Early-stage Hepatocellular Carcinoma Patients Research Articles

Prediction of microvascular invasion in hepatocellular carcinoma patients with MRI radiomics based on susceptibility weighted imaging and T2-weighted imaging.

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

Partial Hepatectomy and Ablation for Survival of Early-Stage Hepatocellular Carcinoma Patients: A Bayesian Emulation Analysis.

Partial hepatectomy and ablation therapy are two widely used surgical procedures for localized early-stage hepatocellular carcinoma (HCC) patients. This article aimed to evaluate their relative effectiveness in terms of overall survival. An emulation analysis approach was first developed based on the Bayesian technique. We estimated propensity scores via Bayesian logistic regression and adopted a weighted Bayesian Weibull accelerated failure time (AFT) model incorporating prior information contained in the published literature. With the Surveillance, Epidemiology, and End Results (SEER)-Medicare data, an emulated target trial with rigorously defined inclusion/exclusion criteria and treatment regimens for early-stage HCC patients over 66 years old was developed. For the main cohort with tumor size less than or equal to 5 cm, a total of 1146 patients were enrolled in the emulated trial, with 301 and 845 in the partial hepatectomy and ablation arms, respectively. The analysis suggested ablation to be significantly associated with inferior overall survival (hazard ratio [HR] = 1.35; 95% credible interval [CrI]: 1.14, 1.60). For the subgroup with tumor size less than or equal to 3 cm, there was no significant difference in overall survival between the two arms (HR = 1.15; 95% CrI: 0.88, 1.52). Overall, the comparative treatment effect of ablation and partial hepatectomy on survival remains inconclusive. This finding may provide further insight into HCC clinical treatment.

Prognostic significance of pan-immune-inflammation value in hepatocellular carcinoma treated by curative radiofrequency ablation: potential role for individualized adjuvant systemic treatment

Background This study aimed to explore the prognostic role of pan-immune-inflammation value (PIV) and develop a new risk model to guide individualized adjuvant systemic treatment following radiofrequency ablation (RFA) for early-stage hepatocellular carcinoma (HCC). Materials and methods Patients with early-stage HCC treated by RFA were randomly divided into training cohort A (n = 65) and testing cohort B (n = 68). Another 265 counterparts were enrolled into external validating cohort C. Various immune-inflammatory biomarkers (IIBs) were screened in cohort A. Prognostic role of PIV was evaluated and validated in cohort B and C, respectively. A nomogram risk model was built in cohort C and validated in pooled cohort D. Clinical benefits of adjuvant anti-angiogenesis therapy plus immune checkpoint inhibitor (AA-ICI) following RFA was assessed in low- and high-risk groups. Results The cutoff point of PIV was 120. High PIV was an independent predictor of unfavorable recurrence-free survival (RFS) and overall survival (OS). RFS and OS rates of patients with high PIV were significantly lower than those with low PIV both in cohort B (P RFS=0.016, P OS=0.011) and C (P RFS<0.001, P OS<0.001). The nomogram model based on PIV, tumor number and BCLC staging performed well in risk stratification in external validating cohort C. Adjuvant AA-ICI treatment showed an added benefit in OS (p = 0.011) for high-risk patients. Conclusions PIV is a feasible independent prognostic factor for RFS and OS in early-stage HCC patients who received curative RFA. The proposed PIV-based nomogram risk model could help clinicians identify high-risk patients and tailor adjuvant systemic treatment and disease follow-up scheme.

Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation.

