We sought to develop a predictive model to preoperatively identify patients with hepatocellular carcinoma (HCC) at risk of undergoing futile upfront liver resection (LR).

Patients undergoing curative-intent LR for HCC were identified from a large multi-institutional database. Futile LR was defined by death or disease recurrence within six months postoperatively. Backward logistic regression was performed to identify factors associated with futility. Additionally, binary criteria were established for surgical candidacy, aiming to keep the likelihood of futility below 20 %.

Among 1633 patients with HCC, 264 (16.2 %) underwent futile upfront LR. Tumor burden score (TBS) (coefficient: 0.083, 95%CI: 0.067-0.099), alpha-fetoprotein (AFP) (coefficient: 0.254, 95%CI: 0.195-0.310), and albumin-bilirubin (ALBI) grade 2/3 (coefficient: 0.566, 95%CI: 0.420-0.718) were independently associated with an increased risk of futile LR. The model demonstrated strong discrimination and calibration in both derivation and validation cohorts. Low, intermediate, and high-risk groups were determined based on the risk model, each with an escalating likelihood of futility, worse histological features, and worse survival outcomes. Six distinct conditions based on AFP-adjusted-to-TBS criteria were established, all with a futility likelihood of less than 20 %. Patients fulfilling these criteria had significantly better long-term recurrence-free and overall survival. The futility risk model was made available online for wide clinical applicability: (https://altaf-pawlik-hcc-futilityofsurgery-calculator.streamlit.app/).

A preoperative risk model and AFP-adjusted-to-TBS criteria were developed and validated to predict the likelihood of futile LR among patients with HCC. This pragmatic clinical tool may assist clinicians in preoperative decision-making, helping them avoid futile surgery unlikely to offer long-term benefits.

Hepatocellular carcinoma (HCC) is a prevalent and lethal cancer, often diagnosed at advanced stages where traditional treatments such as surgical resection, liver transplantation, and locoregional therapies provide limited benefits. Hepatic arterial infusion chemotherapy (HAIC) has emerged as a promising treatment modality for advanced HCC, enhancing anti-tumor efficacy through targeted drug delivery while minimizing systemic side effects. However, the heterogeneous nature of HCC leads to variable responses to HAIC, highlighting the necessity for reliable predictive indicators to tailor personalized treatment strategies. This review explores the factors influencing HAIC success, including patient demographics, tumor characteristics, biomarkers, genomic profiles, and advanced imaging techniques such as radiomics and deep learning models. Additionally, the synergistic potential of HAIC combined with immunotherapy and molecular targeted therapies is examined, demonstrating improved survival outcomes. Prognostic scoring systems and nomograms that integrate clinical, molecular, and imaging data are discussed as superior tools for individualized prognostication compared to traditional staging systems. Understanding these predictors is essential for optimizing HAIC efficacy and enhancing survival and quality of life for patients with advanced HCC. Future research directions include large-scale prospective studies, integration of multi-omics data, and advancements in artificial intelligence to refine predictive models and further personalize treatment approaches.

To compare the prognostic power of commonly used staging systems of hepatocellular carcinoma (HCC) for predicting overall survival after transarterial chemoembolization (TACE).

This retrospective single center study included patients with HCC who underwent TACE between 2008 and 2019 in a single tertiary care center. After initial screening of 408 consecutive patients, 317 patients with HCC treated with conventional or drug-eluting beads-TACE were included. Five HCC staging systems (Barcelona Clinic Liver Cancer, Hong Kong Liver Cancer, Japan Integrated Staging, Cancer of the Liver Italian Program and Okuda) were compared using Kaplan Meier survival analysis and a log-rank test with overall survival (OS) as the study endpoint. Uni- and multivariate analyses of system-specific variables were applied to stratify outcomes and compare the ability to predict OS of patients after TACE. Four different measures were used to assess the homogeneity (Likelihood ratio:LR), discriminatory ability (linear trend:LT and C-index) and explanatory ability (Akaike Information Criterion:AIC).

The OS of the total cohort was 29.8 months. In terms of prognostic stratification, the BCLC staging system had the best performance (LT: 8.209, LR: 26.639, AIC: 317, c-index: 0.818) compared to HKLC (LT: 10.919, LR: 25.802, AIC: 443, c-index: 0.835), JIS (LT: 4.611, LR: 16.880, AIC: 449, c-index: 0.548), CLIP (LT: 6.738, LR: 13.109, AIC: 501, c-index: 0.782), and Okuda (LT: 7.185, LR: 0.760. LR: 16.356, AIC: 487, c-index: 0.760).

Across five commonly utilized international staging systems, the BCLC staging system demonstrated the greatest prognostic accuracy with respect to predicting OS of patients undergoing TACE.

Large hepatocellular carcinoma (HCC) is difficult to resect and accompanied by poor outcome. The aim was to evaluate the short-term and long-term outcomes of patients who underwent liver resection for large HCC, eventually drawing prediction models for short-term and long-term outcomes.

1710 large HCC patients were recruited and randomly divided into the training (n = 1140) and validation (n = 570) cohorts in a 2:1 ratio. Independent risk factors were identified by regression model and used to establish three nomograms for surgical risk, overall survival (OS), and recurrence-free survival (RFS) in the training cohort. Model performances were assessed by discrimination and calibration. The three models were also compared with six other staging systems.

Platelet (PLT), gamma-glutamyl transpeptidase (GGT), albumin-bilirubin (ALBI) grade, blood transfusion and loss, resection margin, tumor size, and tumor number were established in a nomogram to evaluate surgical risk ( https://largehcc.shinyapps.io/largehcc-morbidity/ ). The model had a good prediction capability with a C-index of 0.764 and 0.773 in the training and validation cohorts. Alpha-fetoprotein (AFP), resection margin, tumor size, tumor number, microvascular invasion, Edmondson-Steiner grade, tumor capsular, and satellite nodules were considered to construct a prognostic nomogram to predict the 1-, 3- and 5-year OS ( https://largehcc.shinyapps.io/largehcc-os/ ). The C-index of the model was 0.709 and 0.702 for the training and validation cohorts. Liver cirrhosis, albumin (ALB), total bilirubin (TBIL), AFP, tumor size, tumor number, microvascular invasion, and tumor capsular were used to draw a prognostic nomogram to predict the 1-, 3- and 5-year RFS ( https://largehcc.shinyapps.io/largehcc-rfs/ ). The C-index of the model was 0.695 and 0.675 in the training and validation cohorts. The discrimination showed that the models had significantly better predictive performances than six other staging systems.

