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Background: Microvascular invasion (MVI) is an independent risk factor of poor prognosis in hepatocellular carcinoma (HCC) and can be used to guide the diagnosis and treatment of HCC. The immune system serves as an integral role in the incidence and progression of HCC. However, the molecular biology correlation between MVI and tumor immunity and the value of combining the two parameters to predict patient prognosis and HCC response to treatment remain to be evaluated., Results: In this study, we used univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis to establish the MVI and immune-related gene index (MIRGPI) including eight genes. We demonstrated that the MIRGPI was an independent risk factor in predicting the prognosis of HCC. Subsequently, our research established a nomogram model combining pathologic characteristics and verified its good clinical application value. In addition, our study found that the TP53 gene had a higher mutation frequency and a lower degree of immune infiltration in the high-risk group. The low-risk group had higher sensitivity to immunotherapy, sorafenib, and TACE treatment, and the high-risk group had higher sensitivity to common chemotherapeutic agents. Finally, SEMA3C was found to facilitate the proliferation, migration and invasive ability of HCC by in vitro and in vivo experiments, and its mechanism may be associated with the activation of the NF-Κb/EMT signaling pathway., Conclusions: In summary, the MIRGPI signature we developed is a reliable marker for the prediction of prognosis and treatment response, and is important for the prognostic assessment and individualized treatment of HCC., (© 2024. The Author(s).)

Purpose: To conduct a head-to-head comparison between deep learning (DL) and radiomics models across institutions for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and to investigate the model robustness and generalizability through rigorous internal and external validation., Methods: This retrospective study included 2304 preoperative images of 576 HCC lesions from two centers, with MVI status determined by postoperative histopathology. We developed DL and radiomics models for predicting the presence of MVI using B-mode ultrasound, contrast-enhanced ultrasound (CEUS) at the arterial, portal, and delayed phases, and a combined modality (B + CEUS). For radiomics, we constructed models with enlarged vs. original regions of interest (ROIs). A cross-validation approach was performed by training models on one center's dataset and validating the other, and vice versa. This allowed assessment of the validity of different ultrasound modalities and the cross-center robustness of the models. The optimal model combined with alpha-fetoprotein (AFP) was also validated. The head-to-head comparison was based on the area under the receiver operating characteristic curve (AUC)., Results: Thirteen DL models and 25 radiomics models using different ultrasound modalities were constructed and compared. B + CEUS was the optimal modality for both DL and radiomics models. The DL model achieved AUCs of 0.802-0.818 internally and 0.667-0.688 externally across the two centers, whereas radiomics achieved AUCs of 0.749-0.869 internally and 0.646-0.697 externally. The radiomics models showed overall improvement with enlarged ROIs (P < 0.05 for both CEUS and B + CEUS modalities). The DL models showed good cross-institutional robustness (P > 0.05 for all modalities, 1.6-2.1% differences in AUC for the optimal modality), whereas the radiomics models had relatively limited robustness across the two centers (12% drop-off in AUC for the optimal modality). Adding AFP improved the DL models (P < 0.05 externally) and well maintained the robustness, but did not benefit the radiomics model (P > 0.05)., Conclusion: Cross-institutional validation indicated that DL demonstrated better robustness than radiomics for preoperative MVI prediction in patients with HCC, representing a promising solution to non-standardized ultrasound examination procedures., (© 2024. The Author(s).)

The apelin receptor (APJ) is a key player in tumour angiogenesis, but its role in hepatocellular carcinoma (HCC) remains unclear. This study aims to elucidate the function of the apelin/APJ pathway in HCC using a multi-omics approach and identify potential therapeutic biomarkers. Differentially expressed genes related to the apelin/APJ axis were identified from bulk transcriptomics to reveal HCC-associated disparities. Single-cell and spatial transcriptomics were used to localize and analyse the function of these genes. Machine learning models were constructed to predict outcomes based on apelin/APJ expression, and experimental validation was conducted to explore the pathway's impact on HCC angiogenesis. Single cell analysis revealed an overexpression of APJ/Aplin in the endothelium. The stemness of endothelial cell (EC) with high apelin/APJ was enhanced, as well as the expression of TGFb, oxidative stresses and PI3K/AKT pathway genes. Spatial transcriptomics confirmed that EC populations with high APJ scores were enriched within the tumour. Machine learning models showed high prognostic accuracy. High APJ expression was linked to worse outcomes (p = 0.001), and AUC values were high (1 year, 3 year, 5 year) (0.95, 0.97, 0.98). Immune suppression and non-responsiveness of immune therapy were also seen in high-risk groups. The experimental validation showed that silencing apelin reduced angiogenesis (p  < 0.05), endothelial proliferation, decreased expression of ANG2, KLF2, VEGFA and lower ERK1/2 phosphorylation. Apelin may serve as a potential therapeutic target in HCC, given its role in promoting tumour angiogenesis and poor patient outcomes., (© 2024 The Author(s). Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Background and Aim: There is a pressing need for non-invasive preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). This study investigates the potential of exosome-derived mRNA in plasma as a biomarker for diagnosing MVI., Methods: Patients with suspected HCC undergoing hepatectomy were prospectively recruited for preoperative peripheral blood collection. Exosomal RNA profiling was conducted using RNA sequencing in the discovery cohort, followed by differential expression analysis to identify candidate targets. We employed multiplexed droplet digital PCR technology to efficiently validate them in a larger sample size cohort., Results: A total of 131 HCC patients were ultimately enrolled, with 37 in the discovery cohort and 94 in the validation cohort. In the validation cohort, the expression levels of RSAD2, PRPSAP1, and HOXA2 were slightly elevated while CHMP4A showed a slight decrease in patients with MVI compared with those without MVI. These trends were consistent with the findings in the discovery cohort, although they did not reach statistical significance (P > 0.05). Notably, the expression level of exosomal PRPSAP1 in plasma was significantly higher in patients with more than 5 MVI than in those without MVI (0.147 vs 0.070, P = 0.035)., Conclusion: This study unveils the potential of exosome-derived PRPSAP1 in plasma as a promising indicator for predicting MVI status preoperatively., (© 2024 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

Background: Tumors are heterogenous and consist of subregions, also known as tumoral habitats, each exhibiting varied biological characteristics. Each habitat corresponds to a cluster of tissue sharing similar structural, metabolic, or functional characteristics. The habitat imaging technique facilitates both the visualization and quantification of these tumoral habitats., Purpose: To evaluate the microvascular invasion (MVI) in hepatocellular carcinoma (HCC) (≤5 cm) and assess the recurrence-free survival (RFS) using gadoxetate disodium-enhanced MRI-based habitat imaging., Study Type: Retrospective., Subjects: 180 patients (52.9 years ± 11.7, 156 men) with HCC., Field Strength/sequence: 1.5T/contrast-enhanced T1-weighted gradient-echo sequence., Assessment: The enhancement ratio of signal intensity at the arterial phase (AER) and hepatobiliary phase (HBPER) were calculated. The HCC lesions and their peritumoral tissues of 3, 5, and 7 mm were encoded into four habitats. The volume fraction of each habitat was then quantified. The diagnostic performance was assessed using the receiver operating characteristic analysis with 5-fold cross-validation. The RFS was evaluated with Kaplan-Meier curves., Results: Habitat 2 (with median to high AER and low HBPER) within the peritumoral tissue of 3 mm (f 2 -P 3 ) and tumor diameter could serve as independent risk factors for MVI and showed the statistical significance (odds ratio (OR) of f 2 -P 3  = 1.170, 95% CI = 1.099-1.246; OR of tumor diameter: 6.112, 95% CI = 2.162-17.280). A nomogram was developed by incorporating f 2 -P 3 and tumor diameter, demonstrating high diagnostic accuracy. The area under the curve from 5-fold cross-validation ranged from 0.880 to 1.000. Additionally, the nomogram model demonstrated high efficacy in risk stratification for RFS., Conclusion: Habitat imaging of HCC and its peritumoral microenvironment has the potential for noninvasive and preoperative identification of MVI and prognostic assessment., Level of Evidence: 3 TECHNICAL EFFICACY: Stage 2., (© 2023 International Society for Magnetic Resonance in Medicine.)

