Journal of Hepatocellular Carcinoma最新文献

{"title":"Transarterial Chemoembolization, Molecular Targeted Treatments, and Programmed Death-(Ligand)1 Inhibitors, for Hepatocellular Carcinoma with Lung Metastasis: A Retrospective Cohort Study.","authors":"Junjie Lu, Xiang Chen, Yongfa Liu, Yi Ding, Bo Li, Jin Yang, Wei Peng, Xiaoli Yang","doi":"10.2147/JHC.S509120","DOIUrl":"10.2147/JHC.S509120","url":null,"abstract":"<p><strong>Background: </strong>Treatment options for patients with hepatocellular carcinoma (HCC) and lung metastases are diverse, requiring a personalized approach. Current CNLC guidelines recommend systemic therapy and focal radiation, emphasizing the roles of molecular targeted treatments (MTT) and programmed death-(ligand)1 (PD-[L]1) inhibitors. However, the efficacy of combining TACE with these treatments remains uncertain.</p><p><strong>Purpose: </strong>To compare the efficacy and adverse reactions of TACE combined with MTT and PD-(L)1 versus MTT and (PD-[L]1) in patients with HCC and lung metastasis.</p><p><strong>Materials and methods: </strong>We retrospectively analyzed data from patients treated between January 2019 and May 2024 at the Affiliated Hospital of Southwest Medical University and West China Hospital of Sichuan University. Stabilized inverse probability weighting was employed to reduce bias. The primary outcome was overall survival (OS); secondary outcomes included progression-free survival (PFS) and objective response rate (ORR).</p><p><strong>Results: </strong>Among 167 patients, 141 received TACE, MTT, and PD-(L)1, while 26 received MTT and PD-(L)1. The median follow-up times were 28 and 29 months, respectively. After weighting, baseline characteristics were well balanced. The median OS was significantly longer in the TACE group (15 months) compared to the MTT group (8 months; p=0.023), and PFS was also longer (8 months vs 5 months; p=0.038). For liver lesions, ORR was 42.6% in the TACE group and 46.2% in the MTT group (p=0.73); for lung lesions, ORR was 26.2% and 19.2%, respectively (p=0.449). Safety profiles were similar, except for a higher incidence of rash in the MTT group.</p><p><strong>Conclusion: </strong>TACE combined with MTT and PD-(L)1 demonstrated better outcomes for patients with liver cancer and lung metastases compared to MTT and PD-(L)1 alone, without increasing complication rates, suggesting a promising first-line treatment option.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"1031-1041"},"PeriodicalIF":4.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12108955/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144159603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adjuvant Lenvatinib for High-Risk CNLC IIb/IIIa Hepatocellular Carcinoma After Curative Hepatectomy: A Prospective Exploratory Study.","authors":"Hui-Chuan Sun, Zhi-Yong Huang, Tianfu Wen, Lianxin Liu, Xiao-Dong Zhu, Erlei Zhang, Chuan Li, Xiaoyun Zhang, Jiabei Wang, Jia Fan, Jian Zhou","doi":"10.2147/JHC.S516478","DOIUrl":"10.2147/JHC.S516478","url":null,"abstract":"<p><strong>Objective: </strong>The risk of hepatocellular carcinoma (HCC) recurrence following surgical resection remains high, approaching 50%-70% at 5 years, with the highest risk occurring in the first year after resection. This study aimed to evaluate the efficacy and safety of lenvatinib as adjuvant therapy for HCC.</p><p><strong>Methods: </strong>In this open-label, single-arm, prospective, multicenter Phase II clinical study, a total of 51 hCC patients with China Liver Cancer (CNLC) stage IIb/IIIa (ie tumor number ≥ 4 or vascular invasion, equivalent to BCLC B/C) who underwent R0 resection 4-6 weeks after curative surgery were enrolled. Patients received lenvatinib for up to 12 months, at a dose of 8 mg/day for body weight < 60 kg, or 12 mg/day for ≥ 60 kg. Patients were followed up every 2 months for a median of 24.1 months.</p><p><strong>Results: </strong>The median recurrence-free survival (RFS) was 16.1 months, with a 12-month RFS rate of 60.4%, exceeding the historical rate of under 50% in similar high-risk populations. The 12-month overall survival (OS) rate was 93.6%, while median OS was not reached. Treatment-related adverse events (TRAEs) occurred in 88.0% of patients, with ≥ grade 3 TRAEs in 14.0%, including thrombocytopenia and proteinuria in 6.0% of patients each, and leukopenia, neutropenia, elevated aspartate aminotransferase, and elevated alanine aminotransferase in 2.0% of patients each. AEs leading to the interruption of lenvatinib occurred in 6.0% of patients, and dose reduction was required in 18% of patients. No deaths were observed.