Vessels Encapsulating Tumor Clusters (VETC) Is a Powerful Predictor of Aggressive Hepatocellular Carcinoma

Comparative Immunohistochemical Analysis of Clinicopathological Subgroups in Hepatocellular Carcinomas from Japan and Indonesia

Background

Standardized pathological evaluation based on immunohistochemical (IHC) analysis could improve hepatocellular carcinoma (HCC) diagnoses worldwide. We evaluated differences in clinicopathological subgroups in HCCs from two academic institutions in Tokyo-Japan, and Jakarta-Indonesia.

Methods

Clinicopathological parameters and molecular expression patterns were evaluated in 35 HCCs from Indonesia and 41 HCCs from Japan. IHC analysis of biliary/stem cell (B/S) markers (cytokeratin 19, sal-like protein 4, epithelial cell adhesion molecule) and Wnt/β-catenin (W/B) signaling-related molecules (β-catenin, glutamine synthetase) could determine the IHC-based subgroups. For immuno-subtypes categorization, CD3/CD79α double immunohistochemistry was done to evaluate the infiltration of T and B cells. CD34 staining allowed identification of vessels that encapsulated tumor clusters (VETC).

Results

Indonesian HCC patients were mostly <60 years old (66%) with a hepatitis B virus (HBV) background (82%), in contrast to Japanese HCC patients (8% and 19%, respectively, both P < 0.001). In comparison with Japanese, Indonesian cases more frequently had >5 cm tumor size (74% vs 23%, P = 0.001), poor differentiation (40% vs 24%), portal vein invasion (80% vs 61%), and α-fetoprotein levels >500 ng/ml (45% vs 13%, P = 0.005). No significant differences were found in the proportions of B/S, W/B, and -/- subgroups from both countries. No immune-high tumors were observed among Indonesian cases, and immune-low tumors (66%) were more common than in Japanese cases (54%). VETC-positive tumors in Indonesia were significantly more common (29%), and most were in the HBV (90%) and -/- subgroups (90%), whereas Japanese VETC cases (10%, P = 0.030) were nonviral (100%) and W/B subgroups (75%).

Conclusion

IHC-based analysis more precisely reflected the clinicopathological differences of HCCs in Japan and Indonesia. These findings provide new insights into standardization attempts and HCC heterogeneity among countries.

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.

MRI imaging features for predicting macrotrabecular-massive subtype hepatocellular carcinoma: a systematic review and meta-analysis

PURPOSE

To identify significant MRI features associated with macrotrabecular-massive hepatocellular carcinoma (MTM-HCC), and to assess the distribution of Liver Imaging Radiology and Data System (LI-RADS, LR) category assignments.

METHODS

PubMed and EMBASE were searched up to March 28, 2023. Random-effects model was constructed to calculate pooled diagnostic odds ratios (DORs) and 95% confidence intervals (CIs) for each MRI feature for differentiating MTM-HCC from NMTM-HCC. The pooled proportions of LI-RADS category assignments in MTM-HCC and NMTM-HCC were compared using z-test.

RESULTS

Ten studies included 1978 patients with 2031 HCCs (426 (20.9%) MTM-HCC and 1605 (79.1%) NMTM-HCC). Six MRI features showed significant association with MTM-HCC: tumor in vein (TIV) (DOR = 2.4 [95% CI, 1.6-3.5]), rim arterial phase hyperenhancement (DOR =2.6 [95% CI, 1.4-5.0]), corona enhancement (DOR = 2.6 [95% CI, 1.4-4.5]), intratumoral arteries (DOR = 2.6 [95% CI, 1.1-6.3]), peritumoral hypointensity on hepatobiliary phase (DOR = 2.2 [95% CI, 1.5-3.3]), and necrosis (DOR = 4.2 [95% CI, 2.0-8.5]). The pooled proportions of LI-RADS categories in MTM-HCC were LR-3, 0% [95% CI, 0-2%]; LR-4, 11% [95% CI, 6-16%]; LR-5, 63% [95% CI, 55-71%]; LR-M, 12% [95% CI, 6-19%]; and LR-TIV, 13% [95% CI, 6-22%]. In NMTM-HCC, the pooled proportions of LI-RADS categories were LR-3, 1% [95% CI, 0-2%]; LR-4, 8% [95% CI, 3-15%]; LR-5, 77% [95% CI, 71-82%]; LR-M, 5% [95% CI, 3-7%]; and LR-TIV, 6% [95% CI, 2-11%]. MTM-HCC had significantly lower proportion of LR-5 and higher proportion of LR-M and LR-TIV categories.