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., (© 2023 International Society for Magnetic Resonance in Medicine.)

Background: Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies., Purpose: To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE)., Study Type: Prospective., Population: 188 patients with HCC, divided into a training cohort (n = 150) and a validation cohort (n = 38). In the training cohort, 106/150 patients completed a 2-year follow-up., Field Strength/sequence: 1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences., Assessment: Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors., Statistical Tests: Student's t-test, Mann-Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log rank tests. P < 0.05 was considered significant., Results: Tumor c and liver c were independent predictors of MVI and RFS, respectively. Adding tumor c significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance., Data Conclusion: Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established., Level of Evidence: 2 TECHNICAL EFFICACY: Stage 2., (© 2024 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Background and Objectives : Microvascular invasion (MVI) significantly impacts recurrence and survival rates after liver resection in hepatocellular carcinoma (HCC). Pre-operative prediction of MVI is crucial in determining the treatment strategy. This study aims to develop a nomogram model to predict the probability of MVI based on clinical features in HCC patients. Materials and Methods : A total of 489 patients with a pathological diagnosis of HCC were enrolled from our hospital. Those registered from 2012-2015 formed the derivation cohort, and those from 2016-2019 formed the validation cohort for pre-operative prediction of MVI. A nomogram model for prediction was created using a regression model, with risk factors derived from clinical and tumor-related features before surgery. Results : Using the nomogram model to predict the odds ratio of MVI before hepatectomy, the AFP, platelet count, GOT/GPT ratio, albumin-alkaline phosphatase ratio, ALBI score, and GNRI were identified as significant variables for predicting MVI. The Youden index scores for each risk variable were 0.287, 0.276, 0.196, 0.185, 0.115, and 0.112, respectively, for the AFP, platelet count, GOT/GPT ratio, AAR, ALBI, and GNRI. The maximum value of the total nomogram scores was 220. An increase in the number of nomogram points indicated a higher probability of MVI occurrence. The accuracy rates ranged from 55.9% to 64.4%, and precision rates ranged from 54.3% to 68.2%. Overall survival rates were 97.6%, 83.4%, and 73.9% for MVI(-) and 80.0%, 71.8%, and 41.2% for MVI(+) ( p < 0.001). The prognostic effects of MVI(+) on tumor-free survival and overall survival were poor in both the derivation and validation cohorts. Conclusions : Our nomogram model, which integrates clinical factors, showed reliable calibration for predicting MVI and provides a useful tool enabling surgeons to estimate the probability of MVI before resection. Consequently, surgical strategies and post-operative care programs can be adapted to improve the prognosis of HCC patients where possible.

Background: Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable prognosis, early recurrence, and metastatic progression. However, the precise mechanistic underpinnings governing its onset and advancement remain elusive., Methods: In this research, we downloaded bulk RNA-seq data from the TCGA and HCCDB repositories, single-cell RNA-seq data from the GEO database, and spatial transcriptomics data from the CNCB database. Leveraging the Scissor algorithm, we delineated prognosis-related cell subpopulations and discerned a distinct MVI-related malignant cell subtype. A comprehensive exploration of these malignant cell subpopulations was undertaken through pseudotime analysis and cell-cell communication scrutiny. Furthermore, we engineered a prognostic model grounded in MVI-related genes, employing 101 algorithm combinations integrated by 10 machine-learning algorithms on the TCGA training set. Rigorous evaluation ensued on internal testing sets and external validation sets, employing C-index, calibration curves, and decision curve analysis (DCA)., Results: Pseudotime analysis indicated that malignant cells, showing a positive correlation with MVI, were primarily concentrated in the early to middle stages of differentiation, correlating with an unfavorable prognosis. Importantly, these cells showed significant enrichment in the MYC pathway and were involved in extensive interactions with diverse cell types via the MIF signaling pathway. The association of malignant cells with the MVI phenotype was corroborated through validation in spatial transcriptomics data. The prognostic model we devised demonstrated exceptional sensitivity and specificity, surpassing the performance of most previously published models. Calibration curves and DCA underscored the clinical utility of this model., Conclusions: Through integrated multi-transcriptomics analysis, we delineated MVI-related malignant cells and elucidated their biological functions. This study provided novel insights for managing HCC, with the constructed prognostic model offering valuable support for clinical decision-making., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Huang, Wu, Yu, Xu, Chen, Huo and Li.)

Background: The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance., Purpose: To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients., Materials and Methods: A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation., Results: AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239-5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178-4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007-4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948)., Conclusion: SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits., (© 2024. Italian Society of Medical Radiology.)

Background: Microvascular invasion (MVI) is a risk factor for postoperative recurrence of hepatocellular carcinoma (HCC), even in early-stage HCC. In small HCC ≤ 3 cm, treatment options include anatomical resection or non-anatomical resection, and MVI has a major effect on treatment decisions. We aimed to identify the predictors of MVI in small HCC ≤ 3 cm., Methods: We retrospectively studied 129 patients with very early or early-stage HCC ≤ 3 cm who had undergone 18 F-fluorodeoxyglucose positron emission tomography/computed tomography and subsequent hepatic resection from January 2016 to August 2023. These patients were divided into the derivation cohort (n = 86) and validation cohort (n = 43). We examined the risk factors for MVI using logistic regression analysis, and established a predictive scoring system in the derivation cohort. We evaluated the accuracy of our scoring system in the validation cohort., Results: In the derivation cohort, a Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3), prothrombin induced by vitamin K deficiency or antagonist-II (PIVKA-II), and metabolic tumor volume (MTV) were independent predictors of MVI. We established the scoring system using these three factors. In the validation test, there were no MVI-positive cases with a score of 0 and 1, and all cases were MVI-positive with a score of 4. Moreover, with a score ≥ 2, the sensitivity, specificity, and accuracy of our scoring system were 100%, 71.4%, and 81.4%, respectively., Conclusions: Our scoring system can accurately predict MVI in small HCC ≤ 3 cm, and could contribute to establishing an appropriate treatment strategy., (© 2024. The Author(s) under exclusive licence to Japan Society of Clinical Oncology.)

Objectives: Microvascular invasion (MVI) is a recognized biomarker associated with poorer prognosis in patients with hepatocellular carcinoma. Dual-energy computed tomography (DECT) is a highly sensitive technique that can determine the iodine concentration (IC) in tumour and provide an indirect evaluation of internal microcirculatory perfusion. This study aimed to assess whether the combination of DECT with laboratory data can improve preoperative MVI prediction., Methods: This retrospective study enrolled 119 patients who underwent DECT liver angiography at 2 medical centres preoperatively. To compare DECT parameters and laboratory findings between MVI-negative and MVI-positive groups, Mann-Whitney U test was used. Additionally, principal component analysis (PCA) was conducted to determine fundamental components. Mann-Whitney U test was applied to determine whether the principal component (PC) scores varied across MVI groups. Finally, a general linear classifier was used to assess the classification ability of each PC score., Results: Significant differences were noted (P < .05) in alpha-fetoprotein (AFP) level, normalized arterial phase IC, and normalized portal phase IC between the MVI groups in the primary and validation datasets. The PC1-PC4 accounted for 67.9% of the variance in the primary dataset, with loadings of 24.1%, 16%, 15.4%, and 12.4%, respectively. In both primary and validation datasets, PC3 and PC4 were significantly different across MVI groups, with area under the curve values of 0.8410 and 0.8373, respectively., Conclusions: The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively., Advances in Knowledge: Utilizing PCA, the amalgamation of DECT IC and laboratory features, considering diverse factor loadings, showed substantial promise in accurately classifying MVI. There have been limited endeavours to establish such a combination, offering a novel paradigm for comprehending data in related research endeavours., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Competing Interests: Declaration of Competing Interest The authors declare no competing interest concerning any content of this article.