</p><p><strong>Conclusion: </strong>Lenvatinib may be an effective adjuvant therapy for patients with CNLC stage IIb/IIIa HCC after R0 hepatectomy. However, the findings are limited by the single-arm design and small patient cohort, necessitating larger randomized controlled trials for validation.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"1043-1056"},"PeriodicalIF":4.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12105637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combination of Hepatic Arterial Infusion Chemotherapy with Tyrosine Kinase Inhibitor Provides Better Survival in Advanced Hepatocellular Carcinoma Patients.","authors":"Chung-Wei Liu, Po-Ting Lin, Wei Teng, Wei-Ting Chen, Chung-Wei Su, Yi-Chung Hsieh, Chen-Chun Lin, Chun-Yen Lin, Shi-Ming Lin","doi":"10.2147/JHC.S502922","DOIUrl":"10.2147/JHC.S502922","url":null,"abstract":"<p><strong>Introduction: </strong>Hepatic arterial infusion chemotherapy (HAIC) and tyrosine kinase inhibitors (TKI) are widely used to treat unresectable hepatocellular carcinoma (HCC). This study investigated the benefits of combining TKI and HAIC in these patients.</p><p><strong>Methods: </strong>We retrospectively analyzed patients with unresectable HCC treated at Linkou Chang Gung Memorial Hospital between March 2009 and February 2022. The patients were categorized into two groups: HAIC combined with TKI therapy and HAIC alone. Kaplan-Meier analysis, Cox proportional hazards models, and propensity score matching were applied.</p><p><strong>Results: </strong>Among 130 patients, the combination therapy group showed significantly improved overall survival (OS) (20.2 versus 11.8 months, <i>p</i> = 0.000) and progression-free survival (PFS) (8.2 versus 3.6 months, <i>p</i> = 0.011) compared to the HAIC-only group. These advantages persisted after propensity score matching with improved OS (20.2 vs 12.9 months, <i>p</i> = 0.001) and extrahepatic PFS (12.4 vs 5.5 months, <i>p</i> = 0.008). Combination therapy improved PFS in the stage IV portal vein thrombosis (PVT) subgroup. TKI combination therapy, more than nine HAIC cycles, and post-HAIC transarterial chemoembolization (TACE) were independent predictors of improved OS.</p><p><strong>Conclusion: </strong>Combining HAIC with TKI therapy improves survival outcomes compared to HAIC alone in patients with unresectable HCC, especially in cases with extrahepatic spread and PVT. Sequential TACE following HAIC therapy further enhances survival benefits.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"1017-1029"},"PeriodicalIF":4.2,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12103852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144142713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics of Dynamic Contrast-Enhanced MRI for Predicting Radiation-Induced Hepatic Toxicity After Intensity Modulated Radiotherapy for Hepatocellular Carcinoma: A Machine Learning Predictive Model Based on the SHAP Methodology.","authors":"Fushuang Liu, Lijun Chen, Qiaoyuan Wu, Liqing Li, Jizhou Li, Tingshi Su, Jianxu Li, Shixiong Liang, Liping Qing","doi":"10.2147/JHC.S523448","DOIUrl":"10.2147/JHC.S523448","url":null,"abstract":"<p><strong>Objective: </strong>To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data, dosimetric parameters, and clinical data for predicting radiation-induced hepatic toxicity (RIHT) in patients with hepatocellular carcinoma (HCC) following intensity-modulated radiation therapy (IMRT).</p><p><strong>Methods: </strong>A retrospective analysis of 150 HCC patients was performed, with a 7:3 ratio used to divide the data into training and validation cohorts. Radiomic features from the original MRI sequences and Delta-radiomic features were extracted. Seven ML models based on radiomics were developed: logistic regression (LR), random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), decision tree (DT), and artificial neural network (ANN). The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis and calibration curves. Shapley additive explanations (SHAP) were employed to interpret the contribution of each variable and its risk threshold.