CONCLUSIONS

Six MRI features showed significant association with MTM-HCC. Additionally, compared to NMTM-HCC, MTM-HCC are more likely to be categorized LR-M and LR-TIV and less likely to be categorized LR-5.

CLINICAL RELEVANCE STATEMENT

Several MR imaging features can suggest macrotrabecular-massive hepatocellular carcinoma subtype, which can assist in guiding treatment plans and identifying potential candidates for clinical trials of new treatment strategies.

KEY POINTS

• Macrotrabecular-massive hepatocellular carcinoma is a subtype of HCC characterized by its aggressive nature and unfavorable prognosis. • Tumor in vein, rim arterial phase hyperenhancement, corona enhancement, intratumoral arteries, peritumoral hypointensity on hepatobiliary phase, and necrosis on MRI are indicative of macrotrabecular-massive hepatocellular carcinoma. • Various MRI characteristics can be utilized for the diagnosis of the macrotrabecular-massive hepatocellular carcinoma subtype. This can prove beneficial in guiding treatment decisions and identifying potential candidates for clinical trials involving novel treatment approaches.

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

Level of Evidence

5

Technical Efficacy Stage

1

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.

Exploring the MRI and Clinical Features of P53-Mutated Hepatocellular Carcinoma

Purpose

To study the MRI features (based on LI-RADS) and clinical characteristics of P53-mutated hepatocellular carcinoma (HCC) patients.

Patients and Methods

This study enrolled 344 patients with histopathologically confirmed HCC (P53-mutated group [n = 196], non-P53-mutated group [n = 148]). We retrospectively evaluated the preoperative MRI features, clinical and pathologic features of the lesions and assigned each lesion according to the LI-RADS. MRI findings, clinical features, and pathologic findings were compared using the Student's t test, χ2 test, and multivariable regression analysis.

Results

Most HCC patients were categorized as LR-5. On multivariate analysis, the Edmondson-Steiner grade (odds ratio, 2.280; 95% CI: 1.268, 4.101; p = 0.006) and rim enhancement (odds ratio, 2.517; 95% CI: 1.095, 5.784; p = 0.030) were found to be independent variables associated with P53-mutated HCC. In the group of HCC lesions with the largest tumor diameter (LTD) greater than or equal to 10mm and less than or equal to 20mm, enhancing capsule was an independent predictor of P53-mutated HCC (odds ratio, 6.200; 95% CI: 1.116, 34.449; p = 0.037). Among the HCC lesions (20 mm ˂ LTD ≤ 50 mm), corona enhancement (odds ratio, 2.102; 95% CI: 1.022, 4.322; p = 0.043) and nodule-in-nodule architecture (odds ratio, 2.157; 95% CI: 1.033, 4.504; p = 0.041) were found to be independent risk factors for P53 mutation. Among the HCC lesions (50 mm ˂ LTD ≤ 100 mm), diameter (odds ratio, 1.035; 95% CI: 1.001, 1.069; p = 0.044) and AFP ≥ 400 (ng/mL) (odds ratio, 3.336; 95% CI: 1.052, 10.577; p = 0.041) were found to be independent variables associated with P53-mutated HCC.

Conclusion

Poor differentiation and rim enhancement are potential predictive biomarkers for P53-mutated HCC, while HCCs of different diameters have different risk factors for predicting P53 mutations.

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.

Pathologic characterization of precursors and cholangiocarcinoma referring to peribiliary capillary plexus: a new pathologic approach to bile duct neoplasm

The peribiliary capillary plexus (PCP) regularly and densely lines the basal side of the lining epithelia of normal bile ducts. To determine the pathology of the PCP in high-grade biliary intraepithelial neoplasms (BilINs) and intraductal papillary neoplasms of the bile duct (IPNBs), a precursor of cholangiocarcinoma (CCA), and CCA. Seventy-six cases of surgically resected high-grade BilIN and 83 cases of IPNB were histopathologically examined using endothelial immunostaining of PCP; all cases of high-grade BilIN and 40 cases of IPNB were associated with invasive CCA. Invasive and preinvasive neoplasms were pathologically examined referring to a two-layer pattern composed of biliary lining epithelia and underlying PCP unique to the bile duct. All high-grade BilIIN cases had an underlying single layer of capillaries, similar to PCP (PCP-like capillaries). In 43% of the 83 cases of IPNB, these capillaries were regularly distributed in almost all stalks and intervening stroma of intraluminal neoplastic components, while in the remaining 57% of IPNB, capillaries were sparsely or irregularly distributed in intraluminal components showing cribriform or solid growth patterns composed of striking atypical neoplastic epithelia. Invasive carcinomas associated with high-grade BilIN and IPNB were not lined with capillaries. The loss of PCP-like capillaries underlying high-grade BilIN and in stalks or stroma of IPNB may be involved in the malignant progression of these precursors. Immunostaining of PCP could be a new pathological tool for the evaluation of malignant progression and vascular supply in CCA and its precursors.