Background: In this study, we aimed to establish nomograms to predict the microvascular invasion (MVI) and early recurrence in patients with small hepatocellular carcinoma (SHCC), thereby guiding individualized treatment strategies for prognosis improvement., Methods: This study retrospectively analyzed 326 SHCC patients who underwent radical resection at Wuhan Union Hospital between April 2017 and January 2022. They were randomly divided into a training set and a validation set at a 7:3 ratio. The preoperative nomogram for MVI was constructed based on univariate and multivariate logistic regression analysis, and the prognostic nomogram for early recurrence was constructed based on univariate and multivariate Cox regression analysis. We used the receiver operating characteristic (ROC) curves, area under the curves (AUCs), and calibration curves to estimate the predictive accuracy and discriminability of nomograms. Decision curve analysis (DCA) and Kaplan-Meier survival curves were employed to further confirm the clinical effectiveness of nomograms., Results: The AUCs of the preoperative nomogram for MVI on the training set and validation set were 0.749 (95%CI: 0.684-0.813) and 0.856 (95%CI: 0.805-0.906), respectively. For the prognostic nomogram, the AUCs of 1-year and 2-year RFS respectively reached 0.839 (95%CI: 0.775-0.903) and 0.856 (95%CI: 0.806-0.905) in the training set, and 0.808 (95%CI: 0.719-0.896) and 0.874 (95%CI: 0.804-0.943) in the validation set. Subsequent calibration curves, DCA analysis and Kaplan-Meier survival curves demonstrated the high accuracy and efficacy of the nomograms for clinical application., Conclusions: The nomograms we constructed could effectively predict MVI and early recurrence in SHCC patients, providing a basis for clinical decision-making., (© 2024. The Author(s).)

Background: Hepatocellular carcinoma (HCC) is one of the deadliest malignant tumors in China. Microvascular invasion (MVI) often indicates poor prognosis and metastasis in HCC patients. 18 F-FDG PET-CT is a new imaging method commonly used to screen for tumor occurrence and evaluate tumor stage., Purpose: This study attempted to predict the occurrence of MVI in early-stage HCC through 18 F-FDG positron emission tomography (PET)/computed tomography (CT) imaging findings and laboratory data., Patients and Methods: A total of 113 patients who met the inclusion criteria were divided into two groups based on postoperative pathology: the MVI-positive group and MVI-negative group. We retrospectively analyzed the imaging findings and laboratory data of 113 patients. Imaging findings included tumor size, tumor max imum standard uptake value (SUV max T), and normal liver max imum standard uptake value (SUV max L). The ratios of SUV max T to SUV max L (SUV max T/L) and an SUV max T/L > 2 were defined as active tumor metabolism. The tumor size was indicated by the max imum diameter of the tumor, and a diameter greater than 5 cm was defined as a mass lesion. The laboratory data included the alpha-fetoprotein (AFP) level and the HBeAg level. An AFP concentration > 20 ng/mL was defined as a high AFP level. A HBeAg concentration > 0.03 NCU/mL was defined as HB-positive., Results: The SUV max T/L (p = 0.003), AFP level (p = 0.008) and tumor size (p = 0.015) were significantly different between the two groups. Patients with active tumor metabolism, mass lesions and high AFP levels tended to be MVI positive. Binary logistic regression analysis verified that active tumor metabolism (OR = 4.124, 95% CI, 1.566-10.861; p = 0.004) and high AFP levels (OR = 2.702, 95% CI, 1.214-6.021; p = 0.015) were independent risk factors for MVI. The sensitivity of the combination of these two independent risk factors predicting HCC with MVI was 56.9% (29/51), the specificity was 83.9% (52/62) and the accuracy was 71.7% (81/113)., Conclusion: Active tumor metabolism and high AFP levels can predict the occurrence of MVI in HCC patients., (© 2024. The Author(s).)

Background: Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients., Purpose: Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC)., Method: The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC)., Result: Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance., Conclusion: This multi-parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis., (© 2024 American Association of Physicists in Medicine.)

