTM-HCC was significantly higher than the others (p=0.0015). PD-L1 expression was significantly associated with epithelial cell adhesion molecules and CA IX expression, which are representative markers of tumor stemness and hypoxic conditions, respectively. The CD8 infiltration in VETC-HCC was significantly lower than that in conventional HCC. CONCLUSION: MTM-HCC had different immunophenotypes and TIMEs compared to HCC with the VETC pattern. Although both had immunosuppressive TIME, the elements forming TIME were quite different. To enhance the immune checkpoint inhibitor efficacy, changing TIME from a suppressive to an active form is essential.

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.

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.