Three novel nomograms were developed to predict short-term and long-term outcomes in patients with large HCC who underwent curative resection with adequate performance. These predictive models could help to design therapeutic interventions and surveillance for patients with large HCC.

Identifying alpha-fetal protein (AFP)-negative focal hepatic lesions presents a significant challenge, particularly in China. We sought to develop an economically portable tool for the diagnosis of benign and malignant liver lesions with AFP-negative status, and explore its clinical diagnostic efficiency.

A retrospective study was conducted at Peking University Shenzhen Hospital from January 2017 to February 2023, including a total of 348 inpatients with AFP-negative liver space-occupying lesions. The study used a training set of 252 inpatients from January 2017 to September 2021 to establish a diagnostic model for differentiating benign and malignant AFP-negative liver space-occupying lesions. Additionally, a validation cohort of 96 inpatients from October 2021 to February 2023 was used to confirm the diagnostic performance of the model. From January 2017 to February 2023, patients at JingNing People's Hospital, Gansu Province were assigned to the external cohort (n = 78).

A predictive tool was established by screening age, gender, hepatitis B virus (HBV)/hepatitis C virus (HCV) infected, single lesion, alanine amino transferase (ALT), and lymphocyte-to-monocyte ratio (LMR) using multivariate logistic regression analysis and clinical practice. The area under the curve (AUC) of the model was 0.911 (95% CI [0.873-0.949]) in the training set and 0.882 (95% CI [0.815-0.949]) in the validation cohort. In addition, the model achieved an area under the curve of 0.811 (95% CI [0.687-0.935]) in the external validation cohort.

Our results demonstrated that the predictive tool has the characteristics of good diagnostic efficiency, economy and convenience, which is helpful for the clinical triage and decision-making of AFP-negative liver space-occupying lesions.

Patients with early-stage hepatocellular carcinoma (HCC) generally have good survival rates following surgical resection. However, a subset of these patients experience recurrence within five years post-surgery.

To develop predictive models utilizing machine learning (ML) methods to detect early-stage patients at a high risk of mortality.

Eight hundred and eight patients with HCC at Beijing Ditan Hospital were randomly allocated to training and validation cohorts in a 2:1 ratio. Prognostic models were generated using random survival forests and artificial neural networks (ANNs). These ML models were compared with other classic HCC scoring systems. A decision-tree model was established to validate the contribution of immune-inflammatory indicators to the long-term outlook of patients with early-stage HCC.

Immune-inflammatory markers, albumin-bilirubin scores, alpha-fetoprotein, tumor size, and International Normalized Ratio were closely associated with the 5-year survival rates. Among various predictive models, the ANN model generated using these indicators through ML algorithms exhibited superior performance, with a 5-year area under the curve (AUC) of 0.85 (95%CI: 0.82-0.88). In the validation cohort, the 5-year AUC was 0.82 (95%CI: 0.74-0.85). According to the ANN model, patients were classified into high-risk and low-risk groups, with an overall survival hazard ratio of 7.98 (95%CI: 5.85-10.93, P < 0.0001) between the two cohorts.

A non-invasive, cost-effective ML-based model was developed to assist clinicians in identifying high-risk early-stage HCC patients with poor postoperative prognosis following surgical resection.

The prognosis for patients with hepatocellular carcinoma (HCC) depends on tumor stage and remnant liver function. However, it often includes tumor morphology, which is usually assessed with imaging studies or pathologic analysis, leading to limited predictive performance. Therefore, the aim of this study was to develop a simple and low-cost prognostic score for HCC based on serum biomarkers in routine clinical practice. A total of 3,100 patients were recruited. The least absolute shrinkage and selector operation (LASSO) algorithm was used to select the significant factors for overall survival. The prognostic score was devised based on multivariate Cox regression of the training cohort. Model performance was assessed by discrimination and calibration. Albumin (ALB), alkaline phosphatase (ALP), and alpha-fetoprotein (AFP) were selected by the LASSO algorithm. The three variables were incorporated into multivariate Cox regression to create the risk score (APP score = 0.390* ln (ALP) + 0.063* ln(AFP) - 0.033*ALB). The C-index, K-index, and time-dependent AUC of the score displayed significantly better predictive performance than 5 other models and 5 other staging systems. The model was able to stratify patients into three different risk groups. In conclusion, the APP score was developed to estimate survival probability and was used to stratify three strata with significantly different outcomes, outperforming other models in training and validation cohorts as well as different subgroups. This simple and low-cost model could help guide individualized follow-up.

Diabetes is prevalent among patients with hepatocellular carcinoma (HCC) and is associated with a poor prognosis. Although the hypoglycemic drug metformin has shown antitumor effects, its potential positive effect on patients with HCC and diabetes undergoing transarterial chemoembolization (TACE) remains unclear. Therefore, this study aimed to investigate the efficacy and safety of metformin in patients with HCC and type II diabetes who are receiving TACE.

This retrospective study involved 372 consecutive patients with HCC and type II diabetes across three medical centers between January 2014 and June 2021. All patients underwent TACE. Propensity score matching (PSM) was used to reduce selection bias. Cox proportional hazards regression was employed to compare all-cause death between the metformin and nonmetformin groups while competing risk regression was performed to assess cancer-specific death.