Background: This study aimed to determine the effectiveness of postoperative adjuvant lenvatinib + PD-1 blockade for patients with early-stage hepatocellular carcinoma (HCC) with microvascular invasion (MVI)., Methods: A total of 393 patients with HCC (Barcelona Clinic Liver Cancer stage 0 or A) who underwent curative hepatectomy with histopathologically proven MVI were enrolled according to the inclusion and exclusion criteria and assigned to 2 groups: surgery alone (surgery-alone group) and surgery with lenvatinib and PD-1 blockade (surgery + lenvatinib + PD-1 group) to compare recurrence-free survival (RFS), overall survival (OS), recurrence type, and annual recurrence rate after the application of propensity score matching (PSM). The Cox proportional hazards model was used for univariate and multivariate analyses., Results: Overall, 99 matched pairs were selected using PSM. Patients in the surgery + lenvatinib + PD-1 group had significantly higher 3-year RFS rates (76.8%, 65.7%, and 53.5%) than patients in the surgery-alone group (60.6%, 45.5%, and 37.4%) (P = .012). The 2 groups showed no significant difference in recurrence types and OS. Surgery alone, MVI-M2, and alpha-fetoprotein of ≥200 ng/mL were independent risk factors for RFS (P < .05), and history of alcohol use disorder was an independent risk factor for OS (P = .022)., Conclusion: Postoperative lenvatinib + PD-1 blockade improved the RFS in patients with HCC with MVI and was particularly beneficial for specific individuals., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To compare the ability to depict MRI features of hepatobiliary agents in microvascular infiltration (MVI) of hepatocellular carcinoma (HCC) during different stages of dynamic enhancement MRI., Materials and Methods: A retrospective study included 111 HCC lesions scanned with either Gd-EOB-DTPA or Gd-BOPTA. All cases underwent multiphase dynamic contrast-enhanced scanning before surgery, including arterial phase (AP), portal venous phase (PVP), transitional phase (TP), delayed phase (DP), and hepatobiliary phase (HBP). Two abdominal radiologists independently evaluated MRI features of MVI in HCC, such as peritumoral hyperenhancement, incomplete capsule, non-smooth tumor margins, and peritumoral hypointensity. Finally, the results were reviewed by the third senior abdominal radiologist. Chi-square (χ 2 ) Inspection for comparison between groups. P < 0.05 is considered statistically significant. Receiver operating characteristic (ROC) curve was used to evaluate correlation with pathology, and the area under the curve (AUC) and 95% confidence interval (95% CI) were calculated., Results: Among the four MVI evaluation signs, Gd-BOPTA showed significant differences in displaying two signs in the HBP (P < 0.05:0.000, 0.000), while Gd-EOB-DTPA exhibited significant differences in displaying all four signs (P < 0.05:0.005, 0.006, 0.000, 0.002). The results of the evaluations of the two contrast agents in the DP phase with incomplete capsulation showed the highest correlation with pathology (AUC: 0.843, 0.761). By combining the four MRI features, Gd-BOPTA and Gd-EOB-DTPA have correlated significantly with pathology, and Gd-BOPTA is better (AUC: 0.9312vs0.8712)., Conclusion: The four features of hepatobiliary agent dynamic enhancement MRI demonstrate a good correlation with histopathological findings in the evaluation of MVI in HCC, and have certain clinical significance., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Rationale and Objectives: To explore and compare the performance of LI-RADS® and radiomics from multiparametric MRI in predicting microvascular invasion (MVI) preoperatively in patients with solitary hepatocellular carcinoma (HCC)< 5 cm., Methods: We enrolled 143 patients with pathologically proven HCC and randomly stratified them into training (n = 100) and internal validation (n = 43) cohorts. Besides, 53 patients were enrolled to constitute an independent test cohort. Clinical factors and imaging features, including LI-RADS and three other features (non-smooth margin, incomplete capsule, and two-trait predictor of venous invasion), were reviewed and analyzed. Radiomic features from four MRI sequences were extracted. The independent clinic-imaging (clinical) and radiomics model for MVI-prediction were constructed by logistic regression and AdaBoost respectively. And the clinic-radiomics combined model was further constructed by logistic regression. We assessed the model discrimination, calibration, and clinical usefulness by using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision-curve analysis respectively., Results: Incomplete tumor capsule, corona enhancement, and radiomic features were related to MVI in solitary HCC＜5 cm. The clinical model achieved AUC of 0.694/0.661 (training/internal validation). The single-sequence-based radiomic model's AUCs were 0.753-0.843/0.698-0.767 (training/internal validation). The combination model exhibited superior diagnostic performance to the clinical model (AUC: 0.895/0.848 [training/ internal validation]) and yielded an AUC of 0.858 in an independent test cohort., Conclusion: Incomplete tumor capsule and corona enhancement on preoperative MRI were significantly related to MVI in solitary HCC＜5 cm. Multiple-sequence radiomic features potentially improve MVI-prediction-model performance, which could potentially help determining HCC's appropriate therapy., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bing Feng reports article publishing charges was provided by Beijing Hope Run Special Fund of Cancer Foundation of China (No. LC2021B05). The authors of this manuscript declare that Siyun Liu and Sicong Wang are statisticians from GE Healthcare. The other authors declare no competing interests., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objectives: Impeded diffusion fraction (IDF) is a novel and promising diffusion-weighted imaging (DWI) technique that allows for the detection of various diffusion compartments, including macromolecular coordinated water, free diffusion, perfusion, and cellular free water. This study aims to investigate the clinical potential of IDF-DWI in detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC)., Methods: 66 patients were prospectively included. Metrics derived from IDF-DWI and the apparent diffusion coefficient (ADC) were calculated. Multivariate logistic regression was employed to identify clinical risk factors. Diagnostic performance was evaluated using the area under the receiver operating characteristics curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and the calibration error (cal-error). Additionally, a power analysis was conducted to determine the required sample size., Results: The results suggested a significantly higher fraction of impeded diffusion (F ID ) originating from IDF-DWI in MVI-positive HCCs (p < 0.001). Moreover, the ADC was found to be significantly lower in MVI-positive HCCs (p = 0.019). Independent risk factors of MVI included larger tumor size and elevated alpha-fetoprotein (AFP) levels. The nomogram model incorporating ADC, F ID , tumor size, and AFP level yielded the highest diagnostic accuracy for MVI (AUC-PR = 0.804, AUC-ROC = 0.783, cal-error = 0.044), followed by F ID (AUC-PR = 0.693, AUC-ROC = 0.760, cal-error = 0.060) and ADC (AUC-PR = 0.570, AUC-ROC = 0.651, cal-error = 0.164)., Conclusion: IDF-DWI shows great potential in noninvasively, accurately, and preoperatively detecting MVI in HCC and may offer clinical benefits for prognostic prediction and determination of treatment strategy., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background and Aims: Microvascular invasion (MVI) is a crucial pathological hallmark of HCC that is closely associated with poor outcomes, early recurrence, and intrahepatic metastasis following surgical resection and transplantation. However, the intricate tumor microenvironment and transcriptional programs underlying MVI in HCC remain poorly understood., Approach and Results: We performed single-cell RNA sequencing of 46,789 individual cells from 10 samples of MVI+ (MVI present) and MVI- (MVI absent) patients with HCC. We conducted comprehensive and comparative analyses to characterize cellular and molecular features associated with MVI and validated key findings using external bulk, single-cell, and spatial transcriptomic datasets coupled with multiplex immunofluorescence assays. The comparison identified specific subtypes of immune and stromal cells critical to the formation of the immunosuppressive and pro-metastatic microenvironment in MVI+ tumors, including cycling T cells, lysosomal associated membrane protein 3+ dendritic cells, triggering receptor expressed on myeloid cells 2+ macrophages, myofibroblasts, and arterial i endothelial cells. MVI+ malignant cells are characterized by high proliferation rates, whereas MVI- malignant cells exhibit an inflammatory milieu. Additionally, we identified the midkine-dominated interaction between triggering receptor expressed on myeloid cells 2+ macrophages and malignant cells as a contributor to MVI formation and tumor progression. Notably, we unveiled a spatially co-located multicellular community exerting a dominant role in shaping the immunosuppressive microenvironment of MVI and correlating with unfavorable prognosis., Conclusions: This study provides a comprehensive single-cell atlas of MVI in HCC, shedding light on the complex multicellular ecosystem and molecular features associated with MVI. These findings deepen our understanding of the underlying mechanisms driving MVI and provide valuable insights for improving clinical diagnosis and developing more effective treatment strategies., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: The prognosis of HCC patients without MVI (so called M0) is highly heterogeneous and the need for adjuvant therapy is still controversial., Methods: Patients with HCC with M0 who underwent liver resection (LR) or liver transplantation (LT) as an initial therapy were included. The Eastern Hepatobiliary Surgery Hospital (EHBH)-M0 score was developed from a retrospective cohort to form the training cohort. The classification which was developed using multivariate cox regression analysis was externally validated., Results: The score was developed using the following factors: α-fetoprotein level, tumour diameter, liver cirrhosis, total bilirubin, albumin and aspartate aminotransferase. The score differentiated two groups of M0 patients (≤3, >3 points) with distinct long-term prognoses outcomes (median overall survival (OS), 98.0 vs. 46.0 months; p < 0.001). The predictive accuracy of the score was greater than the other commonly used staging systems for HCC. And for M0 patients with a higher score underwent LR. Adjuvant transcatheter arterial chemoembolization (TACE) was effective to prolong OS., Conclusions: The EHBH M0 scoring system was more accurate in predicting the prognosis of HCC patients with M0 after LR or LT. Adjuvant therapy is recommended for HCC patients who have a higher score., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background and Aims: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans., Methods: Utilising a retrospective dataset of 725 HCC patients from seven institutions, we developed and validated a multitask deep learning model focused on predicting MVI and RFS. The model employs a transformer architecture to extract critical features from preoperative MRI scans. It was trained on a set of 234 patients and internally validated on a set of 58 patients. External validation was performed using three independent sets (n = 212, 111, 110)., Results: The multitask deep learning model yielded high MVI prediction accuracy, with AUC values of 0.918 for the training set and 0.800 for the internal test set. In external test sets, AUC values were 0.837, 0.815 and 0.800. Radiologists' sensitivity and inter-rater agreement for MVI prediction improved significantly when integrated with the model. For RFS, the model achieved C-index values of 0.763 in the training set and ranged between 0.628 and 0.728 in external test sets. Notably, PA-TACE improved RFS only in patients predicted to have high MVI risk and low survival scores (p < .001)., Conclusions: Our deep learning model allows accurate MVI and survival prediction in HCC patients. Prospective studies are warranted to assess the clinical utility of this model in guiding personalised treatment in conjunction with clinical criteria., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Hepatocellular carcinoma (HCC) presents a significant threat to individuals and healthcare systems due to its high recurrence rate. Accurate prognostic models are essential for improving patient outcomes. Gamma-glutamyl transpeptidase (GGT) and prealbumin (PA) are biomarkers closely related to HCC. This study aimed to investigate the predictive value of the GGT to PA ratio (GPR) and to construct prognostic nomograms for HCC patients without microvascular invasion., Methods: We retrospectively analyzed data from 355 HCC patients who underwent radical hepatectomy at Shengjing Hospital of China Medical University between December 2012 and January 2021. Patients were randomly assigned to a training cohort (n = 267) and a validation cohort (n = 88). The linearity of GPR was assessed using restricted cubic spline (RCS) analysis, and the optimal cut-off value was determined by X-tile. Kaplan-Meier survival curves and log-rank tests were used to investigate the associations between GPR and both progression-free survival (PFS) and overall survival (OS). Cox multivariate regression analysis identified independent risk factors, enabling the construction of nomograms. Time-dependent receiver operating characteristic (ROC) and calibration curves were used to evaluate the accuracy of the nomograms. Decision curve analysis (DCA) assessed the predictive value of the models., Results: Patients were categorized into GPR-low and GPR-high groups based on a GPR value of 333.33. Significant differences in PFS and OS were observed between the two groups (both P < 0.001). Cox multivariate analysis identified GPR as an independent risk factor for both PFS (OR = 1.80, 95% CI: 1.24-2.60, P = 0.002) and OS (OR = 1.87, 95% CI: 1.07-3.26, P = 0.029). The nomograms demonstrated good predictive performance, with C-index values of 0.69 for PFS and 0.76 for OS. Time-dependent ROC curves and calibration curves revealed the accuracy of the models in both the training and validation cohorts, with DCA results indicating notable clinical value., Conclusions: GPR emerged as an independent risk factor for both OS and PFS in HCC patients without microvascular invasion. The nomograms based on GPR demonstrated relatively robust predictive efficiency for prognosis., (© 2024. The Author(s).)

