The microvascular invasion (MVI) score evaluates the presence of MVI in patients with hepatocellular carcinoma (HCC) by integrating multiple factors associated with MVI. We aimed to establish a MVI scoring system for HCC based on the clinical characteristics and serum biomarkers of patients with HCC.

A total of 1027 patients with HCC hospitalized at Shandong Provincial Hospital from January 2016 to August 2021 were included and randomly divided into the development group and validation group at a ratio of 3:1. Univariable and multivariable logistic regression analyses were conducted to identify independent risk factors for MVI in HCC patients. Based on these independent risk factors, the preoperative MVI scoring system (diagnostic model) for HCC was established and verified. The receiver operating characteristic (ROC) curves, calibration curves and decision curve analyses (DCA) were employed to evaluate the discrimination and clinical application of the diagnostic model.

Independent risk factors for MVI of HCC involved Hepatitis B virus infection (HBV), large tumor diameter, higher logarithm of Alpha-fetoprotein (Log AFP), higher logarithm of AFP-L3% (Log AFP-L3%), higher logarithm of protein induced by vitamin K absence or antagonist-II (Log PIVKA-II) and higher logarithm of Carbohydrate antigen 125 (Log CA125). The diagnostic model incorporating these six independent risk factors was finally established. The areas under the ROC curve (AUC) assessed by the nomogram in the development cohort and validation cohort were 0.806 (95% CI, 0.773-0.839) and 0.818 (95% CI, 0.763-0.874) respectively. The calibration curve revealed that the results predicted by our diagnostic model for MVI in HCC were highly consistent with the postoperative pathological outcomes. The DCA further indicated promising clinical application of the diagnostic model.

An effective preoperative diagnostic model for MVI of HCC based on readily available tumor markers and clinical characteristics has been established, which is both clinically significant and easy to implement for diagnosing MVI.

Integrating vessels encapsulating tumor clusters (VETC) and microvascular invasion (MVI) (VM hereafter) is potentially useful in risk stratification of hepatocellular carcinoma (HCC). However, noninvasive assessment methods for VM are lacking.

To investigate the diagnostic performance of tomoelastography in assessing the VM status in HCC.

Retrospective.

One hundred sixty-eight patients with surgically confirmed HCC consisting of 115 training and 53 validation cohorts, divided into negative-VM and positive-VM groups with mild or severe-VMs. Of them, 127 patients completed the follow-up (median: 26.1 months).

3D multifrequency tomoelastography with a single-shot spin-echo echo-planar imaging sequence, and liver MRI including T1-weighted in-phase and opposed-phase gradient echo (GRE), T2-weighted turbo spin echo, diffusion-weighted imaging and dynamic contrast-enhanced T1-weighted GRE sequences at 3.0 T.

Shear wave speed (c) and phase angle of the shear modulus (φ) were calculated on tomoelastograms. Imaging features were visually analyzed and clinical features were collected. Conventional models used clinical and imaging features while nomograms combined tomoelastography, clinical and imaging features.

Univariable and multivariable logistic regression analyses, nomogram, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log-rank test. P < 0.05 was considered statistically significant.

Tumor-to-liver parenchyma ratio of c (cr) and tumor c were independent risk factors for positive-VM and severe-VM, respectively. In validation cohort, the nomograms including cr and tumor c performed significantly better than the conventional models for diagnosing positive-VM (0.84 [95% CI: 0.72-0.93] vs. 0.77 [95% CI: 0.64-0.88]) and severe-VM (0.86 [95% CI: 0.68-0.96] vs. 0.75 [95% CI: 0.55-0.89]). Patients with estimated positive-VM (9.3 months)/severe-VM (9.2 months) based on nomograms had shorter median recurrence-free survival than those with estimated negative-VM (>20.0 months)/mild-VM (18.0 months) in validation cohort.

Tomoelastography based-nomograms showed good performance for noninvasively assessing VM status in patients with HCC.

3 TECHNICAL EFFICACY: Stage 2.

This study aimed to develop two preoperative magnetic resonance imaging (MRI) based models for detecting and classifying microvascular invasion (MVI) in early-stage small hepatocellular carcinoma (sHCC) patients.

MVI is graded as M0 (no invasion), M1 (invasion of five or fewer vessels located within 1 cm of the tumor's peritumoral region), and M2 (invasion of more than five vessels or those located more than 1 cm from the tumor's surface). This study enrolled 395 early-stage sHCC (≤ 3 cm) patients from three centers who underwent preoperative gadopentetate-enhanced MRI. From the first two centers, 310 patients were randomly divided into training (n = 217) and validation (n = 93) cohorts in a 7:3 ratio to develop the first model for predicting MVI presence. Among these, 153 patients with identified MVI were further divided into training (n = 112) and validation (n = 41) cohorts, using the same ratio, to construct the second model for MVI classification. An independent test cohort of 85 patients from the third center to validate both models. Univariate and multivariate logistic regression analyses identified independent predictors of MVI and its classification in the training cohorts. Based on these predictors, two nomograms were developed and assessed for their discriminative ability, calibration, and clinical usefulness. Besides, considering the two models are supposed applied in a serial fashion in the real clinical setting, we evaluate the performance of the two models together on the test cohorts by applying them simultaneously. Kaplan-Meier survival curve analysis was employed to assess the correlation between predicted MVI status and early recurrence, similar to the association observed with actual MVI status and early recurrence.

The MVI detection nomogram, with platelet count (PLT), activated partial thromboplastin time (APTT), rim arterial phase hyperenhancement (Rim APHE) and arterial peritumoral enhancement, achieved area under the curve (AUC) of 0.827, 0.761 and 0.798 in the training, validation, and test cohorts, respectively. The MVI classification nomogram, integrating Total protein (TP), Shape, Arterial peritumoral enhancement and enhancement pattern, achieved AUC of 0.824, 0.772, and 0.807 across the three cohorts. When the two models were applied on the test cohorts in a serial fashion, they both demonstrated good performance, which means the two models had good clinical applicability. Calibration and decision curve analysis (DCA) results affirmed the model's reliability and clinical utility. Notably, early recurrence was more prevalent in the MVI grade 2 (M2) group compared to the MVI-absent and M1 groups, regardless of the actual or predicted MVI status.