To analyze the risk factors affecting recurrence in early-stage hepatocellular carcinoma (HCC) patients treated with ablation and then establish a nomogram to provide a clear and accessible representation of the patients' recurrence risk. Collect demographic and clinical data of 898 early-stage HCC patients who underwent ablation treatment at Beijing You'an Hospital, affiliated with Capital Medical University from January 2014 to December 2022. Patients admitted from 2014 to 2018 were included in the training cohort, while 2019 to 2022 were in the validation cohort. Lasso and Cox regression was used to screen independent risk factors for HCC patients recurrence, and a nomogram was then constructed based on the screened factors. Age, gender, Barcelona Clinic Liver Cancer (BCLC) stage, tumor size, globulin (Glob) and γ-glutamyl transpeptidase (γ-GT) were finally incorporated in the nomogram for predicting the recurrence-free survival (RFS) of patients. We further confirmed that the nomogram has optimal discrimination, consistency and clinical utility by the C-index, Receiver Operating Characteristic Curve (ROC), calibration curve and Decision Curve Analysis (DCA). Moreover, we divided the patients into different risk groups and found that the nomogram can effectively identify the high recurrence risk patients by the Kaplan-Meier curves. This study developed a nomogram using Lasso-Cox regression to predict RFS in early-stage HCC patients following ablation, aiding clinicians in identifying high-risk groups for personalized follow-up treatments.

A Novel Nomogram to Predict Prognosis in Elderly Early-Stage Hepatocellular Carcinoma Patients After Ablation Therapy.

Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer. Early diagnosis is crucial for improving prognosis. Elderly HCC patients often have underlying liver diseases such as chronic hepatitis and cirrhosis, leading to impaired liver function and suboptimal liver reserve. Radiofrequency ablation (RFA) has rapidly become one of the most important methods for treating early-stage hepatocellular carcinoma (EHCC) due to its advantages, including minimal trauma, short operation time, less intraoperative bleeding, quick postoperative recovery, cost-effectiveness, and few postoperative-complications. However, the prognostic model for early recurrence after local ablation in elderly EHCC patients has not been widely evaluated. We have developed a prognostic model for the recurrence of local RFA in elderly EHCC patients. This is expected to provide a new early warning system for preventing early recurrence in elderly EHCC patients, prolonging patient's life, and improving overall quality of life. In this study, we included 661 EHCC patients who underwent local ablation, dividing them into a Primary cohort and a Validation cohort in a 7:3 ratio. We characterized the cohorts and utilized the primary cohort to develop a prognostic nomogram model for recurrence after local ablation in elderly EHCC patients. Additionally, the validation cohort was used to assess the potential of the nomogram as a non-invasive biomarker for post-ablation recurrence in EHCC. The user-friendly nomogram incorporates common clinical variables including gender, BCLC stage, tumor number, tumor size, red blood cell (RBC), gamma-glutamyl transferase (GGT), and prothrombin time activity (PTA). The nomogram constructed using the identified seven variables exhibits robust discriminatory capabilities, favorable predictive performance, and noteworthy clinical utility. We developed a user-friendly nomogram based on the BCLC stage classification, which may provide prognostic assessments for elderly EHCC patients at 1, 3, and 5 years post-RFA.

Conventional and machine-learning based risk score for patients with early-stage hepatocellular carcinoma.

The performance of machine-learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. The study retrospectively enrolled 1411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-to-monocyte ratio, and albumin bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATS-INF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Epidemiology of Hepatocellular Carcinoma in Taiwan.

Hepatocellular carcinoma (HCC) is a major contributor to the world's cancer burden. Understanding the HCC incidence rate in Taiwan is thus an interesting avenue of research. From an NHI database, those patients who had been newly diagnosed with HCC and who had been listed on a registry in a catastrophic illness dataset during the years 2013-2021 were enrolled in this study. Antineoplastic agent usage and comorbidities were also studied. The incidence rate of HCC decreased from 57.77 to 44.95 in 100,000 from 2013 to 2021. The average age of patients with HCC increased from 65.54 years old with a CCI score of 4.98 in 2013 to 67.92 years old with a CCI score of 5.49 in 2021. Among these HCC patients, the patients under antineoplastic agent treatment decreased from 53.47% to 31.41% from 2013 to 2021. The presence of comorbidities in HCC patients was about 55.77-83.01% with mild liver disease and 29.93-37.30% with diabetes (without complications) in the period 2013-2021. The incidence rate of HCC slightly decreased in Taiwan. Due to antineoplastic agent usage decreasing over time, these results may indicate that more early-stage HCC patients detected in recent years were mainly treated with surgeries.