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.

The tumor microenvironment of VETC+ hepatocellular carcinoma is enriched of immunosuppressive TAMs spatially close to endothelial cells

BACKGROUND AND AIM

VETC (vessel that encapsulate tumor cluster) is a peculiar vascular phenotype observed in hepatocellular carcinoma (HCC), associated with distant metastases and poor outcome. VETC has been linked to the Tie2/Ang2 axis and is characterized by lymphocytes poor (cold) tumor microenvironment (TME). In this setting the role of Tumor Associated Macrophages (TAMs) has never been explored. Aim of the study is to investigate the presence and features of TAMs in VETC+ HCC and the possible interplay between TAMs and endothelial cells (ECs).

METHODS

The series under study included 42 HCC. Once separated according to the VETC phenotype (21 VETC+; 21 VETC-) we stained consecutive slides with immunohistochemistry for CD68, CD163 and Tie2. Slides were then scanned and QuPath used to quantify morphological features.

RESULTS

VETC+ cases were significantly (p < 0.001) enriched with large, lipid rich CD163+ TAMs (M2 oriented) that were spatially close to ECs; HCC cells significantly (p: 0.002) overexpressed Tie2 with a polarization toward ECs.

CONCLUSIONS

The pro-metastatic attitude of VETC is sustained by a strict morphological relationship between immunosuppressive M2-TAMs, ECs and Tie2-expressing HCC cells.

Histological predictors of aggressive recurrence of hepatocellular carcinoma after liver resection

BACKGROUND AND AIMS

Assessment of recurrence risk after liver resection (LR) is critical in hepatocellular carcinoma (HCC), particularly with the advent of effective adjuvant therapy. The aim of the study was to analyze the clinical and pathological factors associated with recurrence, aggressive recurrence, and survival after LR.

METHOD

Retrospective study in which all single HCC (BCLC-0/A) patients treated with LR between February 2000 and November 2020 were included. The main clinical variables were recorded. Histological features were blindly evaluated by two independent pathologists. Aggressive recurrence was defined as those that exceeded the Milan criteria at 1st recurrence.

RESULTS

A total of 218 patients were included (30% BCLC 0 and 70% BCLC A), median (IQR) tumor size of 28 (19-42mm). The prevalence of microvascular invasion and/or satellitosis (mVI/S) was 39%, with a kappa-index between both pathologists of 0.8. After a median follow-up of 49 (23-85) months, 61/218 (28%) patients died, 32/218 (15%) underwent LT, 127 (58%) developed HCC recurrence. The prevalence of aggressive recurrence was 35% (44/127 Milan-out, with 20 cases at advanced stage), and the 5-year survival was 81%. The presence of mVI/S was the only independent predictor of recurrence [HR:1.83 (1.28-2.61), p<0.001], aggressive recurrence [HR:3.31(1.74-6.29), p<0.001] and mortality [HR:2.23(1.27- 3.91), p:0.005]. The presence of MTM was significantly associated with a higher prevalence of mVI/S, Edmonson Steiner grade III-IV, AFP values and vessels that encapsulate tumor clusters, but MTM was not significantly associated with recurrence, aggressive recurrence, or OS.

CONCLUSION

The presence of mVI/S was the only independent risk factor for aggressive recurrence and mortality. This has important implications for early-stage patient management, especially in the setting of adjuvant immunotherapy or ab initio LT.

Spatial proteomic landscape of primary and relapsed hepatocellular carcinoma reveals immune escape characteristics in early relapse

BACKGROUND AND AIMS

Surgical resection serves as the principal curative strategy for HCC, yet the incidence of postoperative recurrence remains alarmingly high. However, the spatial molecular structural alterations contributing to postoperative recurrence in HCC are still poorly understood.

APPROACH AND RESULTS

We employed imaging mass cytometry to profile the in situ expression of 33 proteins within 358,729 single cells of 92 clinically annotated surgical specimens from 46 patients who were treated with surgical resections for primary and relapsed tumors. We revealed the recurrence progression of HCC was governed by the dynamic spatial distribution and functional interplay of diverse cell types across adjacent normal, tumor margin, and intratumor regions. Our exhaustive analyses revealed an aggressive, immunosuppression-related spatial ecosystem in relapsed HCC. Additionally, we illustrated the prominent implications of the tumor microenvironment of tumor margins in association with relapse HCC. Moreover, we identified a novel subpopulation of dendritic cells (PDL1 + CD103 + DCs) enriched in the peritumoral area that correlated with early postoperative recurrence, which was further validated in an external cohort. Through the analysis of single-cell RNA sequencing data, we found the interaction of PDL1 + CD103 + DCs with regulatory T cells and exhausted T cells enhanced immunosuppression and immune escape through multiple ligand-receptor pathways.