Purpose: Microvascular invasion (MVI) is a major unfavorable prognostic factor for intrahepatic metastasis and postoperative recurrence of hepatocellular carcinoma (HCC). However, the intervention and preoperative prediction for MVI remain clinical challenges due to the absent precise mechanism and molecular marker(s). Herein, we aimed to investigate the mechanisms underlying vascular invasion that can be applied to clinical intervention for MVI in HCC., Experimental Design: The histopathologic characteristics of clinical MVI+/HCC specimens were analyzed using multiplex immunofluorescence staining. The liver orthotopic xenograft mouse model and mechanistic experiments on human patient-derived HCC cell lines, including coculture modeling, RNA-sequencing, and proteomic analysis, were used to investigate MVI-related genes and mechanisms., Results: IQGAP3 overexpression was correlated significantly with MVI status and reduced survival in HCC. Upregulation of IQGAP3 promoted MVI+-HCC cells to adopt an infiltrative vessel co-optive growth pattern and accessed blood capillaries by inducing detachment of activated hepatic stellate cells (HSC) from the endothelium. Mechanically, IQGAP3 overexpression contributed to HCC vascular invasion via a dual mechanism, in which IQGAP3 induced HSC activation and disruption of the HSC-endothelial interaction via upregulation of multiple cytokines and enhanced the trans-endothelial migration of MVI+-HCC cells by remodeling the cytoskeleton by sustaining GTPase Rac1 activity. Importantly, systemic delivery of IQGAP3-targeting small-interfering RNA nanoparticles disrupted the infiltrative vessel co-optive growth pattern and reduced the MVI of HCC., Conclusions: Our results revealed a plausible mechanism underlying IQGAP3-mediated microvascular invasion in HCC, and provided a potential target to develop therapeutic strategies to treat HCC with MVI., (©2024 American Association for Cancer Research.)

Objectives: Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram., Methods: We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram., Results: 27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P = 0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoural artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927)., Conclusions: The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options., Advances in Knowledge: GCN based on MRI could predict MVI on HCC., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: To elucidate how precisely microvascular invasion (MVI) in hepatocellular carcinoma (HCC) can be predicted using multiparametric assessment of gadoxetic acid-enhanced MRI., Methods: In this retrospective single-center study, patients who underwent liver resection or transplantation of HCC were evaluated. Data obtained in patients who underwent liver resection were used as the training set. Nine kinds of MR findings for predicting MVI were compared between HCCs with and without MVI by univariate analysis, followed by multiple logistic regression analysis. Using significant findings, a predictive formula for diagnosing MVI was obtained. The diagnostic performance of the formula was investigated in patients who underwent liver resection (validation set 1) and in patients who underwent liver transplantation (validation set 2) using a receiver operating characteristic curve analysis. The area under the curves (AUCs) of these three groups were compared., Results: A total of 345 patients with 356 HCCs were selected for analysis. Tumor diameter (D) (P = 0.021), tumor washout (TW) (P < 0.01), and peritumoral hypointensity in the hepatobiliary phase (PHH) (P < 0.01) were significantly associated with MVI after multivariate analysis. The AUCs for predicting MVI of the predictive formula were as follows: training set, 0.88 (95% confidence interval (CI) 0.82,0.93); validation set 1, 0.81 (95% CI 0.73,0.87); validation set 2, 0.67 (95% CI 0.51,0.80). The AUCs were not significantly different among three groups (training set vs validation set 1; P = 0.15, training set vs validation set 2; P = 0.09, validation set 1 vs validation set 2; P = 0.29, respectively)., Conclusion: Our multiparametric assessment of gadoxetic acid-enhanced MRI performed quite precisely and with good reproducibility for predicting MVI., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: Habitat imaging allows for the quantification and visualization of various subregions within the tumor. We aim to develop an approach using diffusion-weighted imaging (DWI)-based habitat imaging for preoperatively predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC)., Methods: Sixty-five patients were prospectively included and underwent multi-b DWI examinations. Based on the true diffusion coefficient (D t ), perfusion fraction (f), and mean kurtosis coefficient (MK), which respectively characterize cellular density, perfusion, and heterogeneity, the HCCs were divided into four habitats. The volume fraction of each habitat was quantified. The logistic regression was used to explore the risk factors from habitat fraction and clinical variables. Clinical, habitat, and nomogram models were constructed using the identified risk factors from clinical characteristics, habitat fraction, and their combination, respectively. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curves (AUCs)., Results: MVI-positive HCC exhibited a significantly higher fraction of habitat 4 (f 4 ) and a significantly lower fraction of habitat 2 (f 2 ) (p < 0.001), which were selected as risk factors. Additionally, tumor size and elevated alpha-fetoprotein (AFP) were also included as risk factors for MVI. The nomogram model demonstrated the highest diagnostic performance (AUC = 0.807), followed by the habitat model (AUC = 0.777) and the clinical model (AUC = 0.708). Decision curve analysis indicated that the nomogram model offered more net benefit in identifying MVI compared to the clinical model., Conclusions: DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively determining the MVI of HCC with high accuracy., Clinical Relevance Statement: The proposed strategy, diffusion-weighted imaging-based habitat imaging, can be applied for preoperatively and noninvasively identifying microvascular invasion in hepatocellular carcinoma, which offers potential benefits in terms of prognostic prediction and clinical management., Key Points: • This study proposed a strategy of DWI-based habitat imaging for hepatocellular carcinoma. • The habitat imaging-derived metrics can serve as diagnostic markers for identifying the microvascular invasion. • Integrating the habitat-based metric and clinical variable, a predictive nomogram was constructed and displayed high accuracy for predicting microvascular invasion., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Hepatectomy is widely considered a potential treatment for hepatocellular carcinoma (HCC). Unfortunately, one-third of HCC patients have tumor recurrence within 2 years after surgery (early recurrence), accounting for more than 60% of all recurrence patients. Early recurrence is associated with a worse prognosis. Previous studies have shown that microvascular invasion (MVI) is one of the key factors for early recurrence and poor prognosis in patients with HCC after surgery. This paper reviews the latest literature and summarizes the predictors of MVI, the correlation between MVI and early recurrence, the identification of suspicious nodules or subclinical lesions, and the treatment strategies for MVI-positive HCC. The aim is to explore the management of patients with MVI-positive HCC., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to disclose., (Copyright © 2024 Asian Surgical Association and Taiwan Society of Coloproctology. Published by Elsevier B.V. All rights reserved.)