Alterations in serum metabolic profiles of early-stage hepatocellular carcinoma patients after radiofrequency ablation therapy

ObjectiveTo investigate the alterations in serum metabolic profiles and early-stage hepatocellular carcinoma (HCC) patient characteristics after radiofrequency ablation (RFA) therapy. This evaluation aimed to assess treatment effectiveness and identify potential novel approaches and targets for HCC treatment and prognosis monitoring. MethodsUntargeted metabolomics technology was employed to analyze serum metabolic profiles in healthy volunteer controls (NCs) and early stage HCC patients before and after RFA therapy. Additionally, Human Metabolome Database and Kyoto Encyclopedia of Genes and Genomes database were used to identify the differential metabolites (DMs) and metabolic pathways. Cystoscape was utilized to construct DM gene networks. Amino acid analyses were performed to validate our findings. ResultsWe identified 11, 14, and six DMs between the NC and HCC groups, HCC patients before and after RFA therapy, and post-RFA HCC and NC groups, respectively. The expression levels of these DMs, particularly those of amino acids and lipids, significantly changed. Compared with the NC group, higher levels of L-tyrosine, aspartate, and 18-oxo-oleate were observed in HCC patients, which were significantly reduced in patients after RFA therapy. Meanwhile, HCC patients after RFA therapy had increased levels of L-arginine, phosphatidic acid (20:3), and lysophosphatidyl choline (LPC) (20:4) compared to those before therapy, while their levels before therapy were lower than those of NC. Moreover, most metabolites in the post-RFA and NC groups showed no significant changes in expression, except for L-tyrosine and LPC (16:0). These metabolites could potentially serve as characteristic factors of early-stage HCC patients after RFA therapy. Joint pathway analysis revealed striking changes, mainly in phenylalanine, tyrosine, and tryptophan biosynthesis; alanine, aspartate, and glutamate metabolism; and arginine and aminoacyl-tRNA biosynthesis. Bioinformatics analysis of publicly available data preliminarily identified 187 DM-related metabolic enzymes. ConclusionOur study proposed novel targets for early-stage HCC treatment, laying the groundwork for improving treatment efficacy and prognosis of early-stage HCC patients.

Evaluation of the effectiveness of surgical resection and ablation for the treatment of early-stage hepatocellular carcinoma: A retrospective cohort study.

The optimal treatment strategy for early-stage hepatocellular carcinoma (HCC) remains controversial, specifically in regard to surgical resection (SR) and ablation. The aim of this study was to investigate the impact of SR and ablation on recurrence and prognosis in early-stage HCC patients, to optimize treatment strategies and improve long-term survival. A retrospective analysis was conducted on 801 patients diagnosed with Barcelona Clinic Liver Cancer (BCLC) stage 0/A HCC and treated with SR or ablation between January 2015 and December 2019. The effectiveness and complications of both treatments were analyzed, and patients were followed up to measure recurrence and survival. Propensity score matching (PSM) was employed to increase comparability between the two groups. The Kaplan-Meier method was used to analyze recurrence and survival, and a Cox risk proportional hazard model was used to identify risk factors that affect recurrence and surviva. Before PSM, the overall survival (OS) rates were similar in both groups, with recurrence-free survival (RFS) rates better in the SR group than in the ablation group. After PSM, there was no significant difference in OS between the two groups. However, the RFS rates were significantly better in the SR group than in the ablation group. The ablation group exhibited superior outcomes compared to the SR group, with shorter treatment times, reduced bleeding, shorter hospital stays, and lower hospital costs. Concerning the location of the HCC within the liver, comparable efficacy was observed between SR and ablation for disease located in the noncentral region or left lobe. However, for HCCs located in the central region or right lobe of the liver, SR was more effective than ablation. This study revealed no significant difference in OS between SR and ablation for early-stage HCC, with SR providing better RFS and ablation demonstrating better safety profiles and lower hospital costs. These findings offer valuable insights for clinicians in determining optimal treatment strategies for early-stage HCC patients, particularly in terms of balancing efficacy, safety, and cost considerations.