CONCLUSIONS

We comprehensively depicted the spatial landscape of single-cell dynamics and multicellular architecture within primary and relapsed HCC. Our findings highlight spatial organization as a prominent determinant of HCC recurrence and provide valuable insight into the immune evasion mechanisms driving recurrence.

The Evolving Role of Artificial Intelligence in Gastrointestinal Histopathology: An Update

Significant advances in artificial intelligence (AI) over the past decade potentially may lead to dramatic effects on clinical practice. Digitized histology represents an area ripe for AI implementation. We describe several current needs within the world of gastrointestinal histopathology, and outline, using currently studied models, how AI potentially can address them. We also highlight pitfalls as AI makes inroads into clinical practice.

Evaluation and Prognostication of Gd-EOB-DTPA MRI and CT in Patients With Macrotrabecular-Massive Hepatocellular Carcinoma

BACKGROUND

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is highly aggressive. Comparing the diagnosis ability of CT and gadoxetate disodium (Gd-EOB-DTPA) MRI for MTM-HCC are lacking.

PURPOSE

To compare the performance of Gd-EOB-DTPA MRI and CT for differentiating MTM-HCC from non-MTM-HCC, and determine the prognostic indicator.

STUDY TYPE

Retrospective.

SUBJECTS

Post-surgery HCC patients, divided into the training (N = 272) and external validation (N = 44) cohorts.

FIELD STRENGTH/SEQUENCE

3.0 T, T1-weighted imaging, in-opp phase, and T1-weighted volumetric interpolated breath-hold examination/liver acquisition with volume acceleration; enhanced CT.

ASSESSMENT

Three radiologists evaluated clinical characteristics (sex, age, liver disease, liver function, blood routine, alpha-fetoprotein [AFP] and prothrombin time international normalization ratio [PT-INR]) and imaging features (tumor length, intratumor fat, hemorrhage, arterial phase peritumoral enhancement, intratumor necrosis or ischemia, capsule, and peritumoral hepatobiliary phase [HBP] hypointensity). Compared the performance of CT and MRI for diagnosing MTM-HCC. Follow-up occurred every 3-6 months, and nomogram demonstrated the probability of MTM-HCC.

STATISTICAL TESTS

Fisher test, t-test or Wilcoxon rank-sum test, area under the curve (AUC), 95% confidence interval (CI), multivariable logistic regression, Kaplan-Meier curve, and Cox proportional hazards. Significance level: P < 0.05.

RESULTS

Gd-EOB-DTPA MRI (AUC: 0.793; 95% CI, 0.740-0.839) outperformed CT (AUC: 0.747; 95% CI, 0.691-0.797) in the training cohort. The nomogram, incorporating AFP, PT-INR, and MRI features (non-intratumor fat, incomplete capsule, intratumor necrosis or ischemia, and peritumoral HBP hypointensity) demonstrated powerful performance for diagnosing MTM-HCC with an AUC of 0.826 (95% CI, 0.631-1.000) in the external validation cohort. Median follow-up was 347 days (interquartile range [IQR], 606 days) for the training cohort and 222 days (IQR, 441 days) for external validation cohort. Intratumor necrosis or ischemia was an independent indicator for poor prognosis.

DATA CONCLUSION

Gd-EOB-DTPA MRI might assist in preoperative diagnosis of MTM-HCC, and intratumor necrosis or ischemia was associated with poor prognosis.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Prognostication of Hepatocellular Carcinoma Using Artificial Intelligence

Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.

Macrophage-coated tumor cluster aggravates hepatoma invasion and immunotherapy resistance via generating local immune deprivation

Immune checkpoint inhibitors (ICIs) represent a promising treatment for hepatocellular carcinoma (HCC) due to their capacity for abundant lymphocyte infiltration. However, some patients with HCC respond poorly to ICI therapy due to the presence of various immunosuppressive factors in the tumor microenvironment. Our research reveals that a macrophage-coated tumor cluster (MCTC) signifies a unique spatial structural organization in HCC correlating with diminished recurrence-free survival and overall survival in a total of 572 HCC cases from 3 internal cohorts and 2 independent external validation cohorts. Mechanistically, tumor-derived macrophage-associated lectin Mac-2 binding protein (M2BP) induces MCTC formation and traps immunocompetent cells at the edge of MCTCs to induce intratumoral cytotoxic T cell exclusion and local immune deprivation. Blocking M2BP with a Mac-2 antagonist might provide an effective approach to prevent MCTC formation, enhance T cell infiltration, and thereby improve the efficacy of ICI therapy in HCC.