The nomograms exhibited excellent predictive performance for detecting and classifying MVI in patients with early-stage sHCC, particularly identifying high-risk M2 patients preoperatively.

To investigate the influence of postoperative adjuvant transarterial chemoembolization (PA-TACE) on the prognosis of hepatocellular carcinoma (HCC) patients with microvascular invasion (MVI) following liver resection (LR), and explore whether grading risk of MVI can impact the survival of HCC patients undergoing PA-TACE.

Patients who had HCC with MVI were consecutively enrolled. Overall survival (OS) and recurrence-free survival (RFS) were compared between the PA-TACE and LR groups. Univariate and multivariate analyses were performed to identify independent prognostic factors for these patients. Subgroup survival analysis was conducted using the grading risk of MVI.

The median OS and RFS of the PA-TACE group were significantly longer than the LR group. PA-TACE was associated with significantly better OS (P = 0.032) and RFS (P = 0.023) compared with LR alone. In subgroup analysis, there were no significant differences in prognosis between the PA-TACE and LR groups for HCC patients with low-risk MVI. For HCC patients with high-risk MVI, the PA-TACE group had significantly better prognosis than the LR group (for OS, P = 0.017; for RFS, P = 0.018).

PA-TACE should be performed selectively in HCC patients with high-risk MVI after curative liver resection. Nonetheless, for HCC patients with low-risk MVI, PA-TACE is not recommended.

Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) images, we developed a novel radiomics model. It combined bi-regional features and two machine learning algorithms. The aim of this study was to validate its potential value for preoperative prediction of MVI.

This retrospective study included 304 HCC patients (training cohort, 216 patients; testing cohort, 88 patients) from three hospitals. Intratumoral and peritumoral volumes of interest were delineated in arterial phase, portal venous phase, and hepatobiliary phase images. Conventional radiomics (CR) and deep learning radiomics (DLR) features were extracted based on FeAture Explorer software and the 3D ResNet-18 extractor, respectively. Clinical variables were selected using univariate and multivariate analyses. Clinical, CR, DLR, CR-DLR, and clinical-radiomics (Clin-R) models were built using support vector machines. The predictive capacity of the models was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

The bi-regional CR-DLR model showed more gains and gave better predictive performance than the single-regional models or single-machine learning models. Its AUC, accuracy, sensitivity, and specificity were 0.844, 76.9%, 87.8%, and 69.1% in the training cohort and 0.740, 73.9%, 50%, and 84.5% in the testing cohort. Alpha-fetoprotein (odds ratio was 0.32) and maximum tumor diameter (odds ratio was 1.270) were independent predictors. The AUCs of the clinical model and the Clin-R model were 0.655 and 0.672, respectively. There was no significant difference in the AUCs between all the models (P > 0.005).

Based on Gd-EOB-DTPA-enhanced MRI images, we focused on developing a radiomics model that combines bi-regional features and two machine learning algorithms (CR and DLR). The application of the new model will provide a more accurate and non-invasive diagnostic solution for medical imaging. It will provide valuable information for clinical personalized treatment, thereby improving patient prognosis.

Not applicable.

Adjuvant lenvatinib in combination with transarterial chemoembolization (TACE) has demonstrated prolonged disease-free survival in hepatocellular carcinoma patients at high risk of recurrence post-resection. Here, we present the case of a 68-year-old woman who developed serious side effects including pemphigus erythematosus (PE) linked to lenvatinib usage. Initially treated for breast cancer with radical surgery in April 2018 followed by adjuvant therapy, she was later diagnosed with liver cancer, initially mistaken for metastatic breast cancer to the liver. Although systemic treatment for advanced breast cancer was received, the tumor continued to progress and required partial removal of the liver after final evaluation. Subsequent pathology revealed hepatocellular carcinoma combined with risk factors for recurrence, prompting adjuvant therapy with TACE and oral lenvatinib. After three weeks of lenvatinib administration, the patient developed a skin rash diagnosed as PE through skin pathology. Treatment involved oral methylprednisolone, intravenous human immune globulin, and supportive care, resulting in a cure within a month. This unique case highlights the importance of further research not only on lenvatinib but also on monitoring and managing adverse reactions associated with targeted drugs to optimize patient safety and treatment outcomes.

Hepatocellular carcinoma (HCC) has a poor prognosis, often characterized by microvascular invasion (MVI). Radiomics and habitat imaging offer potential for preoperative MVI assessment.

To identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat-derived radiomic analysis.

Retrospective.

Three hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94).

1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three-dimensional gradient echo sequence.

Clinical model, habitat model, single sequence radiomic models, the peritumor habitat-derived radiomic model, and the combined models were constructed for evaluating MVI. Follow-up clinical data were obtained by a review of medical records or telephone interviews.

Univariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K-M curves, log rank test. A P-value less than 0.05 (two sides) was considered to indicate statistical significance.

Habitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic-Pre model demonstrated AUCs of 0.815 (95% CI: 0.752-0.878) and 0.708 (95% CI: 0.599-0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic-HBP were 0.790 (95% CI: 0.724-0.855) for the training cohort and 0.712 (95% CI: 0.604-0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1-4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High-risk patients (cutoff value >0.11) identified by this model showed shorter recurrence-free survival.

The combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk.