</p><p><strong>Results: </strong>Original radiomic features and Delta-radiomic features were extracted from DCE-MRI images and filtered to generate Radiomics-scores and Delta-Radiomics-scores. These were then combined with independent risk factors (Body Mass Index (BMI), V5, and pre-Child-Pugh score(pre-CP)) identified through univariate and multivariate logistic regression and Spearman correlation analysis to construct the ML models. In the training cohort, the AUC values were 0.8651 for LR, 0.7004 for RF, 0.6349 for SVM, 0.6706 for XGBoost, 0.7341 for AdaBoost, 0.6806 for Decision Tree, and 0.6786 for ANN. The corresponding accuracies were 84.4%, 65.6%, 75.0%, 65.6%, 71.9%, 68.8%, and 71.9%, respectively. The validation cohort further confirmed the superiority of the LR model, which was selected as the optimal model. SHAP analysis revealed that Delta-radiomics made a substantial positive contribution to the model.</p><p><strong>Conclusion: </strong>The interpretable ML model based on radiomics provides a non-invasive tool for predicting RIHT in patients with HCC, demonstrating satisfactory discriminative performance.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"999-1015"},"PeriodicalIF":4.2,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12095435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI Radiomics to Predict Early Treatment Response to TACE Combined with Lenvatinib Plus a PD-1 Inhibitor for Hepatocellular Carcinoma with Portal Vein Tumor Thrombus.","authors":"Deyu Lu, Lingling Zhou, Ziyi Zuo, Zhao Zhang, Xiangwu Zheng, Jialu Weng, Zhijie Yu, Jiansong Ji, Jinglin Xia","doi":"10.2147/JHC.S513696","DOIUrl":"10.2147/JHC.S513696","url":null,"abstract":"<p><strong>Purpose: </strong>To develop and validate a predictor for early treatment response in hepatocellular carcinoma (HCC) patients accompanied by portal vein tumor thrombus (PVTT) undergoing transarterial chemoembolization (TACE), lenvatinib and a programmed cell death protein 1 (PD-1) inhibitor (TLP) therapy.</p><p><strong>Patients and methods: </strong>In this retrospective study, patients with HCC and PVTT from two institutions receiving triple TLP therapy were enrolled. Radiomics features derived from pretreatment contrast-enhanced MRI were curated using intraclass correlation coefficient (ICC), Student's <i>t</i>-test, least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) to ensure robust selection. Various machine learning (ML) algorithms were then used to construct the models. The meaningful clinical indicators were obtained via logistic regression analysis and ultimately integrated with radiomics features to develop a combined model. In addition, we used Shapley Additive exPlanation (SHAP) to clarify the model's operational dynamics.</p><p><strong>Results: </strong>Our study ultimately included 115 patients (7:3 randomization, 80 and 35 in the training and test cohorts, respectively) in total. No patients achieved complete remission, 47 achieved partial remission, 29 achieved stable disease, and 39 experienced disease progression. Among objective response rates (ORRs) and disease control rates (DCRs), 40.9% and 66.1% were reported. One of the four ML classifiers with optimal performance, namely random forest, was adopted as the radiomics model after testing. Regarding the performance assessment, the radiomics model's area under the curve (AUC) values reached 0.92 (95% CI: 0.86-0.97) and 0.79 (95% CI: 0.61-0.95), inferior to the combined model's AUCs of 0.95 (95% CI: 0.68-0.98) and 0.84 (95% CI: 0.91-0.99). Moreover, the SHAP plots illustrate the importance of global variables and the prediction process for individual samples.</p><p><strong>Conclusion: </strong>The model based on machine learning and radiomics showed favorable performance, and the operating mode was visualized through SHAP.