Among 372 patients included in the study, 208 patients (177 male patients and 31 female patients) with a mean age of 59.6 (10.3) years received metformin, and 164 patients (139 male patients and 25 female patients) with a mean age of 60.3 (10.0) years did not. Before PSM, patients with metformin had significantly longer median overall survival (mOS) and median progression-free survival (mPFS) than those without metformin (mOS: 34 months, 95% CI: 25.6-42.4 vs. 20 months, 95% CI: 15.3-24.7; P <0.001; mPFS: 11 months, 95% CI: 9.3-12.7 vs. 8 months, 95% CI: 5.9-10.1; P <0.001). Similar results were observed after PSM. Multivariate regression analysis indicated that metformin was associated with a reduced risk of all-cause mortality (HR: 0.589, 95% CI: 0.454-0.763; P <0.001) and tumor progression (HR: 0.667, 95% CI: 0.526-0.845; P =0.001) before PSM. After excluding deaths related to other factors, metformin continued to demonstrate a reduction in cancer-specific mortality risk among the patients. Subgroup analysis further revealed that patients using metformin had lower all-cause mortality risk and tumor progression risk than those without metformin in most subgroups. Adverse event evaluation suggested that metformin could lead to elevated nausea incidence.

Metformin may confer survival benefits to patients with HCC and type II diabetes undergoing TACE. Metformin may simultaneously address multiple aspects of treatment in these patients.

Patients undergoing liver resection for hepatocellular carcinoma (HCC) often possess good liver reserve, which may limit the prognostic effectiveness of existing liver function scores. This study aimed to develop a novel liver function score and a preoperative prognostic model specifically for HCC resection patients.

Eight hundred twenty-seven HCC patients undergoing initial liver resection were segregated into training and validation cohorts in a 6:4 ratio. Cox regression analysis was employed to identify significant parameters influencing overall survival. The efficacy of the liver function score and prognostic model was evaluated using metrics such as the area under the receiver operating characteristic curve.

Aspartate aminotransferase (AST) and albumin emerged as significant prognostic indicators. The AST-albumin (ASAL) score, calculated as exp [AST (IU/L) × 0.005 - albumin (g/dL) × 1.043] × 100, outperformed existing scores such as Child-Turcotte-Pugh, albumin-bilirubin, platelet-albumin, and AST-platelet ratio index in both training and validation cohorts. Additionally, a scoring model that combined the ASAL score with alpha-fetoprotein and the up-to-seven criterion exhibited superior discriminatory capabilities compared to the American Joint Committee on Cancer tumor, node, metastasis stage, and Barcelona Clinic Liver Cancer stage.

The proposed prognostic model that integrates the novel ASAL score offers promising prognostic potential for HCC patients undergoing liver resection.

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is poorly understood, while the predictive value of the staging in which it is included is controversial.

Patients with cHCC-CCA underwent radical hepatectomy in two medical centers in China were enrolled and staged based on optimal cut-off values of tumor burden score (TBS), determined using the X-Tile. The association between TBS and prognosis was evaluated by Cox proportional hazard models and Kaplan-Meier curves with Log-rank test. TBS model and primary liver cancer (PLC) stages were compared by discrimination, consistency, and clinical utility, which were further validated by a 5-folds cross-validation.

A total of 192 patients were stratified into low, medium, and high TBS, comprising 92, 51 and 49 patients, respectively. Prognoses worsened with elevated TBS in both the training and validation cohorts. TBS was not only an independent prognostic indicator in univariate and multivariate cox regression, but also a stable risk factor in subgroup analysis according to baseline variables. TBS exhibited best discrimination within these predictive models, as evidenced by the highest c-index and area under curve values of time-dependent receiver operating curves within 5 years post-surgery. TBS calibration plots revealed favorable consistency between prediction and observation. Decision curve analysis suggested higher net benefits for TBS. A 5-folds cross-validation revealed consistent results.

TBS could be applied to stratify cHCC-CCA patients after surgery into groups with statistically different prognoses. Moreover, TBS exhibited optimal prognostic value over all available PLC stages and may inform clinical decisions.

To develop multiple machine learning (ML) models based on the prognostic nutritional index (PNI) and determine the optimal model for predicting long-term survival outcomes in hepatocellular carcinoma (HCC) patients after local ablation.

From January 2009 to December 2019, we analyzed data from 848 primary HCC patients who underwent local ablation. ML models were constructed and evaluated using the concordance index (C-index), concordance-discordance area under curve (C/D AUC), and Brier scores. The optimal ML model was interpreted using the partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP) framework. Additionally, the prognostic performance of our model was compared with other models.

Alkaline phosphatase, preoperation alpha-fetoprotein level, PNI, tumor number, and tumor size were identified as independent prognostic factors for ML model construction. Among the 19 ML algorithms tested, the Aorsf model showed superior performance in both the training cohort (C/D AUC: 0.733; C-index: 0.736; Brier score: 0.133) and validation cohort (C/D AUC: 0.713; C-index: 0.793; Brier score: 0.117). The time-dependent AUC of the Aorsf model for predicting overall survival was as follows: 1-, 3-, 5-, 7-, and 9-year were 0.828, 0.765, 0.781, 0.817, and 0.812 in the training cohort, 0.846, 0.859, 0.824, 0.845, and 0.874 in the validation cohort, respectively. The PDP and SHAP algorithms were employed for visual interpretation. Furthermore, time-AUC and decision curve analysis demonstrated that the Aorsf model provided superior clinical benefits compared to other models.

The PNI-based Aorsf model effectively predicts long-term survival outcomes after ablation therapy, making a significant contribution to HCC research by improving surveillance, prevention, and treatment strategies.

Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective.

To develop and independently validate a machine learning mortality risk quantification method for HCC patients using standard-of-care clinical data and liver radiomics on baseline magnetic resonance imaging (MRI).

This retrospective study included all patients with multiphasic contrast-enhanced MRI at the time of diagnosis treated at our institution. Patients were censored at their last date of follow-up, end-of-observation, or liver transplantation date. The data were randomly sampled into independent cohorts, with 85% for development and 15% for independent validation. An automated liver segmentation framework was adopted for radiomic feature extraction. A random survival forest combined clinical and radiomic variables to predict overall survival (OS), and performance was evaluated using Harrell's C-index.

A total of 555 treatment-naïve HCC patients (mean age, 63.8 years ± 8.9 [standard deviation]; 118 females) with MRI at the time of diagnosis were included, of which 287 (51.7%) died after a median time of 14.40 (interquartile range, 22.23) months, and had median followed up of 32.47 (interquartile range, 61.5) months. The developed risk prediction framework required 1.11 min on average and yielded C-indices of 0.8503 and 0.8234 in the development and independent validation cohorts, respectively, outperforming conventional clinical staging systems. Predicted risk scores were significantly associated with OS (p < .00001 in both cohorts).