3 TECHNICAL EFFICACY: Stage 2.

We aimed to develop a convenient nomogram to predict preoperative MVI in patients with hepatocellular carcinoma (HCC). Patients who underwent surgical resection due to HCC from June 2018 to June 2023 at the Third Affiliated Hospital of Sun Yat-sen University were retrospectively reviewed. Univariate and multivariable logistic linear regression analyses were used to investigate potential risk factors for MVI. A nomogram was plotted based on these risk factors. The tumor diameter (≥ 5 cm), BCLC stage, PLT (>127.50 × 109/L), AST (>29.50 U/L) and AFP (>10.07 ng/ml) were identified as independent preoperative risk factors for MVI by univariate and multivariable logistic analysis. The nomogram demonstrated decent accuracy in estimating the presence of MVI, with an AUC of 0.69 (95%CI: 0.64-0.73). The calibration curves exhibited a close match between the predicted probabilities and the actual estimates of MVI in the nomogram (p = 0.947). Decision curve analysis (DCA) revealed that the prediction model had a high net benefit if the threshold probability>20%. High platelet counts were strongly associated with the presence of MVI in HCC patients. Our convenient nomogram demonstrated decent accuracy in estimating the presence of MVI and had notable clinical application.

Surgical resection for hepatocellular carcinoma (HCC) is burdened with a high recurrence rate and a lack of reliable prognostic factors. The aim of this study was to integrate the HCC pathological features with gene mutations to improve the prognostic role of pathological analysis. This is a monocentric prospective study, including 67 patients resected for HCC. All clinical data and histological features were collected, including tumor grade, architecture, margins, microvascular invasion, and microscopic portal vascular invasion (MPVI). Next-generation sequencing (NGS) was performed using a laboratory-developed multi-gene panel, allowing to amplify 330 amplicons (21.77 kb), covering the relevant targets for solid tumor analysis. The most represented mutations were TERT promoter (n = 41, 61.2%), TP53 (n = 18, 26.9%) and CTNNB1 (n = 17, 25.4%). At follow-up, 13 (19.4%) patients experienced HCC recurrence: at multivariate analysis, tumor dimensions (p = 0.040), MPVI (p = 0.010), and TERT mutation (p = 0.034) correlated with recurrence. Dimensions ≥ 4.5 cm (very close to AJCC stage pT3; 9 recurrences, p = 0.041, odd-ratio = 3.7), MPVI (9 recurrences, p = 0.062, OR = 3.3), and TERT (11 recurrences, p = 0.049, OR = 4.4) correlated with disease-free survival also at univariate analysis. The concomitant occurrence of these three variables was present in 7 cases, among which 5 recurred (p = 0.002, OR = 15.94). In conclusion, NGS analysis in resected HCC could not only be used for future therapies but should be integrated with histopathology to predict the risk of tumor recurrence after surgical resection: TERT mutation is among the strongest predictors of tumor recurrence, together with tumor stage (dimensions) and the occurrence of MPVI, which should always be reported separately from the classic MVI.

Previous studies mostly use single-type features to establish a prediction model. We aim to develop a comprehensive prediction model that effectively identify patients with poor prognosis for single hepatocellular carcinoma (HCC) based on artificial intelligence (AI). Patients and methods: 236 single HCC patients were studied to establish a comprehensive prediction model. We collected the basic information of patients and used AI to extract the features of magnetic resonance (MR) images.

The clinical model based on linear regression (LR) algorithm (AUC: 0.658, 95%CI: 0.5021-0.8137), the radiomics model and deep transfer learning (DTL) model based on light gradient-boosting machine (Light GBM) algorithm (AUC: 0.761, 95%CI: 0.6326-0.8886 and AUC: 0.784, 95%CI: 0.6587-0.9087, respectively) were the optimal prediction models. A comparison revealed that the integrated nomogram had the largest area under the receiver operating characteristic curve (AUC) (all P < 0.05). In the training cohort, the integrated nomogram was predictive of recurrence-free survival (RFS) as well as overall survival (OS) (C-index: 0.735 and 0.712, P < 0.001). In the test cohort, the integrated nomogram also can predict RFS and OS (C-index: 0.718 and 0.740, P < 0.001) in patients.

The integrated nomogram composed of signatures in the prediction models can not only predict the postoperative recurrence of single HCC patients but also stratify the risk of OS after the operation.

Personalized precision medicine can be facilitated by clinically available preoperative microvascular invasion (MVI) prediction models that are reliable and postoperative MVI pathological grade-related recurrence prediction models that are accurate. In this study, we aimed to compare different mathematical models to derive the best preoperative prediction and postoperative recurrence prediction models for MVI.

A total of 143 patients with hepatocellular carcinoma (HCC) whose clinical, laboratory, imaging, and pathological data were available were included in the analysis. Logistic regression, Cox proportional hazards regression, LASSO regression with 10-fold cross-validation, stepwise regression, and random forest methods were used for variable screening and predictive modeling. The accuracy and validity of seven preoperative MVI prediction models and five postoperative recurrence prediction models were compared in terms of C-index, net reclassification improvement, and integrated discrimination improvement.

Multivariate logistic regression analysis revealed that a preoperative nomogram model with the variables cirrhosis diagnosis, alpha-fetoprotein > 400, and diameter, shape, and number of lesions can predict MVI in patients with HCC reliably. Postoperatively, a nomogram model with MVI grade, number of lesions, capsule involvement status, macrovascular invasion, and shape as the variables was selected after LASSO regression and 10-fold cross-validation analysis to accurately predict the prognosis for different MVI grades. The number and shape of the lesions were the most common predictors of MVI preoperatively and recurrence postoperatively.

Our study identified the best statistical approach for the prediction of preoperative MVI as well as postoperative recurrence in patients with HCC based on clinical, imaging, and laboratory tests results. This could expedite preoperative treatment decisions and facilitate postoperative management.

To investigate the impact of surgical resection margin and hepatic resection type on prognosis and compare their prognostic significance on patients with hepatocellular carcinoma (HCC) with or without microvascular invasion (MVI) who underwent laparoscopic liver resection (LLR).

A retrospective analysis was conducted on 320 patients with HCC who underwent LLR. According to the grading of MVI, patients were classified as M0, M1 and M2. Patients were divided into the anatomical resection (AR) and nonanatomical resection (NAR) groups according to the hepatic resection type. Survival and Cox regression analyses were performed to explore the effects of AR and NAR, wide and narrow resection margin on overall survival (OS) and time to recurrence (TTR).

