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.

Current hepatocellular carcinoma (HCC) staging systems lack comprehensive assessment of tumor biologic characteristics. This study aimed to develop and validate a tumor biology characteristics score (TBCS) based on alpha-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist II (PIVKA-II) to predict long-term oncologic outcomes after HCC resection.

In this multicenter retrospective cohort study, patients who underwent curative resection for HCC between June 2018 and December 2022 were included. TBCS (range, 2-6 points) was calculated by combining preoperative AFP (<20, 20-199, and ≥200 ng/mL) and PIVKA-II levels (<40, 40-399, and ≥400 mAU/mL). Patients were stratified into low (2 points), medium (3-4 points), and high TBCS groups (5-6 points). The primary outcomes were recurrence-free survival (RFS) and overall survival (OS).

A total of 695 patients were analyzed; the low, medium, and high TBCS groups comprised 132 (19.0%), 233 (33.5%), and 330 patients (47.5%), respectively. Notably, 5-year RFS was 30.4%, 14.7%, and 9.7%, whereas 5-year OS was 42.1%, 35.5%, and 23.5% for low, medium, and high TBCS groups, respectively (both P <.001). Multivariate analysis identified TBCS as an independent predictor of both RFS (medium TBCS: hazard ratio [HR], 1.583; 95% CI, 1.219-2.057; P =.001; high TBCS: HR, 1.895; 95% CI, 1.473-2.438; P <.001) and OS (high TBCS: HR, 1.781; 95% CI, 1.353-2.343; P <.001).

The novel TBCS combining AFP and PIVKA-II effectively stratified patients with HCC into distinct prognostic groups after curative-intent resection, independently predicting both RFS and OS. This score may help identify high-risk patients for more intense postoperative recurrence surveillance and receipt of adjuvant therapies.

Microvascular invasion (MVI) has been recognized as a risk factor for early recurrence after hepatectomy in patients with hepatocellular carcinoma (HCC). This study aimed to estimate the performance of an ultrasensitive chromosomal aneuploidy detector (UCAD) model for preoperative MVI prediction in operable HCC patients based on plasma cell-free DNA (cfDNA).

A prospective study included HCC patients who underwent surgery in 2021. Preoperative peripheral plasma samples of eligible patients were collected to extract cfDNA, which was then subject to next generation sequencing. Low-coverage whole-genome sequencing data were analyzed for chromosomal instability using different parameters, including Z-score, chromosomal instability score (CIN score), tumor fraction (TFx) and a UCAD model (UCAD = CIN score + TFx + Z-score of all chromosomes). Receiver operating characteristic (ROC) curve was used to evaluate the performance of these parameters in preoperative MVI prediction.

Finally, a total of 74 patients with HCC who undergone hepatectomy were prospectively enrolled. Chromosomal instability was evaluated by copy number alterations and oncogenes MCL1 (located at 1q), MYC (located at 8q), TERT (located at 5p), EGFR (located at 7p), and VEGFA (located at 6p) were identified in plasma cfDNA. The UCAD model was a superior parameter in predicting preoperative MVI, with an area under curve (AUC) value 0.749 with a sensitivity of 0.938 specificity of 0.466. Univariate analysis revealed that tumor size (≥ 5 cm) and UCAD (> 0.199) significantly increased the risk of MVI, which were further demonstrated by multivariate analysis, with odd ratio of 1.338 (95%CI, 1.060-1.689) and 2.028 (95%CI, 1.053-3.908) (P < 0.05).

Our cfDNA-based UCAD model has shown a promising performance for preoperative MVI prediction in operable HCC patients.

This study was registered at https://clinicaltrials.gov/ on 16 May 2022, retrospectively registered, Registration number: NCT05371873.

This study aims to develop a machine learning model that accurately diagnoses microvascular invasion (MVI) in hepatocellular carcinoma by using radiomic features from MVI-positive regions of interest (ROIs). Unlike previous studies, which do not account for the location and distribution of MVI, this research focuses on correlating preoperative imaging with postoperative pathological MVI. This study involves obtaining ex vivo 3D ultrasound images of 36 hepatic specimens from nine rabbits. These images are fused with whole-slide images to localize MVI regions precisely. The identified MVI regions are segmented into MVI-positive ROIs, with a 1:3 ratio of positive to negative ROIs. Radiomic features are extracted from each ROI, and 30 features highly associated with MVI are selected for model development. The performance of several machine learning models is evaluated using metrics such as sensitivity, specificity, accuracy, the area under the curve (AUC), and F1 score. The GBDT model achieves the best results, with an AUC of 0.91, an F1 score of 0.85, a sensitivity of 0.76, a specificity of 0.92, and an accuracy of 0.86. The high diagnostic accuracy of these models highlights the potential for future clinical application in the precise diagnosis of MVI using radiomic features from MVI-positive ROIs.

Accurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This study evaluates and compares the performance of clinicoradiologic, traditional radiomics, deep-learning radiomics, feature fusion, and decision fusion models based on multi-region MR habitat imaging using six machine-learning classifiers.

We retrospectively included 300 HCC patients. The intratumoral and peritumoral regions were segmented into distinct habitats, from which radiomics and deep-learning features were extracted using arterial phase MR images. To reduce feature dimensionality, we applied intra-class correlation coefficient (ICC) analysis, Pearson correlation coefficient (PCC) filtering, and recursive feature elimination (RFE). Based on the selected optimal features, prediction models were constructed using decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost (XGB) classifiers. Additionally, fusion models were developed utilizing both feature fusion and decision fusion strategies. The performance of these models was validated using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis.

The decision fusion model (VOI-Peri10-1) using LR and integrating clinicoradiologic, radiomics, and deep-learning features achieved the highest AUC of 0.808 (95% confidence interval [CI]: 0.807-0.912) in the test cohort, with good calibration (Hosmer-Lemeshow test, P > 0.050) and clinical net benefit.

The LR-based decision fusion model integrating clinicoradiologic, radiomics, and deep-learning features shows promise for preoperative prediction of MVI in HCC, aiding in patient outcome predictions and personalized treatment planning.

Microvascular invasion (MVI) affects the prognosis and treatment of hepatocellular carcinoma (HCC); however, its preoperative diagnosis is challenging. Analysis of computed tomography (CT) images using radiomics can detect MVI, but its effectiveness depends on the imaging conditions. We compared the efficacies of radiomics, clinical, and combined models for predicting MVI in HCC using nonstandardized scanning protocols.

This multicenter study included 533 patients who underwent hepatic resection for HCC. Patients were divided randomly into training (n = 426) and test groups (n = 107). We manually extracted 3D CT features in hepatic arterial, portal venous, and venous phases. The radiomics model was trained by machine learning. A logistic regression model was developed based on clinical information, and a fused model was created integrating clinical information and radiomics prediction score (Rad_Score). We calculated areas under the receiver operating characteristic curves (AUCs) for the radiomics, clinical, and mixed models in the test groups.

The clinical model incorporated hepatitis B virus surface antigen, tumor diameter, and log-transformed α-fetoprotein and des-gamma-carboxyprothrombin. The AUCs of the radiomics and clinical models were comparable (p = 0.76). Rad_Score was not an independent significant factor in the fused model (p = 0.40) and its addition did not improve the accuracy of the clinical model alone (p = 0.51).

A clinical model is as effective as a CT radiomics model for predicting MVI status in patients with HCC based on real-world scanning data, and integration of both models does not improve the predictive performance compared with a clinical model alone.

The clinical management of hepatocellular carcinoma (HCC) is strongly influenced by several prognostic factors, mainly tumor stage, patient's health, liver function and specific characteristics of each intervention. The interplay between these factors should be carefully evaluated by a multidisciplinary tumor board. To support this, the novel "multiparametric therapeutic hierarchy" (MTH) concept has been recently proposed. This review will present the main features of available surgical treatments for HCC (liver transplantation, liver resection, ablation). Strengths and weaknesses are reported in the light of clinical decision making and of treatment allocation, with a special focus on the collocation of each treatment in the MTH framework and on how MTH may be useful in supporting clinical decision. Sequential treatments and their role to allow further surgical treatments will also be analyzed.

This study aimed to develop a multiregional radiomics-based model using multisequence MRI to predict microvascular invasion in hepatocellular carcinoma.