Rationale and Objectives: The study was designed to evaluate microvascular invasion (MVI) using three-dimensional (3D) morphological indicators prior to surgery., Materials and Methods: This retrospective study included 156 patients with hepatocellular carcinoma (HCC) at our hospital from 2017 to 2018. Through thin-layer CT scanning and 3D reconstruction, the tumor surface inclination angles can be quantitatively analyzed to determine the surface irregularity rate (SIR), which serves as a comprehensive assessment method for tumor irregularity based on preoperative 3D morphological evaluation. Univariate and multivariate logistic regression analyses were employed to investigate the correlation with MVI., Results: The SIR was related to MVI (OR: 10.667, P < 0.001). Multivariate logistic regression analysis showed that the SIR was an independent risk factor for MVI. The area under the receiver operating characteristic curve (ROC) of prediction model composed of the morphological indicator SIR was 0.831 (95% confidence interval: 0.759-0.895)., Conclusion: The preoperative 3D morphological indicator SIR of a tumor is an accurate predictor of MVI, providing a valuable tool in clinical decision-making., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: Varying diffusion curvature (VDC) MRI is an emerging diffusion-weighted imaging (DWI) technique that can capture non-Gaussian diffusion behavior and reflect tissue heterogeneity. However, its clinical utility has hardly been evaluated. We aimed to investigate the value of the VDC technique in noninvasively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC)., Methods: 74 patients with HCCs, including 39 MVI-positive and 35 MVI-negative HCCs were included into this prospective study. Quantitative metrics between subgroups, clinical risk factors, as well as diagnostic performance were evaluated. The power analysis was also carried out to determine the statistical power., Results: MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D 1 (0.680 ± 0.100 × 10 -3 vs 0.572 ± 0.148 × 10 -3  mm 2 /s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10 -3 vs 1.471 ± 0.322 × 10 -3  mm 2 /s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D 0 between the subgroups (p = 0.562). Tumor size (odds ratio (OR) = 1.242) and alpha-fetoprotein (AFP) (OR = 2.527) were identified as risk factors for MVI. A predictive nomogram was constructed based on D 1 , ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D 1 (AUC = 0.753) and ADC (AUC = 0.647). The diagnostic performance of the nomogram-based model was also validated by the calibration curve and decision curve., Conclusion: VDC can aid in the noninvasive and preoperative diagnosis of HCC with MVI, which may result in the clinical benefit in terms of prognostic prediction and clinical decision-making., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer, accounting for approximately 90% of liver cancer cases. It currently ranks as the fifth most prevalent cancer worldwide and represents the third leading cause of cancer-related mortality. As a malignant disease with surgical resection and ablative therapy being the sole curative options available, it is disheartening that most HCC patients who undergo liver resection experience relapse within five years. Microvascular invasion (MVI), defined as the presence of micrometastatic HCC emboli within liver vessels, serves as an important histopathological feature and indicative factor for both disease-free survival and overall survival in HCC patients. Therefore, achieving accurate preoperative noninvasive prediction of MVI holds vital significance in selecting appropriate clinical treatments and improving patient prognosis. Currently, there are no universally recognized criteria for preoperative diagnosis of MVI in clinical practice. Consequently, extensive research efforts have been directed towards preoperative imaging prediction of MVI to address this problem and the relative research progresses were reviewed in this article to summarize its current limitations and future research prospects.

Objective: This study aimed to investigate the feasibility of using dual-layer spectral CT multi-parameter feature to predict microvascular invasion of hepatocellular carcinoma., Methods: This retrospective study enrolled 50 HCC patients who underwent multiphase contrast-enhanced spectral CT studies preoperatively. Combined clinical data, radiological features with spectral CT quantitative parameter were constructed to predict MVI. ROC was applied to identify potential predictors of MVI. The CT values obtained by simulating the conventional CT scans with 70 keV images were compared with those obtained with 40 keV images., Results: 50 hepatocellular carcinomas were detected with 30 lesions (Group A) with microvascular invasion and 20 (Group B) without. There were significant differences in AFP,tumer size, IC, NIC,slope and effective atomic number in AP and ICrr in VP between Group A ((1000(10.875,1000),4.360±0.3105, 1.7750 (1.5350,1.8825) mg/ml, 0.1785 (0.1621,0.2124), 2.0362±0.2108,8.0960±0.1043,0.2830±0.0777) and Group B (4.750(3.325,20.425),3.190±0.2979,1.4700 (1.4500,1.5775) mg/ml, 0.1441 (0.1373,0.1490),1.8601±0.1595, 7.8105±0.7830 and 0.2228±0.0612) (all p < 0.05). Using 0.1586 as the threshold for NIC, one could obtain an area-under-curve (AUC) of 0.875 in ROC to differentiate between tumours with and without microvascular invasion. AUC was 0.625 with CT value at 70 keV and improved to 0.843 at 40 keV., Conclusion: Dual-layer spectral CT provides additional quantitative parameters than conventional CT to enhance the differentiation between hepatocellular carcinoma with and without microvascular invasion. Especially, the normalized iodine concentration (NIC) in arterial phase has the greatest potential application value in determining whether microvascular invasion exists, and can offer an important reference for clinical treatment plan and prognosis assessment.

Objective: To evaluate the preoperative predictive value of contrast-enhanced ultrasound (CEUS) combined with microflow imaging (MFI) in microvascular invasion (MVI) of hepatocellular carcinoma (HCC)., Methods: In our study, 80 patients with HCC were analyzed retrospectively. According to the gold standard of postoperative pathology, the patients were divided into MVI positive group (n = 39) and MVI negative group (n = 41). we were to analyze the correlation between CEUS and MVI in combination with MFI, to identify independent risk factors for the occurrence of MVI positive, and to analyze the predictive efficacy of every independent risk factor and their combination in preoperative prediction of MVI., Results: In our study, 80 patients were enrolled, including 39 patients in the MVI-positive group and 41 patients in the MVI-negative group, with a MVI-positive rate of 48.8%. By univariate analysis and multivariate analysis, it was found that there were statistically significant differences in enhancement range extension, start time of wash out and CEUS-MFI between the two groups, which were independent risk factors for MVI-positive. The combination of three independent risk factors is more effective than single one in predicting MVI of HCC., Conclusions: CEUS combined with MFI is feasible for the preoperative prediction of MVI in HCC, and can provides meaningful help for individualized clinical treatment.