Machine learning reliably, rapidly, and reproducibly predicts mortality risk in patients with hepatocellular carcinoma from data routinely acquired in clinical practice.

Precision mortality risk prediction using routinely available standard-of-care clinical data and automated MRI radiomic features could enable personalized follow-up strategies, guide management decisions, and improve clinical workflow efficiency in tumor boards.

• Machine learning enables hepatocellular carcinoma mortality risk prediction using standard-of-care clinical data and automated radiomic features from multiphasic contrast-enhanced MRI. • Automated mortality risk prediction achieved state-of-the-art performances for mortality risk quantification and outperformed conventional clinical staging systems. • Patients were stratified into low, intermediate, and high-risk groups with significantly different survival times, generalizable to an independent evaluation cohort.

Hepatocellular carcinoma is the predominant form of primary liver cancer and is the leading cause of cancer-related death. The aim of this study was to construct a nomogram to predict cancer-specific survival (CSS) in middle-aged patients with advanced hepatocellular carcinoma. Clinical data were downloaded from the Surveillance, Epidemiology and End Results (SEER) database for middle-aged patients diagnosed with advanced hepatocellular carcinoma (AJCC stage III and IV) from 2000 to 2019. The patients were randomized in a 7:3 ratio into training cohort and validation cohort. Univariate and multivariate Cox regression analyses were performed in the training cohort to screen for independent risk factors associated with cancer-specific survival for the construction of nomogram. The nomogram was examined and evaluated using the consistency index (C-index), area under the curve (AUC), and calibration plots. The clinical application value of the model was evaluated using decision curve analysis (DCA). A total of 3026 patients were selected, including 2244 in the training cohort and 962 in the validation cohort. Multivariate analysis revealed gender, marital status, American Joint Committee on Cancer (AJCC) stage, tumor size, bone metastasis, lung metastasis, alpha-fetoprotein (AFP) level, surgery, radiotherapy, chemotherapy as independent risk factors, which were all included in the construction of the nomogram. In the training cohort, the AUC values were 0.74 (95% CI: 0.76-0.72), 0.78 (95% CI: 0.82-0.75), and 0.82 (95% CI: 0.86-0.78) at 1-, 3-, and 5-year CSS, respectively. The calibration plots showed good consistency between the actual and predicted values. The DCA curves indicated that the nomogram model could more accurately predict CSS at 1-, 3-, and 5-year in middle-aged patients with advanced hepatocellular carcinoma compared with the AJCC staging system. Highly similar results to the training cohort were also observed in the validation cohort. In the risk stratification system, good differentiation was shown between the 2 groups, and Kaplan-Meier survival analysis indicated that surgery could prolong patient survival. In this study, we developed a nomogram and risk stratification system for predicting CSS in middle-aged patients with advanced hepatocellular carcinoma. The prediction model has good predictive performance and can help clinicians in judging prognosis and clinical decision making.

In this study, we aimed to establish nomograms to predict the microvascular invasion (MVI) and early recurrence in patients with small hepatocellular carcinoma (SHCC), thereby guiding individualized treatment strategies for prognosis improvement.

This study retrospectively analyzed 326 SHCC patients who underwent radical resection at Wuhan Union Hospital between April 2017 and January 2022. They were randomly divided into a training set and a validation set at a 7:3 ratio. The preoperative nomogram for MVI was constructed based on univariate and multivariate logistic regression analysis, and the prognostic nomogram for early recurrence was constructed based on univariate and multivariate Cox regression analysis. We used the receiver operating characteristic (ROC) curves, area under the curves (AUCs), and calibration curves to estimate the predictive accuracy and discriminability of nomograms. Decision curve analysis (DCA) and Kaplan-Meier survival curves were employed to further confirm the clinical effectiveness of nomograms.

The AUCs of the preoperative nomogram for MVI on the training set and validation set were 0.749 (95%CI: 0.684-0.813) and 0.856 (95%CI: 0.805-0.906), respectively. For the prognostic nomogram, the AUCs of 1-year and 2-year RFS respectively reached 0.839 (95%CI: 0.775-0.903) and 0.856 (95%CI: 0.806-0.905) in the training set, and 0.808 (95%CI: 0.719-0.896) and 0.874 (95%CI: 0.804-0.943) in the validation set. Subsequent calibration curves, DCA analysis and Kaplan-Meier survival curves demonstrated the high accuracy and efficacy of the nomograms for clinical application.

The nomograms we constructed could effectively predict MVI and early recurrence in SHCC patients, providing a basis for clinical decision-making.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, posing a significant global health challenge with an increasing incidence. In recent years, multiple staging systems and scores have been proposed, emphasising the necessity for the development of precise prognostic tools. The well-documented etiological relationship between chronic inflammation and carcinogenesis has prompted researchers to explore novel prognostic markers associated with the inflammatory status of HCC patients. This review summarises the current data about inflammation-based scores in the context of HCC. We discuss established scores like the Glasgow Prognostic Score (GPS), modified GPS (mGPS) and the neutrophil-to-lymphocyte ratio (NLR) and others not as extensively studied, examining their utility in predicting survival outcomes and treatment response in HCC patients. Furthermore, we explore emerging scores, including the prognostic nutritional index (PNI) and other lymphocyte-based scores, assessing their potential in refining risk stratification and guiding therapeutic decisions in the era of precision medicine. As research progresses and these scores undergo further refinement and integration into the evolving landscape of HCC management, they carry significant potential for improving patient outcomes.

Deaths from the majority of cancers are falling globally, but the incidence and mortality from hepatocellular carcinoma (HCC) is increasing in the United Kingdom and in other Western countries. HCC is a highly fatal cancer, often diagnosed late, with an incidence to mortality ratio that approaches 1. Despite there being a number of treatment options, including those associated with good medium to long-term survival, 5-year survival from HCC in the UK remains below 20%. Sex, ethnicity and deprivation are important demographics for the incidence of, and/or survival from, HCC. These clinical practice guidelines will provide evidence-based advice for the assessment and management of patients with HCC. The clinical and scientific data underpinning the recommendations we make are summarised in detail. Much of the content will have broad relevance, but the treatment algorithms are based on therapies that are available in the UK and have regulatory approval for use in the National Health Service.