We enrolled 141 patients with hepatocellular carcinoma, including 61 with microvascular invasion, who were diagnosed between March 2017 and July 2022. Clinical data were compared using the Wilcoxon rank-sum test or χ2 test. Patients were randomly divided into training (n=112, 80%) and test (n=29, 20%) data sets. Four MRI sequences-including T2-weighted imaging, T2-weighted imaging with fat suppression, arterial phase-contrast enhancement, and portal venous phase contrast enhancement-were used to build the radiomics model. The tumor volumes of interest were manually delineated, and the expand-5 mm and expand-10 mm volumes of interest were automatically generated. A total of 1409 radiomic features were extracted from each volume of interest. Feature selection was performed using the least absolute shrinkage and selection operator and Spearman correlation analysis. Three logistic regression models (Tumor, Tumor-Expand5, and Tumor-Expand10) were established based on the radiomic features. Model performance was assessed using receiver operating characteristic analysis and Delong's test.

Maximum tumor diameter, hepatitis B virus DNA, and aspartate aminotransferase levels were significantly different between the groups. The Tumor-Expand5mm model exhibited the best performance among the 3 models, with areas under the curve of 0.90 and 0.84 in the training and test data sets.

The Tumor-Expand5 model based on multisequence MRI shows great potential for predicting microvascular invasion in patients with hepatocellular carcinoma, and may further contribute to personal clinical decision-making.

For many cancers, biomarkers have served as an important tool across the cancer care continuum from risk stratification and early detection to diagnosis and treatment. Alpha-fetoprotein (AFP) remains one of the few validated biomarkers for patients with HCC. Although AFP has shown potential for each of these steps, its performance, when used alone, has often been suboptimal. There continue to be discordant recommendations about AFP's value when combined with ultrasound for surveillance, as well as its role in diagnostic algorithms. Conversely, high AFP levels are associated with aggressive tumor biology and survival, so it remains a key factor for the selection of candidates for liver transplant. There have been immense efforts to identify and validate additional biomarkers for each of these steps in the HCC care continuum. Indeed, biomarker panels have shown promising data for HCC risk stratification and surveillance among patients with cirrhosis, as well as prognostication and detection of minimal residual disease in patients undergoing HCC treatment. Several large prospective studies are currently ongoing to evaluate the role of these emerging biomarkers in clinical practice.

Living donor liver transplantation (LDLT) offers timely curative treatment for unresectable hepatocellular carcinoma (HCC). This study aims to validate and compare previous prediction models for HCC outcomes in 488 LDLT recipients.

For 488 patients who underwent LDLT for HCC, pretransplant imaging studies assessed by modified RECSIT criteria, tumor markers such as alpha feto-protein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA II), and explant pathology were recruited. C-index of models for the HCC outcomes was compared, followed by further investigation for the predictive performances of the best model.

We found MoRAL (11√PIVKA-II + 2√AFP) demonstrated a higher C-index for HCC recurrence than other models that included radiologically viable tumor number and/or size (MoRAL: 0.709, Milan: 0.537, UCSF: 0.575, Up-to-7: 0.572, French AFP: 0.634, Pre-MORAL: 0.637, HALT-HCC: 0.626, Metroticket2.0: 0.629) and also had the highest C-index for HCC-specific deaths (0.706). Five-year HCC recurrence was well stratified upon dividing the patients into three groups by MoRAL cutoffs (11.9% for MoRAL < 100, 29.6% for MoRAL 100-200, and 48.6% for MoRAL > 200, p < 0.001). However, patients with major vessel invasion or portal vein tumor thrombus showed similarly high HCC recurrence regardless of this grouping (p = 0.612).

The MoRAL, based on tumor markers, showed the best predictive performance for HCC recurrence and HCC-specific death among the validated models, except in cases with major vessel invasion or portal vein tumor thrombus.

Hepatocellular carcinoma (HCC) represents the second leading cause of cancer deaths worldwide. Six-month imaging along with alpha-fetoprotein (AFP) serum levels detection are the current gold standard to exclude HCC. Protein induced by vitamin K absence (PIVKA-II) has been proposed as a potential screening biomarker for HCC. This study was designed to evaluate the role of PIVKA-II as diagnostic HCC marker, and the correlation between PIVKA-II levels and HCC stage.

PIVKA-II levels were assessed on serum samples of Italian patients. The study population included 80 patients with HCC, 111 with liver cirrhosis (LC), and 111 with chronic hepatitis C (CHC).