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"985-998"},"PeriodicalIF":4.2,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12094907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Real-World Prevalence of Esophagogastric Varices, Bleeding, Emergency Room Visits, and Hospitalization Among Patients with Advanced Hepatocellular Carcinoma in the United States: A Retrospective Cohort Study.","authors":"Neehar D Parikh, Noh Jin Park, Michael Locker, Ishveen Chopra, Jason Yeaw, Shengsheng Yu","doi":"10.2147/JHC.S496618","DOIUrl":"10.2147/JHC.S496618","url":null,"abstract":"<p><strong>Purpose: </strong>Esophagogastric varices (EGV) and upper gastrointestinal bleeding are common and potentially fatal complications in patients with advanced hepatocellular carcinoma (aHCC). We aimed to evaluate the real-world prevalence of EGV among the aHCC population in the United States.</p><p><strong>Patients and methods: </strong>This retrospective cohort study utilized IQVIA's PharMetrics Plus Health Plans Claims database between January 1, 2016, and July 31, 2021 (study period). Adult patients with an aHCC diagnosis who initiated systemic therapies were included, while those with any secondary malignancies or prior liver transplant at baseline were excluded. The date of therapy initiation was the index date; baseline characteristics, prior procedures, and clinical events of interest were captured during the 12-month pre-index (baseline) period. Patients were followed for clinical outcomes (EGV- or bleeding-related emergency room [ER] visits or hospitalization) during the 6-month post-index period. Logistic regression was conducted to identify key predictors of post-index EGV- or bleeding-related ER visit or hospitalization.</p><p><strong>Results: </strong>904 patients with aHCC were included in the study (mean age: 61.3 years; 75.3% male). Sorafenib (423 patients, 46.8%) was the most prescribed aHCC treatment. During the entire study period, 458 patients (50.7%) underwent an esophagogastroduodenoscopy (EGD), of whom 209 (45.6%) had post-index EGV. Among 327 patients (36.2%) with a baseline EGD, 175 (53.5%) were diagnosed with EGV and 50 (15.3%) had variceal bleeding; 141 patients (15.6% of all patients) experienced ≥1 EGV- or bleeding-related ER visit or hospitalization post-index.</p><p><strong>Conclusion: </strong>There is a high prevalence of EGV in patients with aHCC. The presence of EGV, gastrointestinal bleeding, and portal hypertension-related comorbidities was associated with an increased risk of subsequent EGV- or bleeding-related ER visits or hospitalizations in patients with aHCC. Assessment and stratification of varices should be considered in patients with aHCC before initiating systemic therapies to inform treatment decisions.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"961-972"},"PeriodicalIF":4.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12087584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144101945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atezolizumab Plus Bevacizumab Combined with or without Transarterial Chemoembolization in the Treatment of Advanced Hepatocellular Carcinoma: A Single-Center Retrospective Study.","authors":"Jing Li, Yaowei Bai, Fu Xiong, Xiaocui Liu, Junwen Hu, Guilin Zhang, Jiayun Liu, Suyue Wu, Chuansheng Zheng, Xuefeng Kan","doi":"10.2147/JHC.S515453","DOIUrl":"10.2147/JHC.S515453","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to compare the efficacy and safety of atezolizumab plus bevacizumab (T+A) in combination with transarterial chemoembolization (TACE) (T+A+TACE) and T+A for patients with advanced hepatocellular carcinoma (HCC).</p><p><strong>Patients and methods: </strong>From December 2020 to August 2024, 83 patients with advanced HCC who received T+A+TACE treatment or T+A treatment in our hospital were included, and these patients were categorized into TACE+T+A group (n=52) and T+A group (n=31). The clinical outcomes between the two groups were analyzed and compared, and the prognostic factors that affected the efficacy were analyzed.</p><p><strong>Results: </strong>The median overall survival (OS) and median progression-free survival (PFS) in the T+A+TACE group were significantly longer than those of in the T+A group (OS: 22.8 vs 16.9 months, <i>P</i> = 0.015; PFS: 7.1 vs 4.9 months, <i>P</i> = 0.006). A significantly higher objective response rate (ORR) and disease control rate (DCR) that are based on the modified RECIST were achieved in the T+A+TACE group than those of in the T+A group (ORR: 51.9% vs 6.5%, <i>P</i> < 0.001; DCR: 88.5% vs 54.8%, <i>P</i> < 0.001). No significant differences in adverse events (AEs) were observed between the two groups (<i>P</i> > 0.05). The T+A+TACE treatment was identified as a protective factor for OS and PFS.