Direct-acting antivirals (DAAs) have been accessible in Japan since 2014. The aim of this study is to compare how the prognosis of patients with hepatitis C virus (HCV)-associated hepatocellular carcinoma (HCV-HCC) changed before and after DAA development.

A retrospective analysis of 1949 Japanese HCV-HCC patients from January 2000 to January 2023 categorized them into pre-DAA (before 2013, n = 1169) and post-DAA (after 2014, n = 780) groups. Changes in clinical features and prognosis were assessed.

Despite no significant differences in BCLC stage between groups, the post-DAA group exhibited higher rates of sustained virological response (SVR) (45.6% vs. 9.8%), older age (73 vs 69 years), lower levels of AST (40 vs 56 IU/L), ALT (31 vs 46 IU/L), and AFP (11.7 vs 23.6 ng/mL), higher platelet count (13.5 vs 10.8 × 104/μL), better prothrombin time (88.0% vs 81.9%), and better ALBI score (-2.54 vs -2.36) (all P < 0.001). The post-DAA group also showed higher rates of curative treatments (74.1% vs 65.2%) and significantly improved recurrence-free survival (median 2.8 vs 2.1 years). Adjusted for inverse probability weighting, overall survival was superior in the post-DAA group (median 7.4 vs 5.6 years, P < 0.001). Subanalysis within the post-DAA group revealed significantly shorter overall survival for patients without SVR (median 4.8 years vs NA vs NA) compared to pre-SVR or post-SVR patients (both P < 0.001). No significant difference in OS was observed between the pre-SVR and post-SVR groups (P = 1.0).

The development of DAA therapy has dramatically improved the prognosis of HCV-HCC patients.

Because repeat hepatectomy for recurrent hepatocellular carcinoma is a potentially invasive procedure, it is necessary to identify patients who truly benefit from repeat hepatectomy. Albumin-bilirubin grading has been reported to predict survival in patients with hepatocellular carcinoma. However, as prognosis also depends on tumor factors, a staging system that adds tumor factors to albumin-bilirubin grading may lead to a more accurate prognostication in patients with recurrent hepatocellular carcinoma.

Albumin-bilirubin grading and serum alpha-fetoprotein levels were combined and the albumin-bilirubin-alpha-fetoprotein score was created ([albumin-bilirubin grading = 1; 1 point, 2 or 3; 2 points] + [alpha-fetoprotein<75 ng/mL, 0 points; ≥5, 1 point]). Patients were classified into three groups, and their characteristics and survival were evaluated. The predictive ability of the albumin-bilirubin-alpha-fetoprotein score was compared with that of the Cancer of the Liver Italian Program and the Japan Integrated Stage scores.

Albumin-bilirubin-alpha-fetoprotein score significantly stratified postoperative survival (albumin-bilirubin-alpha-fetoprotein score = 1/2/3: 5-year recurrence-free survival [%]: 22.4/20.7/0.0, p < 0.001) and showed the highest predictive value for survival among the integrated systems (albumin-bilirubin-alpha-fetoprotein score/Japan Integrated Stage/Cancer of the Liver Italian Program: 0.785/0.708/0.750).

Albumin-bilirubin-alpha-fetoprotein score is useful for predicting the survival of patients with recurrent hepatocellular carcinoma undergoing repeat hepatectomy.

The prognosis of HCC patients without MVI (so called M0) is highly heterogeneous and the need for adjuvant therapy is still controversial.

Patients with HCC with M0 who underwent liver resection (LR) or liver transplantation (LT) as an initial therapy were included. The Eastern Hepatobiliary Surgery Hospital (EHBH)-M0 score was developed from a retrospective cohort to form the training cohort. The classification which was developed using multivariate cox regression analysis was externally validated.

The score was developed using the following factors: α-fetoprotein level, tumour diameter, liver cirrhosis, total bilirubin, albumin and aspartate aminotransferase. The score differentiated two groups of M0 patients (≤3, >3 points) with distinct long-term prognoses outcomes (median overall survival (OS), 98.0 vs. 46.0 months; p < 0.001). The predictive accuracy of the score was greater than the other commonly used staging systems for HCC. And for M0 patients with a higher score underwent LR. Adjuvant transcatheter arterial chemoembolization (TACE) was effective to prolong OS.

The EHBH M0 scoring system was more accurate in predicting the prognosis of HCC patients with M0 after LR or LT. Adjuvant therapy is recommended for HCC patients who have a higher score.

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer globally, poses a substantial health burden. Influenced by risk factors such as hepatitis B or C virus infections, chronic consumption of alcohol, and metabolic dysfunction, its exact etiology likely involves a complex interplay between viral infection, hepatocyte mutations, and chronic liver diseases like cirrhosis and metabolic dysfunction-associated steatohepatitis, and demographic variables like sex, race, and age. Disease stage significantly impacts the prognosis of HCC. There is significant potential for life-saving and socioeconomic benefits through the implementation of surveillance programs and the introduction of low-cost screening measures for high-risk groups; these screening measures include ultrasound imaging and blood tests. Treatment options for HCC encompass liver resection, transplantation, transarterial chemoembolization, radiation therapy, chemotherapy, targeted therapy, and immunotherapy. Despite therapeutic advances, treating advanced HCC remains challenging, emphasizing the need for continued efforts in prevention, early detection, and development of treatments to improve prognosis and long-term survival.