Introduction and Objectives: In hepatocellular carcinoma (HCC), the prognosis of patients with microvascular invasion (MVI) is poor. Therefore, in this study, we established and evaluated the performance of a novel nomogram to predict MVI in patients with HCC., Materials and Methods: We retrospectively obtained clinical data of 497 patients with HCC who underwent hepatectomy at Liaoning Cancer Hospital from November 1, 2018, to November 4, 2021. The patients (n = 497) were randomized in a 7:3 ratio into the training cohort (TC, n = 349) and the validation cohort (VC, n = 148). We performed Least Absolute Shrinkage and Selection Operator (LASSO) and univariate as well as multivariate logistic regression analyses (ULRA, MRLA) on patients in the TC to identify factors independently predicting MVI., Results: Preoperative FIB-4, AFU, AFP levels, liver cirrhosis, and non-smooth tumor margin were independent risk factors for preoperative MVI prediction. The C-index of the TC, VC, and the entire cohort was 0.846, 0.786, and 0.829, respectively. The calibration curves demonstrated the outstanding agreement between predicted MVI incidences by our model and the actual MVI risk. Decision curve analysis (DCA) confirmed the significance of our predictive model in clinical settings. The Kaplan-Meier (KM) survival curve showed that the recurrence-free survival (RFS) and overall survival (OS) of patients in the high-MVI risk group were poor compared to those in the low-MVI risk group., Conclusions: We constructed and evaluated the performance of the novel nomogram for predicting MVI risk. Our predictive model could adequately predict MVI risk and aid clinicians in selecting appropriate therapeutic strategies for patients., Competing Interests: Declaration of interests None., (Copyright © 2023 Fundación Clínica Médica Sur, A.C. Published by Elsevier España, S.L.U. All rights reserved.)

Background and Aim: Microvascular invasion (MVI) is defined as the presence of micrometastatic cancer cell emboli in hepatic vessels, including small vessels, and at present, researchers believe that is an important factor for early postoperative recurrence and survival. Here, we developed and validated a preoperative predictive model for the presence of MVI in patients with ruptured hepatocellular carcinoma (rHCC)., Methods: We retrospectively collected data for 210 rHCC patients who underwent staged hepatectomy at Wuhan Tongji Hospital, and 91 patients who underwent staged hepatectomy at Zhongshan People's Hospital between January 2010 and March 2021. Then, the former was used as the training cohort and the latter was used as the validation cohort. Logistic regression was used to screen for variables associated with MVI, and these variables were used to construct nomograms. We used R software to assess the discrimination, calibration ability, as well as clinical efficacy of nomograms., Results: Multivariate logistic regression analysis identified four risk factors independently associated with MVI: max tumor length [odds ratio (OR) = 1.385; 95% confidence interval (CI), 1.072-1.790], number of tumors (OR = 2.182; 95% CI, 1.129-5.546), direct bilirubin (OR = 1.515; 95% CI, 1.189-1.930), and alpha-fetoprotein (cutoff = 400 ng/mL) (OR = 2.689; 95% CI, 3.395-13.547). Nomograms were built from the four variables and they were tested for discrimination and calibration, and the results were good., Conclusion: We developed and validated a preoperative predictive model for the presence of MVI in patients with ruptured HCC. This model can help clinicians identify patients at risk of MVI and make better treatment options., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Aim: This work is to explore the predictive and diagnostic value of chemokine C-X-C motif ligand 8 (CXCL8), CXCL9 and CXCL13 combined detections for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Materials & methods: A total of 82 HCC patients with MVI were recruited as the MVI group and 154 patients with non MVI were recruited as the non MVI group. Results: In HCC patients with MVI, CXCL8, CXCL9, CXCL13 levels were significantly elevated. Child-Pugh scores and serum α-fetoprotein level had positive correlation with CXCL8, CXCL9 and CXCL13 levels. The serum levels of CXCL8, 9 and 13 were effective in predicting MVI in HCC patients. Conclusion: CXCL8, CXCL9 and CXCL13 levels in HCC patients are valuable parameters in the prediction of MVI.

Objective: Hepatocellular carcinoma (HCC) is the sixth leading cause of malignant tumors worldwide. Liver resection is a pivotal treatment modality for HCC. Surgical margin plays an important role in decreasing recurrence and improving prognosis for HCC patients., Materials and Methods: This paper aimed to perform a systematic review of the literature in regard to surgical margin in HCC patients with microvascular invasion (MVI)., Results: Residual MVI due to insufficient surgical margins is the main origin of postoperative recurrence and metastasis in HCC patients. A wide surgical margin (WSM) significantly improves oncological outcomes and long-term survival in HCC patients with MVI. Progress in the preoperative prediction of MVI may contribute to precise surgical decision-making in the future., Conclusions: WSM was associated with better outcomes in HCC patients with MVI. WSM is recommended for well-preserved liver function HCC patients who are predicted to have a high risk of MVI preoperatively.

Microvascular invasion (MVI) has been clinically recognized as a prognostic factor for hepatocellular carcinoma (HCC) after surgical treatment. Detection of MVI before surgical operation greatly benefit patients' prognosis and survival. Most of the existing methods for automatic diagnosis of MVI directly use deep neural networks to make predictions, which do not take into account clinical knowledge and lack of interpretability. To simulate the radiologists' decision process, this paper proposes a Two-stage Expert-guided Diagnosis (TED) framework for MVI in HCC. Specifically, the first stage aims to predict key imaging attributes for MVI diagnosis, and the second stage leverages these predictions as a form of attention as well as soft supervision through a variant of triplet loss, to guide the fitting of the MVI diagnosis network. The attention and soft supervision are expected to jointly guide the network to learn more semantically correlated representations and thereafter increase the interpretability of the diagnosis network. Extensive experimental analysis on a private dataset of 466 cases has shown that the proposed method achieves 84.58% on AUC and 84.07% on recall, significantly exceeding the baseline methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Introduction: Microvascular invasion (MVI) is a known risk factor for prognosis after R0 liver resection for hepatocellular carcinoma (HCC). The aim of this study was to develop a deep learning prognostic prediction model by incorporating a new factor of MVI area to the other independent risk factors., Methods: Consecutive patients with HCC who underwent R0 liver resection from January to December 2016 at the Eastern Hepatobiliary Surgery Hospital were included in this retrospective study. For patients with MVI detected on resected specimens, they were divided into two groups according to the size of the maximal MVI area: the small-MVI group and the large-MVI group., Results: Of 193 patients who had MVI in the 337 HCC patients, 130 patients formed the training cohort and 63 patients formed the validation cohort. The large-MVI group of patients had worse overall survival (OS) when compared with the small-MVI group (p = 0.009). A deep learning model was developed based on the following independent risk factors found in this study: MVI stage, maximal MVI area, presence/absence of cirrhosis, and maximal tumor diameter. The areas under the receiver operating characteristic of the deep learning model for the 1-, 3-, and 5-year predictions of OS were 80.65, 74.04, and 79.44, respectively, which outperformed the traditional COX proportional hazards model., Conclusion: The deep learning model, by incorporating the maximal MVI area as an additional prognostic factor to the other previously known independent risk factors, predicted more accurately postoperative long-term OS for HCC patients with MVI after R0 liver resection., (© 2022. Asian Pacific Association for the Study of the Liver.)

Background: Hepatocellular carcinoma (HCC) is the third most common cause of cancer death worldwide, with an overall 5-year survival rate of less than 18%, which may be related to tumor microvascular invasion (MVI). This study aimed to compare the clinical prognosis of HCC patients with or without MVI after radical surgical treatment, and further analyze the preoperative risk factors related to MVI to promote the development of a new treatment strategy for HCC., Methods: According to the postoperative pathological diagnosis of MVI, 160 study patients undergoing radical hepatectomy were divided into an MVI-negative group (n = 68) and an MVI-positive group (n = 92). The clinical outcomes and prognosis were compared between the two groups, and then the parameters were analyzed by multivariate logistic regression to construct an MVI prediction model. Then, the practicability and validity of the model were evaluated, and the clinical prognosis of different MVI risk groups was subsequently compared., Result: There were no significant differences between the MVI-negative and MVI-positive groups in clinical baseline, hematological, or imaging data. Additionally, the clinical outcome comparison between the two groups presented no significant differences except for the pathological grading (P = 0.002) and survival and recurrence rates after surgery (P < 0.001). The MVI prediction model, based on preoperative AFP, tumor diameter, and TNM stage, presented superior predictive efficacy (AUC = 0.7997) and good practicability (high H-L goodness of fit, P = 0.231). Compared with the MVI high-risk group, the patients in the MVI low-risk group had a higher survival rate (P = 0.002) and a lower recurrence rate (P = 0.004)., Conclusion: MVI is an independent risk factor for a poor prognosis after radical resection of HCC. The MVI prediction model, consisting of AFP, tumor diameter, and TNM stage, exhibits superior predictive efficacy and strong clinical practicability for MVI prediction and prognostication, which provides a new therapeutic strategy for the standardized treatment of HCC patients., (© 2022. The Author(s).)