In the whole cohort, narrow resection margin was an independent risk factor for OS and TTR, whereas NAR was not. Subgroup analysis showed that narrow resection margin and NAR were both independent risk factors for OS and TTR in HCC patients with MVI. The 5-year OS and TTR rates of the two groups (NAR-wide resection margin and AR-narrow resection margin) with M1 were 85.3 % versus 62 % and 34.4 % versus 60.2 %. Similarly, the 5-year OS and TTR rates of the two groups (NAR-wide resection margin and AR-narrow resection margin) with M2 were 80.2 % versus 47.9 % and 30.8 % versus 64.8 %.

Anatomical hepatectomy and wide resection margin were independent protective factors for HCC patients with MVI receiving LLR. Nonetheless, wide resection margin had a greater impact on prognosis than anatomical hepatectomy.

To develop and validate a cross-modality tensor fusion (CMTF) model using multi-modality MRI radiomics features and clinical radiological characteristics for the prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

This study included 174 HCC patients (47 MVI-positive and 127 MVI-negative) confirmed by postoperative pathology. The synthetic minority over-sampling technique was used to augment MVI-positive samples. The amplified dataset of 254 samples (127 MVI-positive and 127 MVI-negative) was randomly divided into training and test cohorts in a 7:3 ratio. Radiomics features were respectively extracted from arterial phase, delayed phase, diffusion-weighted imaging, and fat-suppressed T2-weighted imaging. The least absolute shrinkage and selection operator was used for feature selection. Univariate and multivariate logistic regression analyses were employed to identify clinical and radiological independent predictors. The selected multi-modality MRI radiomics features, clinical and radiological characteristics were used to construct the CMTF model, single modality (SM) model, early fusion (EF) model.

The CMTF model demonstrated superior performance in predicting MVI compared to the SM and EF models. When integrating four MRI modalities, the CMTF model achieved a high area under the curve (AUC) with 95 % confidence interval (95 % CI) of 0.894 (0.820-0.968). Additionally, incorporating clinical and radiological characteristics further enhanced the predictive performance of CMTF model, the AUC (95 % CI) value increased to 0.945 (0.892-0.998).

The CMTF model showed promising performance in preoperative MVI prediction, providing a more effective non-invasive detection tool for HCC patients.

Local ablation therapy, encompassing radiofrequency ablation (RFA), microwave ablation, and cryoablation, has emerged as a crucial strategy for managing small hepatocellular carcinomas (HCCs), complementing liver resection and transplantation. This review delves into the clinical significance of tumor size, location, and biology in guiding treatment decisions for HCCs undergoing local ablation therapy, with a focus on tumors smaller than 3 cm. Tumor size significantly influences treatment outcomes, with larger tumors associated with poorer local tumor control due to challenges in creating sufficient ablative margins and the likelihood of microvascular invasion and peritumoral satellite nodules. Advanced ablation techniques such as centripetal or no-touch RFA using multiple electrodes, cryoablation using multiple cryoprobes, and microwave ablation offer diverse options for HCC treatment. Notably, no-touch RFA demonstrates superior local tumor control compared with conventional RFA by achieving sufficient ablative margins, making it particularly promising for hepatic dome lesions or tumors with aggressive biology. Laparoscopic RFA proves beneficial for treating anterior subphrenic HCCs, whereas artificial pleural effusion-assisted RFA is effective for controlling posterior subphrenic HCCs. However, surgical resection generally offers better survival outcomes for periportal HCCs compared with RFA. Cryoablation exhibits a lower incidence of vascular or biliary complications than RFA for HCCs adjacent to perivascular or periductal regions. Additionally, aggressive tumor biology, such as microvascular invasion, can be predicted using magnetic resonance imaging findings and serum tumor markers. Aggressive HCC subtypes frequently exhibit Liver Imaging Reporting and Data System M features on magnetic resonance imaging, aiding in prognosis. A comprehensive understanding of tumor size, location, and biology is imperative for optimizing the benefits of local ablation therapy in managing HCCs.

Microvascular invasion is strongly associated with aggressive tumor behavior and recurrence in hepatocellular carcinoma (HCC) patients. Zinc finger protein 143(ZNF143) is a transcription factor involved in a wide variety of physiological and developmental processes. This study primarily focuses on the exact biological role and mechanism of ZNF143 in HCC migration and invasion.

The expression and prognosis of ZNF143 in HCC patients were analyzed. The levels of ZNF143, mex-3 RNA binding family member C (MEX3C) were quantified by western blot and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Cell migration ability was detected by wound- healing assay. Matrigel transwell assay was conducted to evaluate the invasion of HCC cells. The differential expression genes of ZNF143 overexpression and knockdown were screened by mRNA profiling analysis. Dual luciferase assay was performed to determine the promoter activity of MEX3C. The enrichment of ZNF143 at MEX3C promoter was determined by chromatin immunoprecipitation (ChIP).

ZNF143 is overexpressed in HCC tissues and that its overexpression is correlated with poorer prognosis, especially in HCC patients with higher tumor grades and microvascular invasion. Gain- and loss-of function experiments showed that ZNF143 promotes migration and invasion in HCC cells. mRNA profiling showed that ZNF143 significantly upregulates MEX3C. ZNF143 was positively correlated with MEX3C expression in HCC tissue. ZNF143 activates MEX3C transcription by directly binding to its promoter. MEX3C knockdown inhibited migration and invasion induced by ZNF143 overexpression in HCC cells.

ZNF143 promotes HCC cell migration and invasion by binding to MEX3C promoter and activating its expression.

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 this study was to analyze the clinical and pathological factors associated with recurrence, aggressive recurrence, and survival after LR.

We performed a 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.