The prognosis of patients with hepatocellular carcinoma (HCC) is closely related to their liver reserves. The Child-Pugh (CP) score has traditionally been used to evaluate this reserve, with CP Grade B (CP score ≥ 7) associated with a higher risk of radiation-induced liver disease after stereotactic body radiation therapy (SBRT). However, the CP score has limitations, as it does not accurately assess liver reserve capacity. The albumin-bilirubin (ALBI) score has been introduced as a meticulous indicator of liver reserve for the treatment of HCC. We retrospectively evaluated the role of the ALBI score in estimating the worsening liver reserve in 42 patients with HCC treated with SBRT using CyberKnife between 2015 and 2023. The median biologically effective dose (α/β = 10 Gy) was 100 Gy. For a median follow-up duration of 17.4 months, the 1-year overall survival (OS), local control (LC) and progression-free survival (PFS) rates were 100, 98 and 62%, respectively. Worsening liver reserve was defined as an increase in the modified ALBI grade or CP score within 1 year after SBRT. Univariate and multivariate analyses showed that the baseline ALBI score (≥-2.7 vs <-2.7) was the only significantly different predictor of worsening liver reserve. The OS and LC rates after SBRT for HCC were satisfactory. However, the PFS was poor, and recurrent HCC will require additional treatment. It is clinically important to predict the liver reserve capacity after SBRT, and the baseline ALBI score is a useful predictor.

We sought to develop and validate a preoperative model to predict survival after recurrence (SAR) in hepatocellular carcinoma (HCC).

Although HCC is characterized by recurrence as high as 60%, models to predict outcomes after recurrence remain relatively unexplored.

Patients who developed recurrent HCC between 2000 and 2020 were identified from an international multi-institutional database. Clinicopathologic data on primary disease and laboratory and radiologic imaging data on recurrent disease were collected. Multivariable Cox regression analysis and internal bootstrap validation (5000 repetitions) were used to develop and validate the SARScore. Optimal Survival Tree analysis was used to characterize SAR among patients treated with various treatment modalities.

Among 497 patients who developed recurrent HCC, median SAR was 41.2 months (95% CI 38.1-52.0). The presence of cirrhosis, number of primary tumors, primary macrovascular invasion, primary R1 resection margin, AFP>400 ng/mL on the diagnosis of recurrent disease, radiologic extrahepatic recurrence, radiologic size and number of recurrent lesions, radiologic recurrent bilobar disease, and early recurrence (≤24 months) were included in the model. The SARScore successfully stratified 1-, 3- and 5-year SAR and demonstrated strong discriminatory ability (3-year AUC: 0.75, 95% CI 0.70-0.79). While a subset of patients benefitted from resection/ablation, Optimal Survival Tree analysis revealed that patients with high SARScore disease had the worst outcomes (5-year AUC; training: 0.79 vs. testing: 0.71). The SARScore model was made available online for ease of use and clinical applicability ( https://yutaka-endo.shinyapps.io/SARScore/ ).

The SARScore demonstrated strong discriminatory ability and may be a clinically useful tool to help stratify risk and guide treatment for patients with recurrent HCC.

Liver cancer is one of the most common malignant tumors in the world, and its incidence and mortality are increasing year by year. The prognosis of liver cancer depends on the stage of liver cancer, the treatment method, the liver function, and individual differences. The prognosis of liver cancer mainly worsens with the progression of the stage. The prediction and staging system of liver cancer prognosis plays a very important role in the outcome of liver cancer prognosis, providing some guidance for clinical practice and bringing benefits for patients. This article reports on the prediction models and staging systems that have been applied in the field of liver cancer in the past 5 years, objectively analyzes the advantages and disadvantages, applicable population of each model and staging system, and searches for other patient and clinical characteristics that need to be considered for successfully establishing a prediction model, aiming to improve the specificity, sensitivity, and accuracy of liver cancer prediction and increase the overall survival rate of liver cancer.

Hepatocellular carcinoma (HCC) is the fifth most common cancer in men and the eighth most common cancer in women worldwide. It is also the second leading cause of cancer death worldwide, with 780,000 deaths in 2018. Seventy-two percent of HCC cases occur in Asia, 10% in Europe, 8% in Africa, 5% in North America, and 5% in Latin America (Singal et al. in J Hepatol 72(2):250-261, 2020 [1]).

Multiple hepatocellular carcinoma (HCC) staging systems have been proposed and used clinically over time. These may consider clinical, pathological, radiological, or treatment response factors, depending on the model. Given the heterogeneity of HCC treatment in its different stages and the validation of the systems in different populations, they are not universal. Likewise, the improvement in diagnostic tools, as well as novel therapeutic alternatives, have made these models more complex. Despite this, some have been modified over time in line with advances in the field, and although there is no universally accepted one, each has its usefulness, strengths, and weaknesses.

Cancer metabolism research area evolved greatly, however, is still unknown the impact of systemic metabolism control and diet on cancer. It makes sense that systemic regulators of metabolism can act directly on cancer cells and activate signalling, prompting metabolic remodelling needed to sustain cancer cell survival, tumour growth and disease progression. In the present review, we describe the main glucagon functions in the control of glycaemia and of metabolic pathways overall. Furthermore, an integrative view on how glucagon and related signalling pathways can contribute for pancreatic neuroendocrine tumours (pNETs) and hepatocellular carcinomas (HCC) progression, since pancreas and liver are the major organs exposed to higher levels of glucagon, pancreas as a producer and liver as a scavenger. The main objective is to bring to discussion some glucagon-dependent mechanisms by presenting an integrative view on microenvironmental and systemic aspects in pNETs and HCC biology.

Transarterial chemoembolization (TACE), one of the most commonly used postoperative adjuvant therapy for HCC, has achieved satisfactory outcomes. This study aimed to explore the prognostic value of lung immune prognostic index (LIPI) and develop a novel nomogram for recurrence-free survival (RFS) of HCC patients received postoperative adjuvant TACE (PA-TACE).

The prognostic value of LIPI was evaluated by C-index, receiver operating characteristic (ROC) analysis, and Kaplan-Meier survival curve. An effective nomogram based on preoperative prognostic factors was established from multivariate analysis and validated in the validation cohort.

The ROC and survival analysis demonstrated that the LIPI exhibited better prediction performance of HCC recurrence than other inflammatory biomarkers. According to univariate and multivariate analysis, LIPI, followed by AFP, MVI and age, were significant independent predictors for HCC recurrence and were utilized to construct the nomogram. The C-indexes of the nomogram were 0.746 (95% CI 0.721-0.770) and 0.738 (95% CI 0.701-0.775) in the training and validation cohort, respectively. The AUCs for the 1-, 2-, and 3-year RFS were 0.799, 0.867 and 0.884 in the training cohort and 0.798, 0.779 and 0.770 in the validation cohort, respectively. The calibration curves presented good consistencies. Moreover, compared with the LIPI and other clinical staging system, the established nomogram presented better prognostic performance.