Liver cancer is one of the most common cancers leading to death in the world. Microvascular invasion (MVI) is a principal reason for the poor long-term survival rate after liver cancer surgery. Early detection and treatment are very important for improving the survival rate. Manual examination of MVI based on histopathological images is very inefficient and time consuming. MVI automatic diagnosis based on deep learning methods can effectively deal with this problem, reduce examination time, and improve detection efficiency. In recent years, deep learning-based methods have been widely used in histopathological image analysis because of their impressive performance. However, it is very challenging to identify MVI directly using deep learning methods, especially under the interference of hepatocellular carcinoma (HCC) because there is no obvious difference in the histopathological level between HCC and MVI. To cope with this problem, we adopt a method of classifying the MVI boundary to avoid interference from HCC. Nonetheless, due to the specificity of the histopathological tissue structure with the MVI boundary, the effect of transfer learning using the existing models is not obvious. Therefore, in this paper, according to the features of the MVI boundary histopathological tissue structure, we propose a new classification model, i.e., the PCformer, which combines the convolutional neural network (CNN) method with a visual transformer and improves the recognition performance of the MVI boundary histopathological image. Experimental results show that our method has better performance than other models based on a CNN or a transformer.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Microvascular invasion (MVI) is considered the major risk factor for postoperative recurrence and metastasis in HCC. The diagnosis of MVI relies on the postoperative pathological assessment of the tumor tissues. Seeking non-invasive methods and biomarkers for evaluation of MVI before surgery has important clinical implications for guiding surgical treatment and improving patients' survival. Recent studies have reported the applications of radiomics technique in prediction of MVI in HCC and showed promising results. Herein we summarized the research progress in CT- or MRI-based radiomics models for prediction of MVI in HCC to provide helpful thinking for further research in this field.

Hepatocellular carcinoma (HCC) is a kind of highly aggressive tumor of the digestive system. Several studies have confirmed that microvascular invasion (MVI) is an independent risk factor for early recurrence and poor prognosis of HCC after surgery. Currently, pathological examination is the gold standard for diagnosing MVI. This paper summarizes concept, prognosis, preoperative prediction and treatment plan based on literature review of MVI in HCC.

Background: The prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy. In terms of recent studies, microvascular invasion (MVI) is one of the potential predictors of recurrence. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning., Aim: To develop a radiomic analysis model based on pre-operative magnetic resonance imaging (MRI) data to predict MVI in HCC., Methods: A total of 113 patients recruited to this study have been diagnosed as having HCC with histological confirmation, among whom 73 were found to have MVI and 40 were not. All the patients received preoperative examination by Gd-enhanced MRI and then curative hepatectomy. We manually delineated the tumor lesion on the largest cross-sectional area of the tumor and the adjacent two images on MRI, namely, the regions of interest. Quantitative analyses included most discriminant factors (MDFs) developed using linear discriminant analysis algorithm and histogram analysis with MaZda software. Independent significant variables of clinical and radiological features and MDFs for the prediction of MVI were estimated and a discriminant model was established by univariate and multivariate logistic regression analysis. Prediction ability of the above-mentioned parameters or model was then evaluated by receiver operating characteristic (ROC) curve analysis. Five-fold cross-validation was also applied via R software., Results: The area under the ROC curve (AUC) of the MDF (0.77-0.85) outperformed that of histogram parameters (0.51-0.74). After multivariate analysis, MDF values of the arterial and portal venous phase, and peritumoral hypointensity in the hepatobiliary phase were identified to be independent predictors of MVI ( P < 0.05). The AUC value of the model was 0.939 [95% confidence interval (CI): 0.893-0.984, standard error: 0.023]. The result of internal five-fold cross-validation (AUC: 0.912, 95%CI: 0.841-0.959, standard error: 0.0298) also showed favorable predictive efficacy., Conclusion: Noninvasive MRI radiomic model based on MDF values and imaging biomarkers may be useful to make preoperative prediction of MVI in patients with primary HCC., Competing Interests: Conflict-of-interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background and Objectives: To evaluate the individual and combined associations of cytokeratin 19 (CK19) and microvascular invasion (MVI) with prognosis of patients with hepatocellular carcinoma (HCC)., Methods: Clinicopathological data on 352 patients with HCC who underwent radical resection at our hospital between January 2013 and December 2015 were retrospectively analyzed. Patients were divided into four groups: CK19(-)/MVI(-), CK19(-)/MVI(+), CK19(+)/MVI(-), and CK19(+)/MVI(+)., Results: Of the 352 HCC patients, 154 (43.8%) were CK19(-)/MVI(-); 116 (33.0%), CK19(-)/MVI(+); 31 (8.8%), CK19(+)/MVI(-); and 51 (14.5%), CK19(+)/MVI(+). The disease-free survival of CK19(-)/MVI(-) patients was significantly higher than that of CK19(-)/MVI(+) patients and CK19(+)/MVI(+) patients. Similar results were observed for overall survival. CK19(+)/MVI(+) patients showed significantly lower overall survival than the other three groups., Conclusions: CK19 expression and MVI predict poor prognosis after radical resection of HCC, and the two markers jointly contribute to poor OS. Combining CK19 and MVI may predict post-resection prognosis better than using either factor on its own., (© 2022. The Author(s).)

Background: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC., Methods: We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated, and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression., Results: Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and a-fetoprotein (AFP) were independently associated with MVI: DL-MVI (odds ratio [OR] = 35.738; 95% confidence interval [CI] 14.027-91.056; p < 0.001), AFP (OR = 4.634, 95% CI 2.576-8.336; p < 0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824., Conclusions: Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC., (© 2022. The Author(s).)

Background: Microvascular invasion (MVI) is a significant risk factor affecting survival outcomes of patients after R0 liver resection (LR) for hepatocellular carcinoma (HCC). The current classification of MVI is not refined enough to prognosticate long-term survival of these patients, and a new MVI classification is needed., Methods: Patients with HCC who underwent R0 LR at the Eastern Hepatobiliary Surgery Hospital from January 2013 to December 2013 and with resected specimens showing MVI were included in this study with an aim to establish a novel MVI classification. The classification which was developed using multivariate cox regression analysis was externally validated., Results: There were 180 patients in the derivation cohort and 131 patients in the external validation cohort. The following factors were used for scoring: α-fetoprotein level (AFP), liver cirrhosis, tumor number, tumor diameter, MVI number, and distance between MVI and HCC. Three classes of patients could be distinguished by using the total score: class A, ≤3 points; class B, 3.5-5 points and class C, >5 points with distinct long-term survival outcomes (median recurrence free survival (mRFS), 22.6, 10.2, and 1.9 months, P < 0.001). The predictive accuracy of this classification was more accurate than the other commonly used classifications for HCC patients with MVI. In addition, the mRFS of class C patients was significantly prolonged (1.9 months vs. 6.2 months, P < 0.001) after adjuvant transcatheter arterial chemoembolization (TACE)., Conclusions: A novel MVI classification was established in predicting prognosis of HCC patients with MVI after R0 LR. Adjuvant TACE was useful for class C patients., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021. Published by Elsevier Ltd.)