A total of 218 patients were included (30% BCLC 0 and 70% BCLC A), median (IQR) tumor size of 28 (19-42 mm). 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 liver transplantation, 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 rate was 81%. The presence of mVI/S was the only independent predictor of recurrence (hazard ratio [HR] 1.83, 95% CI 1.28-2.61, p <0.001), aggressive recurrence (HR 3.31, 95% CI 1.74-6.29, p <0.001) and mortality (HR 2.23, 95% CI 1.27-3.91, p = 0.005). The macrotrabecular-massive subtype was significantly associated with a higher prevalence of mVI/S, Edmonson Steiner grade III-IV, AFP values and vessels that encapsulate tumor clusters, but not with recurrence, aggressive recurrence, or overall survival.

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.

Assessment of recurrence risk after liver resection is crucial in patients with hepatocellular carcinoma. Patients with a high risk of recurrence are candidates for liver transplantation as an ab initio indication or for the potential use of adjuvant therapy. Aggressive recurrences, defined as those exceeding the Milan criteria at first recurrence, have a significant impact on overall survival (OS). Fifty-eight percent of patients experienced hepatocellular carcinoma recurrence, with a prevalence of aggressive recurrence at the first occurrence standing at 35%. After a median follow-up of 49 (23-85) months, 61 (28%) patients died, and 32 (15%) underwent liver transplantation, resulting in a 5-year OS rate of 81%. Microvascular invasion and/or satellitosis was present in 39% of our cohort and was the only independent predictor of recurrence, aggressive recurrence, and OS on multivariate analysis. This is important as it could be used to guide therapeutic management.

To examine the relationship between microvascular invasion (MVI) grading severity and long-term outcomes in early-stage hepatocellular carcinoma (HCC) patients undergoing laparoscopic liver resection (LLR).

Patients who had LLR for early-stage HCC were enrolled. According to the grading severity of MVI, patients were classified into M0, M1 and M2. Recurrence-free survival (RFS) and overall survival (OS) among the groups were compared. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors of OS and RFS.

Among 233 patients, MVI grading as M0, M1, and M2 accounts for 122 (52.4 ​%), 84 (36 ​%), and 27 (11.6 ​%) patients, respectively. The median OS and RFS in patients with M0, M1, and M2 were 84.9, 40.1, and 25.2 months; and 76.9, 27.0, and 18.8 months, respectively. Multivariable analyses identified both M1 and M2 to be independent risk factors for OS and RFS.

Grading severity of MVI was independently associated with RFS and OS after LLR for early-stage HCC. Patients with MVI, especially those with M2, should receive stringent recurrence surveillance and active adjuvant therapy.

Liver transplantation (LT) is an effective method for curing hepatocellular carcinoma (HCC). However postoperative tumor recurrence can lead to higher mortality rates. To select suitable candidates for LT, the Milan Criteria (MC) were first proposed based on tumor morphological characteristics. For those patients who meet the MC, the MC can effectively reduce the postoperative tumor recurrence rate and improve the prognosis of patients undergoing LT. It has always been internationally recognized as the gold standard for selecting candidates for LT, marking a milestone in the history of LT for HCC. However, its strict conditions exclude some HCC patients who could benefit from LT. Therefore, comprehension consideration criteria, including serum biomarkers, tumor histology, and other factor, have been continuously proposed in addition to tumor morphology. This article summaries the prediction model for HCC recurrence after LT from five aspects: tumor morphology, serum markers, histopathology, cellular inflammatory factors and downstaging treatment before transplantation. The aim is to assist clinicians in accurately assessing HCC status, selecting appropriate liver transplant candidates, maximize graft and patients' survival, and optimizing the utilization of social health resources.

Background/Objectives: Cholangiocarcinoma (CCA) and hepatocellular carcinoma (HCC) represent major primary liver cancers, affecting one of the most vital organs in the human body. T regulatory (Treg) cells play an important role in liver cancers through the immunosuppression of antitumor immune responses. The current study focuses on the characterization of circulating natural killer (NK) cells and T cell subsets, including Treg cells, in CCA and HCC patients, before and after surgical tumor resection, in order to understand the effect of tumor resection on the homeostasis of peripheral blood NK cells and T cells. Methods: Whole blood assays were performed to monitor immune alterations and the functional competence of circulating lymphocytes in a group of ten healthy individuals, eight CCA patients, and twenty HCC patients, before and one month after the surgical procedure, using flow cytometry, cell sorting, and qRT-PCR. Results: Before tumor resection, both HCC and CCA patients display increased percentages of CD8+ Treg cells and decreased frequencies of circulating CD4+ Treg cells. Notwithstanding, no functional impairment was detected on circulating CD4+ Treg cells, neither in CCA nor in HCC patients. Interestingly, the frequency of peripheral CD4+ Treg cells increased from 0.55% ± 0.49 and 0.71% ± 0.54 (in CCA and HCC, respectively) at T0 to 0.99% ± 0.91 and 1.17% ± 0.33 (in CCA and HCC, respectively) at T1, following tumor resection. Conclusions: Our results suggest mechanisms of immune modulation induced by tumor resection.

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.

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.

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).

This study unveils the potential of exosome-derived PRPSAP1 in plasma as a promising indicator for predicting MVI status preoperatively.

Hepatocellular carcinoma (HCC) recurrence is highly correlated with increased mortality. Microvascular invasion (MVI) is indicative of aggressive tumor biology in HCC.

To construct an artificial neural network (ANN) capable of accurately predicting MVI presence in HCC using magnetic resonance imaging.

This study included 255 patients with HCC with tumors < 3 cm. Radiologists annotated the tumors on the T1-weighted plain MR images. Subsequently, a three-layer ANN was constructed using image features as inputs to predict MVI status in patients with HCC. Postoperative pathological examination is considered the gold standard for determining MVI. Receiver operating characteristic analysis was used to evaluate the effectiveness of the algorithm.

Using the bagging strategy to vote for 50 classifier classification results, a prediction model yielded an area under the curve (AUC) of 0.79. Moreover, correlation analysis revealed that alpha-fetoprotein values and tumor volume were not significantly correlated with the occurrence of MVI, whereas tumor sphericity was significantly correlated with MVI (P < 0.01).