MiR-215-5p Plays a Key Role in Suppressing Vascular Invasion and Recurrence in Hepatocellular Carcinoma by Blocking Vasculogenic Mimicry.

This research explores the significance of miR-215-5p and vasculogenic mimicry (VM) in forecasting the prognosis for hepatocellular carcinoma (HCC). We analyzed HCC-associated miRNA expression profiles using data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Samples included tissue and blood from 80 early-stage HCC patients and serum from 120 healthy individuals. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was employed to measure miR-215-5p and zinc finger E-box binding homeobox 2 (ZEB2) gene expressions. Hematoxylin and eosin (H&E) and CD34/Periodic Acid-Schiff (PAS) double staining assessed VM presence in HCC tissue sections. Bioinformatics tools predicted interactions between miR-215-5p and ZEB2, confirmed through luciferase reporter assays. We also examined the impact of miR-215-5p or ZEB2 overexpression on HCC cell invasion, migration, and VM formation using scratch, Transwell invasion assays, and Matrigel 3D cultures. Bioinformatics analysis indicated that miR-215-5p was under-expressed in HCC, particularly in cases with vascular invasion, which correlated with worse patient outcomes. In contrast, ZEB2, targeted by miR-215-5p, was overexpressed in HCC. RT-qPCR validated these expression patterns in HCC tissues. Among the HCC patients, 38 were VM positive and 42 VM negative. Logistic regression highlighted a negative correlation between miR-215-5p levels and VM positivity in HCC tissues and a positive correlation for ZEB2 with VM positivity and tumor vascular invasion. Lower miR-215-5p levels were linked to increased HCC recurrence and metastasis. Both bioinformatics analysis and luciferase assays demonstrated a direct interaction between miR-215-5p and ZEB2. Enhancing miR-215-5p levels reduced ZEB2 expression, consequently diminishing invasion, migration, and VM formation of the HCC cells in vitro. miR-215-5p expression inversely correlates with VM occurrence in HCC tissues, while ZEB2 expression shows a direct correlation. By targeting ZEB2, miR-215-5p may hinder VM in HCC tissues, helping to prevent vascular invasion and HCC recurrence. Thus, miR-215-5p emerges as a vital prognostic indicator for predicting vascular invasion and recurrence in HCC.

Volatile organic compounds for diagnosis of early hepatocellular carcinoma in at-risk patients

BackgroundVolatile organic compounds (VOCs) have been shown as promising biomarkers for hepatocellular carcinoma (HCC) diagnosis. We aimed to investigate the performance of VOCs for diagnosing early-stage HCC in patients at-risk for HCC. MethodsVOCs were identified in exhaled breath samples collected from 87 early-stage HCC patients, 90 cirrhotic patients, and 72 HBV-infected patients using thermal desorption-gas chromatography/field-asymmetric ion mobility spectrometry. The VOC levels were compared between the three groups. An association between VOCs and HCC was determined using logistic regression analysis. Diagnostic performance of VOCs was estimated using the AUROC and compared to serum alpha-fetoprotein (AFP). ResultsThe levels of acetone monomer, dimethyl sulfide, 1,4-pentadiene, isopropyl alcohol, and acetone dimer were significantly different between the three groups. After adjusting for liver function test and AFP, acetone dimer was significantly associated with HCC. Acetone dimer significantly outperformed AFP with 86.2 % vs. 61.2 % sensitivity, 87.6 % vs. 66.2 % specificity, 86.9 % vs. 63.5 % for accuracy, and AUROC of 0.908 vs. 0.665, p = 0.007, <0.001, <0.001, and 0.001, respectively, for differentiating between HCC and cirrhosis. ConclusionAcetone showed a better performance than AFP for diagnosing early HCC in at-risk patients. Further studies to validate the utility of VOCs as an HCC surveillance tool are needed.