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.

Diagnostic Model for Proliferative HCC Using LI-RADS: Assessing Therapeutic Outcomes in Hepatectomy and TKI-ICI Combination

BACKGROUND

Proliferative hepatocellular carcinoma (HCC), aggressive with poor prognosis, and lacks reliable MRI diagnosis.

PURPOSE

To develop a diagnostic model for proliferative HCC using liver imaging reporting and data system (LI-RADS) and assess its prognostic value.

STUDY TYPE

Retrospective.

POPULATION

241 HCC patients underwent hepatectomy (90 proliferative HCCs: 151 nonproliferative HCCs), divided into the training (N = 167) and validation (N = 74) sets. 57 HCC patients received combination therapy with tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs).

FIELD STRENGTH/SEQUENCE

3.0 T, T1- and T2-weighted, diffusion-weighted, in- and out-phase, T1 high resolution isotropic volume excitation and dynamic gadoxetic acid-enhanced imaging.

ASSESSMENT

LI-RADS v2018 and other MRI features (intratumoral artery, substantial hypoenhancing component, hepatobiliary phase peritumoral hypointensity, and irregular tumor margin) were assessed. A diagnostic model for proliferative HCC was established, stratifying patients into high- and low-risk groups. Follow-up occurred every 3-6 months, and recurrence-free survival (RFS), progression-free survival (PFS) and overall survival (OS) in different groups were compared.

STATISTICAL TESTS

Fisher's test or chi-square test, t-test or Mann-Whitney test, logistic regression, Harrell's concordance index (C-index), Kaplan-Meier curves, and Cox proportional hazards. Significance level: P < 0.05.

RESULTS

The diagnostic model, incorporating corona enhancement, rim arterial phase hyperenhancement, infiltrative appearance, intratumoral artery, and substantial hypoenhancing component, achieved a C-index of 0.823 (training set) and 0.804 (validation set). Median follow-up was 32.5 months (interquartile range [IQR], 25.1 months) for postsurgery patients, and 16.8 months (IQR: 13.2 months) for combination-treated patients. 99 patients experienced recurrence, and 30 demonstrated tumor nonresponse. Differences were significant in RFS and OS rates between high-risk and low-risk groups post-surgery (40.3% vs. 65.8%, 62.3% vs. 90.1%, at 5 years). In combination-treated patients, PFS rates differed significantly (80.6% vs. 7.7% at 2 years).

DATA CONCLUSION

The MR-based model could pre-treatment identify proliferative HCC and assist in prognosis evaluation.

LEVEL OF EVIDENCE: 4

TECHNICAL EFFICACY

Stage 2.

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.

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.

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.

A Cox Nomogram for Assessing Recurrence Free Survival in Hepatocellular Carcinoma Following Surgical Resection Using Dynamic Contrast-Enhanced MRI Radiomics

BACKGROUND

The prognosis of hepatocellular carcinoma (HCC) is difficult to predict and carries high mortality. This study utilized radiomic techniques with clinical examinations to assess recurrence in HCC.

PURPOSE

To develop a Cox nomogram to assess the risk of postoperative recurrence in HCC using radiomic features of three volumes of interest (VOIs) in preoperative dynamic contrast-enhanced MRI (DCE-MRI), along with clinical findings.

STUDY TYPE

Retrospective.

SUBJECTS

249 patients with pathologically proven HCCs undergoing surgical resection at three institutions were selected.

FIELD STRENGTH/SEQUENCE

Fat saturated T2-weighted, Fat saturated T1-weighted, and DCE-MRI performed at 1.5 T and 3.0 T.

ASSESSMENT

Three VOIs were generated; the tumor VOI corresponds to the area from the tumor core to the outer perimeter of the tumor, the tumor +10 mm VOI represents the area from the tumor perimeter to 10 mm distal to the tumor in all directions, finally, the background liver parenchyma VOI represents the hepatic tissue outside the tumor. Three models were generated. The total radiomic model combined information from the three listed VOI's above. The clinical-radiological model combines physical examination findings with imaging characteristics such as tumor size, margin features, and metastasis. The combined radiomic model includes features from both models listed above and showed the highest reliability for assessing 24-month survival for HCC.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) Cox regression, univariable, and multivariable Cox regression, Kmeans clustering, and Kaplan-Meier analysis. The discrimination performance of each model was quantified by the C-index. A P value <0.05 was considered statistically significant.