Analysis of variable correlations regarding MVI in tumors with diameters < 3 cm should prioritize tumor sphericity. The ANN model demonstrated strong predictive MVI for patients with HCC (AUC = 0.79).

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.

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).

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.

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.

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.

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).

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).

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.

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.

At present, liver transplantation (LT) is one of the best treatments for hepatocellular carcinoma (HCC). Accurately predicting the survival status after LT can significantly improve the survival rate after LT, and ensure the best way to make rational use of liver organs.

To develop a model for predicting prognosis after LT in patients with HCC.

Clinical data and follow-up information of 160 patients with HCC who underwent LT were collected and evaluated. The expression levels of alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin, Golgi protein 73, cytokeratin-18 epitopes M30 and M65 were measured using a fully automated chemiluminescence analyzer. The best cutoff value of biomarkers was determined using the Youden index. Cox regression analysis was used to identify the independent risk factors. A forest model was constructed using the random forest method. We evaluated the accuracy of the nomogram using the area under the curve, using the calibration curve to assess consistency. A decision curve analysis (DCA) was used to evaluate the clinical utility of the nomograms.

The total tumor diameter (TTD), vascular invasion (VI), AFP, and cytokeratin-18 epitopes M30 (CK18-M30) were identified as important risk factors for outcome after LT. The nomogram had a higher predictive accuracy than the Milan, University of California, San Francisco, and Hangzhou criteria. The calibration curve analyses indicated a good fit. The survival and recurrence-free survival (RFS) of high-risk groups were significantly lower than those of low- and middle-risk groups (P < 0.001). The DCA shows that the model has better clinical practicability.

The study developed a predictive nomogram based on TTD, VI, AFP, and CK18-M30 that could accurately predict overall survival and RFS after LT. It can screen for patients with better postoperative prognosis, and improve long-term survival for LT patients.

Microvascular invasion (MVI) is a critical factor in predicting the recurrence and prognosis of hepatocellular carcinoma (HCC) after liver transplantation (LT). However, there is a lack of reliable preoperative predictors for MVI. The purpose of this study is to evaluate the potential of an 18F-FDG PET/CT-based nomogram in predicting MVI before LT for HCC.

83 HCC patients who obtained 18F-FDG PET/CT before LT were included in this retrospective research. To determine the parameters connected to MVI and to create a nomogram for MVI prediction, respectively, Logistic and Cox regression models were applied. Analyses of the calibration curve and receiver operating characteristic (ROC) curves were used to assess the model's capability to differentiate between clinical factors and metabolic data from PET/CT images.

Among the 83 patients analyzed, 41% were diagnosed with histologic MVI. Multivariate logistic regression analysis revealed that Child-Pugh stage, alpha-fetoprotein, number of tumors, CT Dmax, and Tumor-to-normal liver uptake ratio (TLR) were significant predictors of MVI. A nomogram was constructed using these predictors, which demonstrated strong calibration with a close agreement between predicted and actual MVI probabilities. The nomogram also showed excellent differentiation with an AUC of 0.965 (95% CI 0.925-1.000).

The nomogram based on 18F-FDG PET/CT metabolic characteristics is a reliable preoperative imaging biomarker for predicting MVI in HCC patients before undergoing LT. It has demonstrated excellent efficacy and high clinical applicability.

The objective of this study is to evaluate and compare the survival benefit and safety of surgery following conversion therapy versus surgery alone in patients diagnosed with surgically resectable hepatocellular carcinoma (HCC) at China Liver Cancer Staging (CNLC) IIb/IIIa stage.

A total of 95 patients diagnosed with surgically resectable CNLC IIb/IIIa HCC were retrospectively enrolled in our study from November 2018 to December 2022. Among them, 30 patients underwent conversion therapy followed by hepatectomy, while the remaining 65 received surgery alone. The primary endpoint was recurrence-free survival (RFS). Propensity score matching was employed to minimize bias in the retrospective analysis.

Compared to the surgery alone group, the conversion therapy group demonstrated a significantly prolonged median RFS (17.1 vs. 7.0 months; P =0.014), a reduced incidence of microvascular invasion (MVI, 23.3 vs. 81.5%; P <0.001), and a comparable rate of achieving Textbook Outcome in Liver Surgery (TOLS, 83.3 vs. 76.9%; P =0.476). Multivariate analysis indicated that conversion therapy was independently associated with improved RFS after hepatectomy (HR=0.511, P =0.027). The same conclusions were obtained after propensity score matching.

The findings of our study offer preliminary evidence that preoperative conversion therapy significantly prolongs RFS in patients with surgically resectable HCC at CNLC IIb/IIIa stage. Furthermore, combining conversion therapy and hepatectomy represents a relatively safe treatment strategy.

As an indicator of tumor invasiveness, microvascular invasion (MVI) is a crucial risk factor for postoperative relapse, metastasis, and unfavorable prognosis in hepatocellular carcinoma (HCC). Nevertheless, the genetic mechanisms underlying MVI, particularly for Chinese patients, remain mostly uncharted.

We applied deep targeted sequencing on 66 Chinese HCC samples. Focusing on the telomerase reverse transcriptase (TERT) promoter (TERTp) and TP53 co-mutation (TERTp+/TP53+) group, gene set enrichment analysis (GSEA) was used to explore the potential molecular mechanisms of the TERTp+/TP53+ group on tumor progression and metastasis. Additionally, we evaluated the tumor immune microenvironment of the TERTp+/TP53+ group in HCC using multiplex immunofluorescence (mIF) staining.

Among the 66 HCC samples, the mutated genes that mostly appeared were TERT, TP53, and CTNNB1. Of note, we found 10 cases with TERTp+/TP53+, of which nine were MVI-positive and one was MVI-negative, and there was a co-occurrence of TERTp and TP53 (p < 0.05). Survival analysis demonstrated that patients with the TERTp+/TP53+ group had lower the disease-free survival (DFS) (p = 0.028). GSEA results indicated that telomere organization, telomere maintenance, DNA replication, positive regulation of cell cycle, and negative regulation of immune response were significantly enriched in the TERTp+/TP53+ group (all adjusted p-values (p.adj) < 0.05). mIF revealed that the TERTp+/TP53+ group decreased CD8+ T cells infiltration (p = 0.25) and enhanced PDL1 expression (p = 0.55).