Adjuvant holmium-166 radioembolization after radiofrequency ablation in early-stage hepatocellular carcinoma patients: a dose-finding study (HORA EST HCC trial)

PurposeThe aim of this study was to investigate the biodistribution of (super-)selective trans-arterial radioembolization (TARE) with holmium-166 microspheres (166Ho-MS), when administered as adjuvant therapy after RFA of HCC 2–5 cm. The objective was to establish a treatment volume absorbed dose that results in an absorbed dose of ≥ 120 Gy on the hyperemic zone around the ablation necrosis (i.e., target volume).MethodsIn this multicenter, prospective dose-escalation study in BCLC early stage HCC patients with lesions 2–5 cm, RFA was followed by (super-)selective infusion of 166Ho-MS on day 5–10 after RFA. Dose distribution within the treatment volume was based on SPECT-CT. Cohorts of up to 10 patients were treated with an incremental dose (60 Gy, 90 Gy, 120 Gy) of 166Ho-MS to the treatment volume. The primary endpoint was to obtain a target volume dose of ≥ 120 Gy in 9/10 patients within a cohort.ResultsTwelve patients were treated (male 10; median age, 66.5 years (IQR, [64.3–71.7])) with a median tumor diameter of 2.7 cm (IQR, [2.1–4.0]). At a treatment volume absorbed dose of 90 Gy, the primary endpoint was met with a median absorbed target volume dose of 138 Gy (IQR, [127–145]). No local recurrences were found within 1-year follow-up.ConclusionAdjuvant (super-)selective infusion of 166Ho-MS after RFA for the treatment of HCC can be administered safely at a dose of 90 Gy to the treatment volume while reaching a dose of ≥ 120 Gy to the target volume and may be a favorable adjuvant therapy for HCC lesions 2–5 cm.Trial registrationClinicaltrials.gov NCT03437382. (registered: 19-02-2018)

A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.

This study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS). The research included 881 early-stage HCC patients treated at Beijing You'an Hospital, affiliated with Capital Medical University, from 2014 to 2022.A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). The study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram's predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients. The established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.

A Transformer-Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation.

We aimed to develop a Transformer-based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA. A Swin Transformer DL network was trained to establish associations between magnetic resonance imaging (MRI) datasets and the ground truth of microvascular invasion (MVI) based on 696 surgical resection (SR) patients with solitary HCC ≤3 cm, and was validated in an external cohort (n = 180). The multiphase MRI-based DL risk outputs using an optimal threshold of .5 was employed as a MVI classifier for prognosis stratification in the RFA cohort (n = 180). Over 90% of all enrolled patients exhibited hepatitis B virus infection. Liver cirrhosis was significantly more prevalent in the RFA cohort compared to the SR cohort (72.2% vs. 44.1%, p < .001). The MVI risk outputs exhibited good performance (area under the curve values = .938 and .883) for predicting MVI in the training and validation cohort, respectively. The RFA patients at high risk of MVI classified by the MVI classifier demonstrated significantly lower recurrence-free survival (RFS) and overall survival rates at 1, 3 and 5 years compared to those classified as low risk (p < .001). Multivariate cox regression modelling of a-fetoprotein > 20 ng/mL [hazard ratio (HR) = 1.53; 95% confidence interval (95% CI): 1.02-2.33, p = .047], high risk of MVI (HR = 3.76; 95% CI: 2.40-5.88, p < .001) and unfavourable tumour location (HR = 2.15; 95% CI: 1.40-3.29, p = .001) yielded a c-index of .731 (bootstrapped 95% CI: .667-.778) for evaluating RFS after RFA. Among the three risk factors, MVI was the most powerful predictor for intrahepatic distance recurrence. The proposed MVI classifier can serve as a valuable imaging biomarker for prognostic stratification in early-stage HCC patients undergoing RFA.