PIVKA-II serum levels progressively increase from patients with CHC to patients with HCC. In the HCC group, PIVKA-II values are higher in the more advanced stages of the disease, assessed by the Barcelona Clinic Liver Cancer (BCLC) staging system (BCLC-B vs. BCLC-A vs. BCLC-0). Youden's index analysis identified a value >37 mAU/mL as the optimal threshold for the best combination of sensitivity and specificity (80% and 76%, respectively) and, at the best cut-off of 5.2 ng/mL, AFP yielded 53% specificity and 78% sensitivity. The combination of PIVKA-II and AFP reached positive and negative predictive values of 73.9% and 94.2%, respectively.

PIVKA-II levels are increased in the HCC patients, compared to control groups. The increase is more evident in patients with advanced HCC. The diagnostic performance of PIVKA-II seems more sensitive than AFP while the combination of PIVKA-II and AFP resulted in the best diagnostic accuracy, reaching 73.9% positive predictive value and 94.2% negative predictive value, thus improving the diagnostic capability of the single marker.

Recent advancements in combination therapy for unresectable hepatocellular carcinoma (uHCC) have shown promise, but reliable serological prognostic indicators are currently lacking for patients undergoing triple combination therapy of stereotactic body radiation therapy (SBRT), immunotherapy, and targeted therapy. We aimed to investigate the prognostic significance of early alpha fetoprotein (AFP) and des-gamma-carboxy prothrombin (DCP) responses in these patients.

This retrospective research included 115 uHCC patients treated with SBRT in combination with immunotherapy and targeted therapy (triple therapy) at our institution from April 2021 to December 2022. Participants were categorized into high AFP and high DCP cohorts based on baseline levels. AFP and DCP responses were defined as decreases from baseline of over 50% and 70%, respectively, according to ROC curve analysis. Differences in overall survival (OS), progression-free survival (PFS), and objective response rate (ORR) were assessed between the tumor biomarker response and non-response groups.

Multivariate analysis indicated that AFP or DCP response at 6-8 weeks post-therapy significantly influenced ORR (high AFP cohort: odds ratio [OR] 5.50, 95% CI 2.04-14.83, p=0.001; high DCP cohort: OR 7.99, 95%CI 2.82-22.60, p<0.001). The median PFS was notably longer in tumor biomarker response groups (high AFP cohort: 13.7 vs 6.2 months, hazard ratio [HR] 0.36, 95% CI 0.20-0.62, p<0.001; high DCP cohort: 15.6 vs 9.3 months, HR 0.44, 95% CI 0.26-0.74, p=0.002). AFP or DCP response was associated with prolonged OS (high AFP cohort: not reached vs. 21.9 months, HR 0.47, 95% CI 0.22-0.99, p=0.047; high DCP cohort: not reached vs. 20.6 months, HR 0.35, 95% CI 0.14-0.86, p=0.022).

AFP or DCP response at 6-8 weeks post-therapy predicts better oncological outcomes in patients with uHCC treated with triple therapy.

The value of serum biomarkers, particularly alpha-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II), gains increasing attention in prognostic evaluation and recurrence monitoring for patients with hepatocellular carcinoma (HCC). This study investigated the implications of serological incomplete conversion (SIC) of these 2 biomarkers as prognostic indicators for long-term outcomes after HCC resection.

A multicenter observational study was conducted on a cohort of HCC patients presenting with AFP (>20 ng/mL) or PIVKA-II (>40 mAU/mL) positivity who underwent curative-intent resection. Based on their postoperative AFP and PIVKA-II levels at first postoperative follow-up (4~8 weeks after surgery), these patients were stratified into the serological incomplete conversion (SIC) and serological complete conversion (SCC) groups. The study endpoints were recurrence and overall survival (OS).

Among 1755 patients, 379 and 1376 were categorized as having SIC and SCC, respectively. The SIC group exhibited 1- and 5-year OS rates of 67.5% and 26.3%, with the corresponding recurrence rates of 53.2% and 79.0%, respectively; while the SCC group displayed 1- and 5-year OS rates of 95.8% and 62.5%, with the corresponding recurrence rates of 16.8% and 48.8%, respectively (both P < .001). Multivariate Cox regression analysis demonstrated that postoperative SIC was an independent risk factor for both increased recurrence (HR: 2.40, 95% CI, 2.04-2.81, P < .001) and decreased OS (HR: 2.69, 95% CI, 2.24-3.24, P < .001).