</p><p><strong>Conclusion: </strong>TACE further improved the efficacy of T+A treatment for patients with advanced HCC, and it did not increase the incidence of AEs. T+A+TACE treatment is a promising treatment option for patients with advanced HCC.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"973-984"},"PeriodicalIF":4.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12090845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144110966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Intra-Operative Ablation-Specific Features Based on Ultrasound Fusion Imaging be Used to Predict Early Recurrence of Hepatocellular Carcinoma After Microwave Ablation: A Proof-of-Concept Study.","authors":"Haiyu Kang, Zhong Liu, Bin Huang, Shuang Liang, Kai Yang, Huahui Liu, Minhua Lu, Ronghua Yan, Xin Chen, Erjiao Xu","doi":"10.2147/JHC.S512926","DOIUrl":"10.2147/JHC.S512926","url":null,"abstract":"<p><strong>Purpose: </strong>Intra-operative factors are crucial to early recurrence of hepatocellular carcinoma (HCC) after microwave ablation (MWA), but few models have been developed based on intra-operative data to predict HCC recurrence after MWA. To quantify the intra-operative factors associated with MWA and establish an artificial intelligence (AI) model for predicting early recurrence of HCC after ablation based on contrast-enhanced ultrasound (CEUS) fusion imaging.</p><p><strong>Patients and methods: </strong>79 hCC patients, who underwent MWA with one-year follow-up and intraoperative CEUS fusion imaging assessment were retrospectively included. Three classifiers (support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP)) were developed to predict early HCC recurrence from CEUS fusion images. Thirteen ablation-specific features were defined and screened using minimum redundancy maximum relevance (mRMR), and leave-one-out cross-validation (LOOCV) was adopted for performance evaluation. Comparative analyses were conducted among classifiers and between a senior interventional doctor and the best classifier in terms of the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>Of 79 eligible patients who were included, 22 were in the early-recurrence (age 60.18 ± 10.97; 20 males) and 57 were in the non-early recurrence (age 58.81 ± 10.89; 50 males). Six features were selected out by mRMR for early recurrence prediction and AUCs of three models were 0.84 (95% CI: 0.74, 0.94) 0.79 (95% CI: 0.69, 0.89) and 0.77 (95% CI: 0.67, 0.88) (p = 0.20 and 0.23 for SVM and RF, respectively), which was significantly better than that achieved by senior doctor's assessment (AUC, 0.56; 95% CI: 0.44, 0.68; p = 0.002 for MLP).</p><p><strong>Conclusion: </strong>The prediction model based on ablation-specific features using intra-operative ultrasound fusion imaging data was feasible to predict early recurrence of HCC after MWA and showed great potential in guiding the real-time adjustment of the intra-operative ablation strategy so as to achieve precise ablation.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"949-960"},"PeriodicalIF":4.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding ARF4 and EIF5B-Based Prognostic Signatures and Immune Landscape Following Insufficient Radiofrequency Ablation in Hepatocellular Carcinoma: Through Multi-Omics and Experimental Validation.","authors":"Yixin Zhang, Yongpan Lu, Sui Zheng, Wanrong Luo, Min Tan, Baoming Luo","doi":"10.2147/JHC.S517528","DOIUrl":"https://doi.org/10.2147/JHC.S517528","url":null,"abstract":"<p><strong>Background: </strong>Radiofrequency ablation (RFA) is pivotal in non-surgical hepatocellular carcinoma (HCC) treatments but poses a high postoperative recurrence risk, exceeding conventional surgeries. Insufficient tumor ablation may trigger immune responses, promoting tumor progression locally. Hence, this study seeks to pinpoint immune biomarkers to improve treatment precision and prognostic accuracy for RFA patients.