Ablation alone is noninferior to radiotherapy plus ablation in the patients with early-stage hepatocellular carcinoma: a population-based study

Recently, the efficacy of two low-invasive treatments, ablation, and radiotherapy, has been fully compared for the patients with the early-stage hepatocellular carcinoma (HCC). However, the comparison between radiotherapy plus ablation and ablation alone has been less frequently reported. Data from the Surveillance, Epidemiology, and End Results (SEER) database were searched for early-stage HCC patients treated with ablation plus radiotherapy or ablation alone. The outcome measures were overall survival (OS) and cancer-specific survival (CSS). The propensity score matching (PSM) was used to reduce selection bias. We included 240 and 6619 patients in the radiotherapy plus ablation group and ablation group before the PSM. After PSM, 240 pairs of patients were included. The median OS (mOS) and median CSS (mCSS) of patients receiving ablation alone were longer than that of receiving radiotherapy plus ablation (mOS: 47 vs. 34 months, P = 0.019; mCSS: 77 vs. 40 months, P = 0.018, after PSM) before and after PSM. The multivariate analysis indicated that radiotherapy plus ablation independent risk factor for OS and CSS before PSM, but the significance disappeared after PSM. The detailed subgroup analyses indicated ablation alone brought more benefit in very early-stage HCC and older patients. In addition, we found different types of radiotherapy might lead to different outcomes when combined with ablation. In conclusion, ablation alone is noninferior to radiotherapy plus ablation in patients with early-stage HCC. The additional radiation prior to ablation may bring survival benefits in the patients with higher tumor stage. However, due to the risk of selection bias in that study, the results should be interpreted cautiously.

Imaging Delay Following Liver-Directed Therapy Increases Progression Risk in Early- to Intermediate-Stage Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related deaths in the world. Patients with early-stage HCC are treated with liver-directed therapies to bridge or downstage for liver transplantation (LT). In this study, the impact of HCC care delay on HCC progression among early-stage patients was investigated. Early-stage HCC patients undergoing their first cycle of liver-directed therapy (LDT) for bridge/downstaging to LT between 04/2016 and 04/2022 were retrospectively analyzed. Baseline variables were analyzed for risk of disease progression and time to progression (TTP). HCC care delay was determined by the number of rescheduled appointments related to HCC care. The study cohort consisted of 316 patients who received first-cycle LDT. The HCC care no-show rate was associated with TTP (p = 0.004), while the overall no-show rate was not (p = 0.242). The HCC care no-show rate and HCC care delay were further expanded as no-show rates and rescheduled appointments for imaging, laboratory, and office visits, respectively. More than 60% of patients experienced HCC care delay for imaging and laboratory appointments compared to just 8% for office visits. Multivariate analysis revealed that HCC-specific no-show rates and HCC care delay for imaging (p < 0.001) were both independently associated with TTP, highlighting the importance of minimizing delays in early-stage HCC imaging surveillance to reduce disease progression risk.

From Conversion to Resection for Unresectable Hepatocellular Carcinoma: A Review of the Latest Strategies.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in China, accounting for the majority of primary liver cancer cases. Liver resection is the preferred curative method for early-stage HCC. However, up to 80-85% of patients have already missed the opportunity of radical surgery due to tumor advances at the time of consultation. Conversion therapies are a series of medications and treatments for initially inoperable patients. For early-stage unresectable HCC (uHCC) patients, conversion therapies are designed to meet surgical requirements by increasing the volume of the residual liver. Meanwhile, for advanced cases, conversion therapies strive for tumor shrinkage and down-staging, creating the opportunity for liver resection or liver transplantation. This review summarizes the latest advances in conversion therapies and highlights their potential for improving the survival benefit of patients with uHCC.

ACTB may serve as a predictive marker for the efficacy of lenvatinib in patients with HBV-related early-stage hepatocellular carcinoma following partial hepatectomy: a retrospective cohort study.