TERTp+/TP53+ was significantly enriched in MVI-positive patients, leading to poor prognosis for HCC patients by promoting proliferation of HCC cell and inhibiting infiltration of immune cell surrounding HCC. TERTp+/TP53+ can be utilized as a potential indicator for predicting MVI-positive patients and poor prognosis, laying a preliminary foundation for further exploration of co-mutation in HCC with MVI and clinical treatment.

Although the elderly constitute more than a third of hepatocellular carcinoma (HCC) patients, they have not been adequately represented in treatment and prognosis studies. Thus, there is not enough evidence to guide the treatment of such patients. The objective of this study is to identify the prognostic factors of older patients with HCC and to construct a new prognostic model for predicting their overall survival (OS).

2,721 HCC patients aged ≥ 65 were extracted from the public database-Surveillance, Epidemiology, and End Results (SEER) and randomly divided into a training set and an internal validation set with a ratio of 7:3. 101 patients diagnosed from 2008 to 2017 in the First Affiliated Hospital of Zhejiang University School of Medicine were identified as the external validation set. Univariate cox regression analyses and multivariate cox regression analyses were adopted to identify these independent prognostic factors. A predictive nomogram-based risk stratification model was proposed and evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, and a decision curve analysis (DCA).

These attributes including age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, alpha-fetoprotein level, fibrosis score, bone metastasis, lung metastasis, and grade were the independent prognostic factors for older patients with HCC while predicting survival duration. We found that the nomogram provided a good assessment of OS at 1, 3, and 5 years in older patients with HCC (1-year OS: (training set: AUC = 0.823 (95%CI 0.803-0.845); internal validation set: AUC = 0.847 (95%CI 0.818-0.876); external validation set: AUC = 0.732 (95%CI 0.521-0.943)); 3-year OS: (training set: AUC = 0.813 (95%CI 0.790-0.837); internal validation set: AUC = 0.844 (95%CI 0.812-0.876); external validation set: AUC = 0.780 (95%CI 0.674-0.887)); 5-year OS: (training set: AUC = 0.839 (95%CI 0.806-0.872); internal validation set: AUC = 0.800 (95%CI 0.751-0.849); external validation set: AUC = 0.821 (95%CI 0.727-0.914)). The calibration curves showed that the nomogram was with strong calibration. The DCA indicated that the nomogram can be used as an effective tool in clinical practice. The risk stratification of all subgroups was statistically significant (p < 0.05). In the stratification analysis of surgery, larger resection (LR) achieved a better survival curve than local destruction (LD), but a worse one than segmental resection (SR) and liver transplantation (LT) (p < 0.0001). With the consideration of the friendship to clinicians, we further developed an online interface (OHCCPredictor) for such a predictive function ( https://juntaotan.shinyapps.io/dynnomapp_hcc/ ). With such an easily obtained online tool, clinicians will be provided helpful assistance in formulating personalized therapy to assess the prognosis of older patients with HCC.

Age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, AFP level, fibrosis score, bone metastasis, lung metastasis, and grade were independent prognostic factors for elderly patients with HCC. The constructed nomogram model based on the above factors could accurately predict the prognosis of such patients. Besides, the developed online web interface of the predictive model provide easily obtained access for clinicians.

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).

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.

MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D1 (0.680 ± 0.100 × 10-3 vs 0.572 ± 0.148 × 10-3 mm2/s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10-3 vs 1.471 ± 0.322 × 10-3 mm2/s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D0 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 D1, ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D1 (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.

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.

Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis.

We collected H&E-stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep-learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve.

We successfully developed a MVI artificial intelligence diagnostic model (MVI-AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI-AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI.

We developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.

Hepatocellular carcinoma (HCC), particularly when accompanied by microvascular invasion (MVI), has a markedly high risk of recurrence after liver resection. Adjuvant immunotherapy is considered a promising avenue. This multicenter, open-label, randomized, controlled, phase 2 trial was conducted at six hospitals in China to assess the efficacy and safety of adjuvant sintilimab, a programmed cell death protein 1 inhibitor, in these patients. Eligible patients with HCC with MVI were randomized (1:1) into the sintilimab or active surveillance group. The sintilimab group received intravenous injections every 3 weeks for a total of eight cycles. The primary endpoint was recurrence-free survival (RFS) in the intention-to-treat population. Key secondary endpoints included overall survival (OS) and safety. From September 1, 2020, to April 23, 2022, a total of 198 eligible patients were randomly allocated to receive adjuvant sintilimab (n = 99) or undergo active surveillance (n = 99). After a median follow-up of 23.3 months, the trial met the prespecified endpoints. Sintilimab significantly prolonged RFS compared to active surveillance (median RFS, 27.7 versus 15.5 months; hazard ratio 0.534, 95% confidence interval 0.360-0.792; P = 0.002). Further follow-up is needed to confirm the difference in OS. In the sintilimab group, 12.4% of patients experienced grade 3 or 4 treatment-related adverse events, the most common of which were elevated alanine aminotransferase levels (5.2%) and anemia (4.1%). These findings support the potential of immune checkpoint inhibitors as effective adjuvant therapy for these high-risk patients. Chinese Clinical Trial Registry identifier: ChiCTR2000037655 .

In patients with hepatocellular carcinoma (HCC), accurately predicting the preoperative microvascular invasion (MVI) status is crucial for improving survival rates. This study proposes a multi-modal domain-adaptive fusion model based on deep learning methods to predict the preoperative MVI status in HCC.