RESULTS

The combined radiomic model, which included features from the radiomic VOI's and clinical imaging provided the highest performance (C-index: training cohort = 0.893, test cohort = 0.851, external cohort = 0.797) in assessing the survival of HCC.

CONCLUSION

The combined radiomic model provides superior ability to discern the possibility of recurrence-free survival in HCC over the total radiomic and the clinical-radiological models.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Hepatocellular Carcinoma: Optimal Radiological Evaluation before Liver Transplantation

Liver transplantation (LT) is the recommended curative-intent treatment for patients with early or intermediate-stage hepatocellular carcinoma (HCC) who are ineligible for resection. Imaging plays a central role in staging and for selecting the best LT candidates. This review will discuss recent developments in pre-LT imaging assessment, in particular LT eligibility criteria on imaging, the technical requirements and the diagnostic performance of imaging for the pre-LT diagnosis of HCC including the recent Liver Imaging Reporting and Data System (LI-RADS) criteria, the evaluation of the response to locoregional therapy, as well as the non-invasive prediction of HCC aggressiveness and its impact on the outcome of LT. We will also briefly discuss the role of nuclear medicine in the pre-LT evaluation and the emerging role of artificial intelligence models in patients with HCC.

Development of a Model including MRI Features for Predicting Advanced-stage Recurrence of Hepatocellular Carcinoma after Liver Resection

Background Identifying patients at high risk for advanced-stage hepatocellular carcinoma (HCC) recurrence after liver resection may improve patient survival. Purpose To develop a model including MRI features for predicting postoperative advanced-stage HCC recurrence. Materials and Methods This single-center, retrospective study includes consecutive adult patients who underwent preoperative contrast-enhanced MRI and curative-intent resection for early- to intermediate-stage HCC (from December 2011 to April 2021). Three radiologists evaluated 52 qualitative features on MRI scans. In the training set, Fine-Gray proportional subdistribution hazard analysis was performed to identify clinical, laboratory, imaging, pathologic, and surgical variables to include in the predictive model. In the test set, the concordance index (C-index) was computed to compare the developed model with current staging systems. The Kaplan-Meier survival curves were compared using the log-rank test. Results The study included 532 patients (median age, 54 years; IQR, 46-62 years; 465 male patients), 302 patients from the training set (median age, 54 years; IQR, 46-63 years; 265 male patients), and 128 patients from the test set (median age, 53 years; IQR, 46-63 years; 108 male patients). Advanced-stage recurrence was observed in 38 of 302 (12.6%) and 15 of 128 (11.7%) of patients from the training and test sets, respectively. Serum neutrophil count (109/L), tumor size (in centimeters), and arterial phase hyperenhancement proportion on MRI scans were associated with advanced-stage recurrence (subdistribution hazard ratio range, 1.16-3.83; 95% CI: 1.02, 7.52; P value range, <.001 to .02) and included in the predictive model. The model showed better test set prediction for advanced-stage recurrence than four staging systems (2-year C-indexes, 0.82 [95% CI: 0.74, 0.91] vs 0.63-0.68 [95% CI: 0.52, 0.82]; P value range, .001-.03). Patients at high risk for HCC recurrence (model score, ≥15 points) showed increased advanced-stage recurrence and worse all-stage recurrence-free survival (RFS), advanced-stage RFS, and overall survival than patients at low risk for HCC recurrence (P value range, <.001 to .02). Conclusion A model combining serum neutrophil count, tumor size, and arterial phase hyperenhancement proportion predicted advanced-stage HCC recurrence better than current staging systems and may identify patients at high risk. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tsai and Mellnick in this issue.

ERS: A simple scoring system to predict early recurrence after surgical resection for hepatocellular carcinoma

BACKGROUND

Surgical resection (SR) is a potentially curative treatment of hepatocellular carcinoma (HCC) hampered by high rates of recurrence. New drugs are tested in the adjuvant setting, but standardised risk stratification tools of HCC recurrence are lacking.

OBJECTIVES

To develop and validate a simple scoring system to predict 2-year recurrence after SR for HCC.

METHODS

2359 treatment-naïve patients who underwent SR for HCC in 17 centres in Europe and Asia between 2004 and 2017 were divided into a development (DS; n = 1558) and validation set (VS; n = 801) by random sampling of participating centres. The Early Recurrence Score (ERS) was generated using variables associated with 2-year recurrence in the DS and validated in the VS.