The results emphasize that postoperative incomplete conversion of either AFP or PIVKA-II is a significant prognostic marker, indicating a higher risk for adverse oncologic outcomes following HCC resection. This revelation has crucial implications for refining postoperative adjuvant therapy and surveillance strategies for HCC patients.

Hepatocellular Carcinoma (HCC) is a malignant tumor with high morbidity and mortality worldwide, which represents a serious threat to human life, health and quality of life. Blood-based detection is essential for HCC screening, early diagnosis, prognosis evaluation, and surveillance. Current non-invasive detection strategy including serum alpha-fetoprotein (AFP), ultrasound, computerized tomography, and magnetic resonance imaging. The limited specificity of an AFP and the dependence on operator experience and diagnostic personnel for ultrasound have constrained their utility in early HCC diagnosis. In recent years, with the development of various detection technologies, there has been an increasing focus on exploring blood-based detection markers for HCC. The types of markers include protein markers, DNA mutation, DNA epigenetic modification, mRNA, miRNA, and so on. However, numerous methodological and biological factors limit the clinical sensitivity and generalization performance of these new biomarkers. In this review, we describe the state-of-the-art technologies for cfDNA analysis, and discuss outstanding biological and technical challenges that, if addressed, would substantially improve HCC diagnostics and patient care.

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.

To develop and validate a preoperative prediction model based on multimodal ultrasound and biochemical indicator for identifying microvascular invasion (MVI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm.

From May 2022 to November 2023, a total of 318 patients with pathologically confirmed single HCC ≤ 5 cm from three institutions were enrolled. All of them underwent preoperative biochemical, conventional ultrasound (US), and contrast-enhanced ultrasound (CEUS) (Sonazoid, 0.6 mL, bolus injection) examinations. Univariate and multivariate logistic regression analyses on clinical information, biochemical indicator, and US imaging features were performed in the training set to seek independent predictors for MVI-positive. The models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis in both validation and test sets. Subgroup analyses in patients with different liver background and tumor sizes were conducted to further investigate the model's performance.

Logistic regression analyses showed that obscure tumor boundary in B-mode US, intra-tumoral artery in pulsed-wave Doppler US, complete Kupffer-phase agent clearance in Sonazoid-CEUS, and biomedical indicator PIVKA-II were independently correlated with MVI-positive. The combined model comprising all predictors showed the highest AUC, which were 0.937 and 0.893 in the validation and test sets. Good calibration and prominent net benefit were achieved in both sets. No significant difference was found in subgroup analyses.

The combination of biochemical indicator, conventional US, and Sonazoid-CEUS features could help preoperative MVI prediction in patients with a single HCC ≤ 5 cm.

Investigation of imaging features in conventional US, Sonazoid-CEUS, and biochemical indicators showed a significant relation with MVI-positivity in patients with a single HCC ≤ 5 cm, allowing the construction of a model for preoperative prediction of MVI status to help treatment decision making.

MVI status is important for patients with a single HCC ≤ 5 cm. The model based on conventional US, Sonazoid-CEUS and PIVKA-II performs best for MVI prediction. The combined model has potential for preoperative prediction of MVI status.

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.

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.

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.

Retrospective.

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.

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

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.

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.

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.

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

4 TECHNICAL EFFICACY: Stage 2.

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.

An ultrasensitive immunosensor of Cys/Au@TiO2 based on disposable screen-printed electrodes (SPE) for PIVKA-II detection for hepatocellular carcinoma (HCC) diagnosis was developed by utilizing Cystine (Cys) and nanocomposite Au@TiO2. Firstly, HAuCl4 underwent a reduction reaction with NaBH4, then Au nanoparticles were coated onto TiO2 nanoparticles. Followed, Cys/Au@TiO2 was formed through self-assembly of cysteine to allow the monoclonal antibody of abnormal thrombospondin to bound to the amino group on the surface of the composite by covalent bonding. The mechanism is to determine the changes in the current of the sensor caused by the specific binding of the abnormal prothrombin monoclonal antibody adsorbed by the complex with its antigen. The Cys/Au@TiO2 immunosensor was fully characterized by various analytical approaches and it showed a wide linear testing range of 1-10000 pg mL-1 (R2 = 0.991) and the limit of detection down to 0.77 pg ml-1, with highly sensitivity and specificity. The results showed that the developed immunosensor platform can effectively detect trace amounts of PIVKA-II protein and has potent clinical application for HCC diagnosis.