From January 2008 to May 2022, we collected 163 cases of HCC from our institution and 42 cases from another medical facility, with each case including Computed Tomography (CT) images from the pre-contrast phase (PCP), arterial phase (AP), and portal venous phase (PVP). We divided our institution's dataset (n=163) into training (n=119) and test sets (n=44) in an approximate 7:3 ratio. Additionally, we included cases from another institution (n=42) as an external validation set (test1 set). We constructed three single-modality models, a simple concatenated multi-modal model, two current state-of-the-art image fusion model and a multi-modal domain-adaptive fusion model (M-DAFM) based on deep learning methods. We evaluated and analyzed the performance of these constructed models in predicting preoperative MVI using the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI) methods.

In comparison with all models, M-DAFM achieved the highest AUC values across the three datasets (0.8013 for the training set, 0.7839 for the test set, and 0.7454 for the test1 set). Notably, in the test set, M-DAFM's Decision Curve Analysis (DCA) curves consistently demonstrated favorable or optimal net benefits within the 0-0.65 threshold probability range. Additionally, the Net Reclassification Improvement (NRI) values between M-DAFM and the three single-modal models, as well as the simple concatenation model, were all greater than 0 (all p < 0.05). Similarly, the NRI values between M-DAFM and the two current state-of-the-art image fusion models were also greater than 0. These findings collectively indicate that M-DAFM effectively integrates valuable information from multi-phase CT images, thereby enhancing the model's preoperative predictive performance for MVI.

The M-DAFM proposed in this study presents an innovative approach to improve the preoperative predictive performance of MVI.

Microvascular invasion (MVI) is closely correlated with poor clinical outcomes in patients with hepatocellular carcinoma (HCC). A grading system of MVI is needed to assist in the management of HCC patient.

Multicenter data of HCC patients who underwent liver resection with curative intent was analyzed. This grading system was established by detected number and distance from tumor boundary of MVI. Survival outcomes were compared among patients in each group. This system was verified by time-receiver operating characteristic curve, time-area under the curve, calibration curve, and decision curve analyses. Cox regression analysis was performed to study the associated factors of prognosis. Logistic analysis was used to study the predictive factors of MVI.

All patients were classified into 4 groups: M0: no MVI; M1: 1~5 proximal MVIs (≤1 cm from tumor boundary); M2a: >5 proximal MVIs (≤1 cm from tumor boundary); M2b: ≥1 distal MVIs (>1 cm from tumor boundary). The recurrence-free survival (RFS), overall survival (OS), and early RFS rates among all the individual groups were significantly different. Based on the number of proximal MVI (0~5 vs >5), patients in the M2b group were further divided into two subgroups which also showed different prognosis. Multiple methods showed this grading system to be significantly better than the MVI two-tiered system in prognostic evaluation. Four multivariate models for RFS, OS, early RFS, late RFS, and a predictive model of MVI were then established and were shown to satisfactorily evaluate prognosis and have a great discriminatory power, respectively.

This MVI grading system could precisely evaluate prognosis of HCC patients after liver resection with curative intent and it could be employed in routine pathological reports. The severity of MVI from both adjacent and distant from tumor boundary should be stated. A hypothesis about two occurrence modes of distal MVI was proposed.

To compare the performances of gadoxetate disodium-enhanced MRI (EOB-MRI) and extracellular contrast agent-enhanced MRI (ECA-MRI) for predicting microvascular invasion (MVI) in HCC.

From November 2009 to December 2021, consecutive HCC patients who underwent preoperative contrast-enhanced MRI were retrospectively enrolled into either an ECA-MRI or EOB-MRI cohort. In the ECA-MRI cohort, a preoperative MVI score was constructed in the training dataset using a logistic regression model that evaluated pathological type. In a propensity score-matched testing dataset of the ECA-MRI cohort, the MVI score was validated and compared with a previously proposed EOB-MRI-based MVI score calculated in the EOB-MRI cohort. Time-to-early recurrence survival was evaluated by the Kaplan-Meier method with the log-rank test.

A total of 536 patients were included (478 men; 53 years, interquartile range, 46-62 years), 322 (60.1 %) with pathologically confirmed MVI. Based on the training dataset, independent variables associated with MVI included serum alpha-fetoprotein > 400 ng/ml (odds ratio [OR] = 2.3), infiltrative appearance (OR = 4.9), internal artery (OR = 2.5) and nodule-in-nodule architecture (OR = 2.4), which were incorporated into the ECA-MRI-based MVI score. The testing dataset AUC of the ECA-MRI score was 0.720, which was comparable to that of the EOB-MRI-based MVI score (AUC = 0.721; P =.99). Patients from either the ECA-MRI or the EOB-MRI cohort with model-predicted MVI had significantly shorter time-to-early recurrence than those without MVI (P <.001).

Based on the preoperative serum alpha-fetoprotein and three MRI features, ECA-MRI demonstrated comparable performance to EOB-MRI for predicting MVI in HCC.

To investigate the potential of radiomics analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in preoperatively predicting microvascular invasion (MVI) in patients with combined hepatocellular-cholangiocarcinoma (cHCC-CC) before surgery.

A cohort of 91 patients with histologically confirmed cHCC-CC who underwent preoperative liver DCE-MRI were enrolled and divided into a training cohort (27 MVI-positive and 37 MVI-negative) and a validation cohort (11 MVI-positive and 16 MVI-negative). Clinical characteristics and MR features of the patients were evaluated. Radiomics features were extracted from DCE-MRI, and a radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. Prediction performance of the developed radiomics signature was evaluated by utilizing the receiver operating characteristic (ROC) analysis.

Larger tumor size and higher Radscore were associated with the presence of MVI in the training cohort (p = 0.026 and < 0.001, respectively), and theses findings were also confirmed in the validation cohort (p = 0.040 and 0.001, respectively). The developed radiomics signature, composed of 4 stable radiomics features, showed high prediction performance in both the training cohort (AUC = 0.866, 95% CI 0.757-0.938, p < 0.001) and validation cohort (AUC = 0.841, 95% CI 0.650-0.952, p < 0.001).

The radiomics signature developed from DCE-MRI can be a reliable imaging biomarker to preoperatively predict MVI in cHCC-CC.