RESULTS

Variables associated with 2-year recurrence in the DS were (with associated points) alpha-fetoprotein (<10 ng/mL:0; 10-100: 2; >100: 3), size of largest nodule (≥40 mm: 1), multifocality (yes: 2), satellite nodules (yes: 2), vascular invasion (yes: 1) and surgical margin (positive R1: 2). The sum of points provided a score ranging from 0 to 11, allowing stratification into four levels of 2-year recurrence risk (Wolbers' C-indices 66.8% DS and 68.4% VS), with excellent calibration according to risk categories. Wolber's and Harrell's C-indices apparent values were systematically higher for ERS when compared to Early Recurrence After Surgery for Liver tumour post-operative model to predict time to early recurrence or recurrence-free survival.

CONCLUSIONS

ERS is a user-friendly staging system identifying four levels of early recurrence risk after SR and a robust tool to design personalised surveillance strategies and adjuvant therapy trials.

Nucleus-localized circSLC39A5 suppresses hepatocellular carcinoma development by binding to STAT1 to regulate TDG transcription

Accumulating evidence indicates that circular RNAs (circRNAs) are inextricably linked to cancer development. However, the function and mechanism of nucleus-localized circRNAs in hepatocellular carcinoma (HCC) still require investigation. Here, qRT-PCR and receiver-operating characteristic curve were used to detect the expression and diagnostic potential of circSLC39A5 for HCC. The biological function of circSLC39A5 in HCC was investigated in vitro and in vivo. Nucleoplasmic separation assay, fluorescence in situ hybridization, RNA pulldown, RNA immunoprecipitation, the HDOCK Server, the NucleicNet Webserver, crosslinking-immunoprecipitation, MG132 treatment, and chromatin immunoprecipitation were utilized to explore the potential molecular mechanism of circSLC39A5 in HCC. The results showed that circSLC39A5 was downregulated in both HCC tissues and plasma and was associated with satellite nodules and lymph node metastasis/vascular invasion. CircSLC39A5 was stably expressed in plasma samples under different storage conditions, showing good diagnostic potential for HCC (AUC = 0.915). CircSLC39A5 inhibited proliferation, migration, and invasion, facilitated the apoptosis of HCC cells, and was associated with low expression of Ki67 and CD34. Remarkably, circSLC39A5 is mainly localized in the nucleus and binds to the transcription factor signal transducer and activator of transcription 1 (STAT1), affecting its stabilization and expression. STAT1 binds to the promoter of thymine DNA glycosylase (TDG). Overexpression of circSLC39A5 elevates TDG expression and reverses the increase of proliferating cell nuclear antigen (PCNA) expression and the overactive cell proliferation caused by TDG silencing. Our findings uncovered a novel plasma circRNA, circSLC39A5, which may be a potential circulating diagnostic marker for HCC, and the mechanism by which nucleus-localized circSLC39A5 exerts a transcriptional regulatory role in HCC by affecting STAT1/TDG/PCNA provides new insights into the mechanism of circRNAs.

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.

Advances in Histological and Molecular Classification of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a primary liver cancer characterized by hepatocellular differentiation. HCC is molecularly heterogeneous with a wide spectrum of histopathology. The prognosis of patients with HCC is generally poor, especially in those with advanced stages. HCC remains a diagnostic challenge for pathologists because of its morphological and phenotypic diversity. However, recent advances have enhanced our understanding of the molecular genetics and histological subtypes of HCC. Accurate diagnosis of HCC is important for patient management and prognosis. This review provides an update on HCC pathology, focusing on molecular genetics, histological subtypes, and diagnostic approaches.

Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma

Background & Aims

Distinct vascular patterns, including microvascular invasion (MVI) and vessels encapsulating tumour clusters (VETC), are associated with poor outcomes of hepatocellular carcinoma (HCC). Imaging surrogates of these vascular patterns potentially help to predict post-resection recurrence. Herein, a prognostic model integrating imaging-based surrogates of these distinct vascular patterns was developed to predict postoperative recurrence-free survival (RFS) in patients with HCC.

Methods

Clinico-radiological data of 1,285 patients with HCC from China undergoing surgical resection were retrospectively enrolled from seven medical centres between 2014 and 2020. A prognostic model using clinical data and imaging-based surrogates of MVI and VETC patterns was developed (n = 297) and externally validated (n = 373) to predict RFS. The surrogates (i.e. MVI and VETC scores) were individually built from preoperative computed tomography using two independent cohorts (n = 360 and 255). Whether the model's stratification was associated with postoperative recurrence following anatomic resection was also evaluated.