The lack of effective biomarkers for the treatment of postoperative recurrence in hepatocellular carcinoma (HCC) persists despite lenvatinib therapy. This study aims to identify beta-actin (ACTB) as a predictive biomarker for lenvatinib that can facilitate individualized treatment for HCC. This retrospective study included a subset of patients with HCC who underwent partial hepatectomy, with some receiving postoperative lenvatinib treatment and others not receiving lenvatinib treatment. A propensity score matching (PSM) analysis of patients who underwent treatment with or without lenvatinib following HCC partial hepatectomy was performed. Immunohistochemistry was employed to determine the levels of ACTB expression in HCC samples obtained from matched patients (n=225) enrolled in this study. The X-Tile was employed to determine the optimal cut-off point of ACTB levels for predicting time to recurrence (TTR). To assess the correlation between ACTB levels and lenvatinib efficacy, a subgroup analysis of TTR was conducted. A Cox regression model with an interaction term was utilized to assess the predictive significance of the model. Subsequently, a nomogram was developed and its discriminative ability and predictive accuracy were assessed using the concordance index (C-index) and calibration curve. For the investigation of the ACTB expression, HCC and para-tumoral normal tissues were employed. The patient-derived xenograft (PDX) model was utilized to validate the correlation between ACTB levels and lenvatinib responsiveness. After PSM, a total of 76 patients who underwent postoperative lenvatinib treatment were included in the analysis, with a median TTR of 24.35 months. Early-stage HCC patients with lower levels of ACTB exhibited a more favorable response to lenvatinib therapy compared to those with higher levels. The reduced expression of ACTB was indicative of the benefits of lenvatinib, as opposed to higher levels {hazard ratio (HR) =0.243 [95% confidence interval (CI): 0.096-0.619], P<0.001, P value for interaction =0.014}. In approximately 81.8% of cases involving HCC patients, there was an observed increase in the expression of ACTB. Multivariate analysis of the lenvatinib cohort revealed Child-Pugh [HR =5.416 (95% CI: 1.390-21.104), P=0.015], Barcelona Clinic Liver Cancer (BCLC) stage [HR =2.508 (95% CI: 1.116-5.639), P=0.026], and ACTB [HR =5.879 (95% CI: 2.424-14.259), P<0.001] score as independent factors for TTR, and all were included in the nomogram. The survival probability based on the calibration curve showed that the prediction of the nomogram was in good agreement with the actual observation. The C-index of the nomogram for predicting survival was 0.76 (95% CI: 0.71-0.84). Moreover, the PDXs derived from tumors exhibiting low levels of ACTB expression demonstrated a heightened sensitivity to lenvatinib treatment. In patients with tumors treated with lenvatinib, low ACTB expression can predict a lower risk of recurrence. The validation of this potential biomarker in independent cohorts is necessary prior to its implementation for precision treatment stratification in patients undergoing partial hepatectomy for early-stage HCC.

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Radiomics-guided prognostic assessment of early-stage hepatocellular carcinoma recurrence post-radical resection.

The prognosis of early-stage hepatocellular carcinoma (HCC) patients after radical resection has received widespread attention, but reliable prediction methods are lacking. Radiomics derived from enhanced computed tomography (CT) imaging offers a potential avenue for practical prognostication in HCC patients. We recruited early-stage HCC patients undergoing radical resection. Statistical analyses were performed to identify clinicopathological and radiomic features linked to recurrence. Clinical, radiomic, and combined models (incorporating clinicopathological and radiomic features) were built using four algorithms. The performance of these models was scrutinized via fivefold cross-validation, with evaluation metrics including the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) being calculated and compared. Ultimately, an integrated nomogram was devised by combining independent clinicopathological predictors with the Radscore. From January 2016 through December 2020, HCC recurrence was observed in 167 cases (64.5%), with a median time to recurrence of 26.7months following initial resection. Combined models outperformed those solely relying on clinicopathological or radiomic features. Notably, among the combined models, those employing support vector machine (SVM) algorithms exhibited the most promising predictive outcomes (AUC: 0.840 (95% Confidence interval (CI): [0.696, 0.984]), ACC: 0.805, SEN: 0.849, SPE: 0.733). Hepatitis B infection, tumour size > 5cm, and alpha-fetoprotein (AFP) > 400ng/mL were identified as independent recurrence predictors and were subsequently amalgamated with the Radscore to create a visually intuitive nomogram, delivering robust and reliable predictive performance. Machine learning models amalgamating clinicopathological and radiomic features provide a valuable tool for clinicians to predict postoperative HCC recurrence, thereby informing early preventative strategies.