Preoperative LIPI might be a powerful predictor for RFS in HCC patients received PA-TACE. The LIPI-based nomogram could further effectively predict the risk of recurrence and help clinicians formulate personalized follow-up strategies and adjuvant therapy to improve patient outcomes.

To develop and validate a risk score based on preoperative clinical-radiological parameters for predicting overall survival (OS) in patients undergoing surgical resection for hepatocellular carcinoma (HCC).

From July 2010 to December 2021, consecutive patients with surgically-proven HCC who underwent preoperative contrast-enhanced MRI were retrospectively enrolled. A preoperative OS risk score was constructed in the training cohort using a Cox regression model and validated in a propensity score-matched internal validation cohort and an external validation cohort.

A total of 520 patients were enrolled, among whom 210, 210, and 100 patients were from the training, internal validation, and external validation cohorts, respectively. Independent predictors for OS included incomplete tumor "capsule," mosaic architecture, tumor multiplicity, and serum alpha-fetoprotein, which were incorporated into the "OSASH score." The C-index the OSASH score was 0.85, 0.81, and 0.62 in the training, internal, and external validation cohorts, respectively. Using 32 as the cutoff point, the OSASH score stratified patients into prognostically distinct low- and high-risk groups among all study cohorts and six subgroups (all p < 0.05). Furthermore, patients with BCLC stage B-C HCC and OSASH-low risk achieved comparable OS to that of patients with BCLC stage 0-A HCC and OSASH-high risk in the internal validation cohort (5-year OS rates, 74.7 vs. 77.8%; p = 0.964).

The OSASH score may help predict OS in HCC patients undergoing hepatectomy and identify potential surgical candidates among those with BCLC stage B-C HCC.

By incorporating three preoperative MRI features and serum AFP, the OSASH score may help predict postsurgical overall survival in patients with hepatocellular carcinoma and identify potential surgical candidates among those with BCLC stage B and C HCC.

• The OSASH score incorporating three MRI features and serum AFP can be used to predict OS in HCC patients who received curative-intent hepatectomy. • The score stratified patients into prognostically distinct low- and high-risk strata in all study cohorts and six subgroups. • Among patients with BCLC stage B and C HCC, the score identified a subgroup of low-risk patients who achieved favorable outcomes after surgery.

Hepatocellular carcinoma (HCC) ranks fourth in cancer-related deaths worldwide. Semiannual surveillance of the disease for patients with cirrhosis or hepatitis B virus allows for early detection with more favorable outcomes. The current underuse of surveillance programs demonstrates the need for intervention at both the patient and provider level. Mail outreach along with navigation provision has proven to increase surveillance follow-up in patients, while provider-targeted electronic medical record reminders and compliance reports have increased provider awareness of HCC surveillance. Imaging is the primary mode of diagnosis in HCC with The Liver Imaging Reporting and Data System (LI-RADS) being a widely accepted comprehensive system that standardizes the reporting and data collection for HCC. The management of HCC is complex and requires multidisciplinary team evaluation of each patient based on their preference, the state of the disease, and the available medical and surgical interventions. Staging systems are useful in determining the appropriate intervention for HCC. Early-stage HCC is best managed by curative treatment modalities, such as liver resection, transplant, or ablation. For intermediate stages of the disease, transarterial local regional therapies can be applied. Advanced stages of the disease are treated with systemic therapies, for which there have been recent advances with new drug combinations. Previously sorafenib was the mainstay systemic treatment, but the recent introduction of atezolizumab plus bevacizumab proves to have a greater impact on overall survival. Although there is a current lack of improved outcomes in Phase III trials, neoadjuvant therapies are a potential avenue for HCC management in the future.

Hepatocellular carcinoma (HCC) is the principal primary liver cancer and one of the most frequent malignant tumors worldwide in patients with chronic liver disease. When diagnosed at an advanced stage, it is often associated with portal vein tumor thrombosis (PVTT), which heavily affects patients' prognosis. Imaging evaluation is crucial in PVTT detection and staging; computed tomography and magnetic resonance are the principal diagnostic tools. Contrast-enhanced ultrasound (CEUS) is a non-invasive and easily repeatable method that can also be used in patients with impaired renal function. It represents an important means for the identification of PVTT, particularly differentiating neoplastic and non-neoplastic thrombosis through the analysis of ultrasound enhancement characteristics of the thrombosis (arterial hyperenhancement and portal washout), thus allowing more refined disease staging, appropriate treatment planning, and response evaluation, along with prognosis assessment.

For the 23rd Nationwide Follow-up Survey of Primary Liver Cancer in Japan, data from 20 889 newly registered patients and 42 274 previously registered follow-up patients were compiled from 516 institutions over a 2-year period from January 1, 2014 to December 31, 2015. Basic statistics compiled for patients newly registered in the 23rd survey were cause of death, past medical history, clinical diagnosis, imaging diagnosis, treatment-related factors, pathological diagnosis, recurrence status, and autopsy findings. Compared with the previous 22nd survey, the population of patients with hepatocellular carcinoma (HCC) was older at the time of clinical diagnosis, had more female patients, had more patients with non-B non-C HCC, had smaller tumor diameter, and was more frequently treated with hepatectomy. Cumulative survival rates were calculated for HCC, intrahepatic cholangiocarcinoma, and combined hepatocellular cholangiocarcinoma (combined HCC and intrahepatic cholangiocarcinoma) by treatment type and background characteristics for patients newly registered between 2004 and 2015 whose final outcome was survival or death. The median overall survival and cumulative survival rates for HCC were calculated by dividing patients by combinations of background factors (number of tumors, tumor diameter, Child-Pugh grade, or albumin-bilirubin grade) and by treatment type (hepatectomy, radiofrequency ablation therapy, transcatheter arterial chemoembolization, hepatic arterial infusion chemotherapy, and systemic therapy). The same values were also calculated according to registration date by dividing patients newly registered between 1978 and 2015 into five time period groups. The data obtained from this nationwide follow-up survey are expected to contribute to advancing clinical research and treatment of primary liver cancer in the world.