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.

We retrospectively studied 129 patients with very early or early-stage HCC ≤ 3 cm who had undergone 18F-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.

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.

Our scoring system can accurately predict MVI in small HCC ≤ 3 cm, and could contribute to establishing an appropriate treatment strategy.

To investigate whether protein induced by vitamin K antagonist-II (PIVKA-II) combined with alpha-fetoprotein (AFP) can improve the diagnostic and differential diagnostic accuracy of childhood hepatic tumors.

A multi-center prospective observational study was performed at nine regional institutions around China. Children with hepatic mass (Group T) were divided into hepatoblastoma group (Group THB) and hemangioendothelioma group (Group THE), children with extrahepatic abdominal mass (Group C). Peripheral blood was collected from each patient prior to surgery or chemotherapy. The area under the curve (AUROC) was used to evaluate the diagnostic efficiency of PIVKA-II and the combined tumor markers with AFP.

The mean levels of PIVKA-II and AFP were both significantly higher in Group T than Group C (p = 0.001, p < 0.001), in Group THB than Group THE (p = 0.018, p = 0.013) and in advanced HB than non-advanced HB (p = 0.001, p = 0.021). For the diagnosis of childhood hepatic tumors, AUROC of PIVKA-II (cut-off value 32.6 mAU/mL) and AFP (cut-off value 120 ng/mL) was 0.867 and 0.857. The differential diagnostic value of PIVKA-II and AFP in hepatoblastoma from hemangioendothelioma was further assessed, AUROC of PIVKA-II (cut-off value 47.1mAU/mL) and AFP (cut-off value 560 ng/mL) was 0.876 and 0.743. The combined markers showed higher AUROC (0.891, 0.895 respectively) than PIVKA-II or AFP alone.

The serum level of PIVKA-II was significantly higher in children with hepatic tumors, especially those with malignant tumors. The combination of PIVKA-II with AFP further increased the diagnostic performance.

Clinical Trials, NCT03645655. Registered 20 August 2018, https://www.

gov/ct2/show/NCT03645655 .

As a hepatotropic virus, hepatitis B virus (HBV) can establish a persistent chronic infection in the liver, termed, chronic hepatitis B (CHB), which causes a series of liver-related complications, including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). HCC with HBV infection has a significantly increased morbidity and mortality, whereas it could be preventable. The current goal of antiviral therapy for HBV infection is to decrease CHB-related morbidity and mortality, and achieve sustained suppression of virus replication, which is known as a functional or immunological cure. The natural history of chronic HBV infection includes four immune phases: the immune-tolerant phase, immune-active phase, inactive phase, and reactivation phase. However, many CHB patients do not fit into any of these defined phases and are regarded as indeterminate. A large proportion of indeterminate patients are only treated with dynamic monitoring rather than recommended antiviral therapy, mainly due to the lack of definite guidelines. However, many of these patients may gradually have significant liver histopathological changes during disease progression. Recent studies have focused on the prevalence, progression, and carcinogenicity of indeterminate CHB, and more attention has been given to the prevention, detection, and treatment for these patients. Herein, we discuss the latest understanding of the epidemiology, clinical characteristics, and therapeutic strategies of indeterminate CHB, to provide avenues for the management of these patients.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Serum biomarkers play an important role in the early diagnosis and prognosis of HCC. Because a certain percentage of HCC patients are negative for alpha-fetoprotein (AFP), the diagnosis of AFP-negative HCC is essential to improve the detection rate of HCC.

To establish an effective model for diagnosing AFP-negative HCC based on serum tumour biomarkers.

A total of 180 HCC patients were enrolled in this study. The expression levels of GP73, des-γ-carboxyprothrombin (DCP), CK18-M65, and CK18-M30 were detected by a fully automated chemiluminescence analyser. The variables were selected by logistic regression analysis. Several models were constructed using stepwise backward logistic regression. The performance of the models was compared using the C statistic, integrated discrimination improvement, net reclassification improvement, and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).