</p><p><strong>Methods: </strong>The study utilized data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and The International Cancer Genome Consortium (ICGC) database to investigate novel immune biomarkers influencing the prognosis of patients undergoing insufficient radiofrequency ablation (IRFA). Subsequently, an IRFA model was developed and validated. Then, we employed Quantitative real time-Polymerase Chain Reaction (qPCR), Western blotting (WB), immunohistochemistry (IHC), and immunofluorescence (IF) techniques on human HCC cell lines and IRFA animal model to validate ADP-ribosylation factor 4 (ARF4) and Eukaryotic translation initiation factor 5B (EIF5B) expression and prognostic relevance post-IRFA. In addition, knockdown of ARF4 and EIF5B was performed to evaluate cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT). Finally, transcriptome sequencing was subsequently performed to confirm and extend our findings.</p><p><strong>Results: </strong>ARF4 and EIF5B were identified as critical immune targets affecting IRFA patient prognosis, forming the basis of an IRFA risk model. High-risk scores in this model correlated with poorer prognoses and reduced responsiveness to immune checkpoint inhibitors (ICIs) across multiple cancer types. Experimental validations confirmed the protective role of ARF4 and EIF5B in IRFA outcomes, while knockdown experiments suggested their involvement in promoting cell proliferation, migration, invasion, and EMT in IRFA models, potentially through pathways like P53 and Transforming Growth Factor Beta(TGF-β) signaling pathway activation as indicated by transcriptome sequencing.</p><p><strong>Conclusion: </strong>ARF4 and EIF5B have demonstrated promising potential as biomarkers influencing patient prognosis following RFA in HCC. These findings suggest they could serve as viable therapeutic targets aimed at mitigating HCC recurrence post-RFA.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"909-931"},"PeriodicalIF":4.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144078445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Cohort Study.","authors":"Ke Su, Xin Liu, Yue-Can Zeng, Junnv Xu, Han Li, Heran Wang, Shanshan Du, Huadong Wang, Jinbo Yue, Yong Yin, Zhenjiang Li","doi":"10.2147/JHC.S521378","DOIUrl":"https://doi.org/10.2147/JHC.S521378","url":null,"abstract":"<p><strong>Background: </strong>This study was conducted to assess the efficacy and safety of magnetic resonance (MR)-guided hypofractionated radiotherapy in patients with unresectable hepatocellular carcinoma (HCC). Machine learning-based radiomics was utilized to predict responses in these patients.</p><p><strong>Methods: </strong>This retrospective study included 118 hCC patients who received MR-guided hypofractionated radiotherapy. The primary study endpoint was the objective response rate (ORR). Radiomics features were based on the gross tumor volume (GTV). K-means clustering was performed to differentiate cancer subtypes based on radiomics. Nine radiomics-utilizing machine learning models were built and validated internally through 5-fold cross-validation.</p><p><strong>Results: </strong>The ORR, median progression-free survival (mPFS), and median overall survival (mOS) were 54.4%, 21.7 months, and 40.7 months, respectively. No patient experienced Grade 3/4 adverse events. 1130 radiomics features were extracted from the GTV, of which 7 were included for further analysis. K-means clustering identified 2 subtypes based on the selected features. Subtype 1 had significantly higher response, longer mPFS, and longer mOS than Subtype 2. In both internal and external validations, the multi-layer perceptron (MLP) model demonstrated superior predictive performance for response, achieving a receiver operating characteristic-area under the curve (ROC-AUC) of 0.804 and 0.842, respectively.</p><p><strong>Conclusion: </strong>MR-guided radiotherapy was proven to be effective and safe for HCC. The machine learning radiomics model developed in this study could accurately predict the response of radiotherapy-treated inoperable HCC.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"933-947"},"PeriodicalIF":4.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144078447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}