Results

The MVI and VETC scores demonstrated effective performance in their respective training and validation cohorts (AUC: 0.851-0.883 for MVI and 0.834-0.844 for VETC). The prognostic model incorporating serum alpha-foetoprotein, tumour multiplicity, MVI score, and VETC score achieved a C-index of 0.748-0.764 for the developing and external validation cohorts and generated three prognostically distinct strata. For patients at model-predicted medium risk, anatomic resection was associated with improved RFS (p <0.05). By contrast, anatomic resection had no impact on RFS in patients at model-predicted low or high risk (both p >0.05).

Conclusions

The proposed model integrating imaging-based surrogates of distinct vascular patterns enabled accurate prediction for RFS. It can potentially be used to identify HCC surgical candidates who may benefit from anatomic resection.

Impact and implications

MVI and VETC are distinct vascular patterns of HCC associated with aggressive biological behaviour and poor outcomes. Our multicentre study provided a model incorporating imaging-based surrogates of these patterns for preoperatively predicting RFS. The proposed model, which uses imaging detection to estimate the risk of MVI and VETC, offers an opportunity to help shed light on the association between tumour aggressiveness and prognosis and to support the selection of the appropriate type of surgical resection.

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.

Imaging prognostication and tumor biology in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, and represents a significant global health burden with rising incidence rates, despite a more thorough understanding of the etiology and biology of HCC, as well as advancements in diagnosis and treatment modalities. According to emerging evidence, imaging features related to tumor aggressiveness can offer relevant prognostic information, hence validation of imaging prognostic features may allow for better noninvasive outcomes prediction and inform the selection of tailored therapies, ultimately improving survival outcomes for patients with HCC.

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.

Association between the expression levels of ADAMTS16 and BMP2 and tumor budding in hepatocellular carcinoma

Tumor budding (TB) has become a crucial factor for predicting the malignancy grade and prognostic outcome for multiple types of solid cancer. Studies have investigated the prognostic value of TB in hepatocellular carcinoma (HCC). However, its molecular mechanism in HCC remains unclear. To the best of our knowledge, the present study was the first to compare the expression of differentially expressed genes (DEGs) between TB-positive (TB-pos) and TB-negative HCC tissues. In the present study, total RNA was extracted from 40 HCC tissue specimens and then sequenced. According to Gene Ontology (GO) functional annotation, upregulated DEGs were markedly associated with embryonic kidney development-related GO terms, which suggested that the TB process may at least partly mimic the process of embryonic kidney development. Subsequently, two genes, a disintegrin and metalloproteinase domain with thrombospondin motifs 16 (ADAMTS16) and bone morphogenetic protein 2 (BMP2), were screened and verified through immunohistochemical analysis of HCC tissue microarrays. According to the immunohistochemical results, ADAMTS16 and BMP2 were upregulated in TB-pos HCC samples, and BMP2 expression was increased in budding cells compared with the tumor center. Additionally, through cell culture experiments, it was demonstrated that ADAMTS16 and BMP2 may promote TB of liver cancer, thus promoting the malignant progression of liver cancer. Further analysis revealed that ADAMTS16 expression was associated with necrosis and cholestasis, and BMP2 expression was associated with the Barcelona Clinic Liver Cancer stage and the vessels encapsulating tumor clusters. Overall, the findings of the present study provided insights into the possible mechanisms of TB in HCC and revealed potential anti-HCC therapeutic targets.

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.

Understanding the causes of recurrent HCC after liver resection and radiofrequency ablation

INTRODUCTION

Surgical resection and radiofrequency ablation are preferred options for early-stage disease, with 5-year recurrence rates as high as 70% when patients are treated according to guidelines. With increasing availability of therapeutic options, including but not limited to, immune-checkpoint inhibitors (ICI), tyrosine kinase inhibitors, antiangiogenics, and adoptive cell therapies, understanding the causes of recurrence and identifying its predictors should be priorities in the hepatocellular carcinoma (HCC) research agenda.

AREAS COVERED

Current knowledge of HCC predictors of recurrence is reviewed, and recent insights about its underlying mechanisms are presented. In addition, results from recent clinical trials investigating treatment combinations are critically appraised.

EXPERT OPINION

HCC recurrence is either due to progressive growth of microscopic residual disease, or to de novo cancer development in the context of a diseased liver, each occurring in an early (<2years) vs. late (≥2 years) fashion. Collectively, morphological, proteomic, and transcriptomic data suggest vascular invasion and angiogenesis as key drivers of HCC recurrence. Agents aimed at blocking either of these two hallmarks should be prioritized at the moment of early-stage HCC clinical trial design. Emerging results from clinical trials testing ICI in early-stage HCC underscore the importance of defining the best treatment sequence and the most appropriate combination strategies. Lastly, as different responses to systemic therapies are increasingly defined according to the HCC etiology, patient enrolment into clinical trials should take into account the biological characteristics of their inherent disease.