Non-alcoholic fatty liver disease (NAFLD) has gained attention in the last few years due to its increasing prevalence worldwide becoming a global epidemic. The increasing incidence of NAFLD and the concurrent increase in the number of hepatocellular carcinoma (HCC) cases at a global level is a matter of concern. HCC has several risk factors, of which NAFLD and its associated metabolic disturbances-type 2 diabetes mellitus, obesity, and dyslipidemia-are of great interest due to their accelerating rise in incidence worldwide. There is a high amount of data derived from basic and clinical studies that reveal the molecular pathways that drive NAFLD-associated HCC. Based on these findings, new prevention, surveillance, and treatment strategies are emerging. However, current data on treatment modalities in NAFLD-associated HCC are still scarce, though the results from non-NAFLD HCC studies are promising and could provide a basis for a future research agenda to address NAFLD/NASH patients. Clinicians should carefully assess all the clinical and radiological parameters and establish a prognosis based on the Barcelona Clinic Liver Cancer classification and discuss in a multidisciplinary team the treatment strategy. The specific factors associated with NAFLD-associated HCC which can have a negative impact on survival even in patients with early HCC, such as cardiovascular disease, type 2 diabetes, and obesity, should be taken into consideration. This review aims to discuss the latest recommendations regarding the diagnosis and treatment of NAFLD-associated HCC and the remaining challenges.

CD8+T cells are essential components of the adaptive immune system and are crucial in the body's immune system. This study aimed to investigate how the prognosis of patients with advanced hepatocellular carcinoma (HCC) was affected by their CD8+ T cell counts and age and established an effective nomogram model to predict the overall survival (OS).

A total of 427 patients with advanced HCC from Beijing Ditan Hospital, Capital Medical University, were enrolled in this study and randomly divided into training and validation groups, with 300 and 127 individuals in each group, respectively. Cox regression analysis was used to screen for independent risk factors for advanced HCC, and the interactive relationship between CD8+T cells and patient age was examined to establish a nomogram prediction model.

Cox multivariate regression and interaction analyses indicated that tumor number, tumor size, aspartate aminotransferase (AST), C-reactive protein (CRP), relationship of CD8+T cell counts and age were independent predictors of 6-month OS in patients with advanced HCC, and the nomogram model was established based on these factors. The area under the receiver operating characteristic curve (AUC) of the nomogram model for predicting the 3-month, 6-month, and 12-month OS rates were 0.821, 0.802, and 0.756, respectively. Moreover, in clinical practice, patients with true-positive survival benefit more than true-positive death, therefore, we selected 25% as the clinical decision threshold probability based on probability density functions (PDFs) and clinical utility curves (CUCs), which can distinguish approximately 92% of patients who died and 37% of patients who survived.

The nomogram model based on CD8+T cell counts and age accurately assessed the prognosis of patients with advanced HCC and suggested that high CD8+T cell levels are beneficial to the survival of patients with advanced HCC.

The aim of this study was to develop prognostic scores, including the tumor burden score (TBS) and albumin-bilirubin (ALBI) grade, for evaluating the outcomes of hepatocellular carcinoma (HCC) patients after radiofrequency ablation (RFA).

This retrospective study enrolled treatment-naïve HCC patients with BCLC 0-A who underwent RFA between January 2009 and December 2019. Regular follow-up was conducted after RFA to determine progression-free survival (PFS) and overall survival (OS). The patients were randomly allocated to the training or validation datasets in a 1:1 ratio. Preoperative prognostic scores were developed based on the results of multivariate analysis. The discriminatory ability of the scores was assessed using time-dependent AUC and compared with other models.

Serum alpha-fetoprotein (AFP) level and TBS were identified as independent prognostic factors for PFS, while serum AFP, TBS, and ALBI were identified as independent prognostic factors for OS in HCC patients after RFA. The time-dependent AUCs of the AFP-TBS score for the 1-, 3-, and 5-year PFS were 0.651, 0.667, and 0.620, respectively, in the training set, and 0.657, 0.687, and 0.704, respectively, in the validation set. For the 1-, 3-, and 5-year OS, the time-dependent AUCs were 0.680, 0.712, and 0.666, respectively, in the training set, and 0.712, 0.706 and 0.726 in the validation set for the AFP-TBS-ALBI score (ATA). The C-indices and AIC demonstrated that the scores provided better clinical benefits compared to other models.

The ATA/AT score, derived from clinical and objective laboratory variables, can assist in individually predicting the prognosis of HCC patients undergoing curative RFA.

Early-stage hepatocellular carcinoma could benefit from upfront liver resection (LR) or liver transplantation (LT), but the optimal strategy in terms of tumor-related outcomes is still debated. We compared the oncological outcomes of LR and LT for hepatocellular carcinoma, stratifying the study population into a low-, intermediate-, and high-risk class according to the risk of death at 5-y predicted by a previously developed prognostic model. The impact of tumor pathology on oncological outcomes of low- and intermediate-risk patients undergoing LR was investigated as a secondary outcome.

We performed a retrospective multicentric cohort study involving 2640 patients consecutively treated by LR or LT from 4 tertiary hepatobiliary and transplant centers between 2005 and 2015, focusing on patients amenable to both treatments upfront. Tumor-related survival and overall survival were compared under an intention-to-treat perspective.

We identified 468 LR and 579 LT candidates: 512 LT candidates underwent LT, whereas 68 (11.7%) dropped-out for tumor progression. Ninety-nine high-risk patients were selected from each treatment cohort after propensity score matching. Three and 5-y cumulative incidence of tumor-related death were 29.7% and 39.5% versus 17.2% and 18.3% for LR and LT group ( P = 0.039), respectively. Low-risk and intermediate-risk patients treated by LR and presenting satellite nodules and microvascular invasion had a significantly higher 5-y incidence of tumor-related death (29.2% versus 12.5%; P < 0.001).

High-risk patients showed significantly better intention-to-treat tumor-related survival after upfront LT rather than LR. Cancer-specific survival of low- and intermediate-risk LR patients was significantly impaired by unfavorable pathology, suggesting the application of ab-initio salvage LT in such scenarios.

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

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

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

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

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