The results showed that the expression levels of GP73, DCP, CK18-M65, and CK18-M30 were significantly greater in AFP-negative HCC patients than in healthy controls (P < 0.001). Multivariate logistic regression analysis revealed that GP73, DCP, and CK18-M65 were independent factors for diagnosing AFP-negative HCC. By comparing the diagnostic performance of multiple models, we included GP73 and CK18-M65 as the model variables, and the model had good discrimination ability (area under the curve = 0.946) and good goodness of fit. The DCA curves indicated the good clinical utility of the nomogram.

Our study identified GP73 and CK18-M65 as serum biomarkers with certain application value in the diagnosis of AFP-negative HCC. The diagnostic nomogram based on CK18-M65 combined with GP73 demonstrated good performance and effectively identified high-risk groups of patients with HCC.

The study was designed to evaluate microvascular invasion (MVI) using three-dimensional (3D) morphological indicators prior to surgery.

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.

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).

The preoperative 3D morphological indicator SIR of a tumor is an accurate predictor of MVI, providing a valuable tool in clinical decision-making.

Elevated Protein Induced by Vitamin-K Absence-II (PIVKA-II) has been shown to be an adverse prognostic factor in HCC patients undergoing liver transplantation (LT). No definitive data are available about the impact of PIVKA-II concerning post-LT recurrence in patients not secreting (≤ 20 ng/mL) alpha-fetoprotein (AFP). An observational retrospective study of the East-West HCC-LT consortium is reported. Between 2000 and 2019, 639 HCC patients were enrolled in 5 collaborative European and Japanese centers. To minimize the initial selection bias, an inverse probability therapy weighting method was adopted to analyze the data. In the post-inverse probability therapy weighting population, PIVKA-II (HR = 2.00; 95% CI: 1.52-2.64; p < 0.001) and AFP (HR=1.82; 95% CI: 1.48-2.24; p < 0.001) were the most relevant independent risk factors for post-LT recurrence. A sub-analysis focusing only on patients who are AFP non-secreting confirmed the negative role of PIVKA-II (HR=2.06, 95% CI: 1.26-3.35; p =0.004). When categorizing the entire population into 4 groups according to the AFP levels (≤ or > 20 ng/mL) and PIVKA (≤ or > 300 mUA/mL) at the time of LT, the lowest recurrence rates were observed in the low AFP-PIVKA-II group (5-year recurrence rate = 8.0%). Conversely, the high AFP-PIVKA-II group had the worst outcome (5-year recurrence rate = 35.1%). PIVKA-II secretion is a relevant risk factor for post-LT HCC recurrence. The role of this marker is independent of the AFP status. Combining both tumor markers, especially in the setting of LT, should be of great relevance for adding information about predicting the post-LT risk of tumor recurrence and selecting these patients for transplantation.

Hepatocellular cancer (HCC) is one of the main reasons for liver transplantation (LT). Biomarkers, such as alpha-foetoprotein (AFP) and Des-gamma-carboxy-prothrombin (DCP), can be helpful in defining the recurrence risk post LT. This study aims to evaluate the association between the intensity of DCP immunohistochemical labelling and serum DCP levels in patients undergoing LT for HCC. We carried out a prospective monocentric study including patients who all underwent LT for cirrhosis between 2016 and 2018 and all fell under the Milan criteria. The accepted diagnostic criteria for HCC were contrast-enhanced imaging and histology. Thirty-nine patients were followed for a median of 21 months, with HCC lesions categorized into negative, focally positive, and diffusely positive groups based on DCP immunohistochemistry. The serum DCP levels were significantly higher in the positive groups (258 mAU/mL for the focally and 257 mAU/mL for the diffusely positive) than in the negative group (48 mAU/mL) (p = 0.005) at diagnosis and at the time of liver transplantation (220 mAU/mL for the diffuse positive group). Microvascular invasion (58.8% vs. 19.0% for the diffusely positive and negative groups, respectively, p < 0.001) and lesion size (20 mm in the diffusely labelled group versus 12 mm in the other groups, p = 0.002) were significantly correlated with DCP labelling. Late recurrence occurred only in the positive groups; in the negative group, it occurred within the first 3 months after transplantation. DCP labelling in liver lesions correlates with serum levels and a more aggressive tumour profile. Further investigation is needed to determine if highly DCP-labelled tumours allow for the better selection of high-risk patients before LT.