Journal of Hepatocellular Carcinoma最新文献

Hepatocellular carcinoma is the fifth leading cancer in related diseases most commonly in men and women. The curative treatments of liver cancer are short-listed, associated with toxicities and therapeutically. Emerging nanotechnologies exhibited the possibility to treat or target liver cancer. Over the years, to phytosome solid lipid nanoparticles, gold, silver, liposomes, and phospholipid nanoparticles have been produced for liver cancer therapy, and some evidence of their effectiveness has been established. Ideas are limited to the laboratory scale, and in order to develop active targeting of nanomedicine for the clinical aspects, they must be extended to a larger scale. Thus, the current review focuses on previously and presently published research on the creation of phytosomal nanocarriers for the treatment of hepatocellular carcinoma. In hepatocellular carcinoma (HCC), phytosomal nanotherapeutics improve the targeted delivery and bioavailability of phytochemicals to tumor cells, thereby reducing systemic toxicity and increasing therapeutic efficacy. In order to address the intricate molecular processes implicated in HCC, this strategy is essential.

Purpose: To construct a 2.5-dimensional (2.5D) CT radiomics-based deep learning (DL) model to predict early postoperative recurrence of hepatocellular carcinoma (HCC).

Patients and methods: We retrospectively analyzed the data of patients who underwent HCC resection at 2 centers. The 232 patients from center 1 were randomly divided into the training (162 patients) and internal validation cohorts (70 patients); 91 patients from center 2 formed the external validation cohort. We developed a 2.5D DL model based on a central 2D image with the maximum tumor cross-section and adjacent slices. Multiple views (transverse, sagittal, and coronal) and phases (arterial, plain, and portal) were incorporated. Multi-instance learning techniques were applied to the extracted data; the resulting comprehensive feature set was modeled using Logistic Regression, RandomForest, ExtraTrees, XGBoost, and LightGBM, with 5-fold cross validation and hyperparameter optimization with Grid-search. Receiver operating characteristic curves, calibration curves, DeLong test, and decision curve analysis were used to evaluate model performance.

Results: The 2.5D DL model performed well in the training (AUC: 0.920), internal validation (AUC: 0.825), and external validation cohorts (AUC: 0.795). The 3D DL model performed well in the training cohort and poorly in the internal and external validation cohorts (AUCs: 0.751, 0.666, and 0.567, respectively), indicating overfitting. The combined model (2.5D DL+clinical) performed well in all cohorts (AUCs: 0.921, 0.835, 0.804). The Hosmer-Lemeshow test, DeLong test, and decision curve analysis confirmed the superiority of the combined model over the other signatures.

Conclusion: The combined model integrating 2.5D DL and clinical features accurately predicts early postoperative HCC recurrence.

Purpose: This study aims to explore the value of radiomics combined with clinical parameters in predicting recurrence-free survival (RFS) after the resection of hepatocellular carcinoma (HCC).

Patients and methods: In this retrospective study, a total of 322 patients with HCC who underwent contrast-enhanced computed tomography (CT) and radical surgical resection were enrolled and randomly divided into a training group (n = 223) and a validation group (n = 97). In the training group, Univariate and multivariate Cox regression analyses were employed to obtain clinical variables related to RFS for constructing the clinical model. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were employed to construct the radiomics model, and the clinical-radiomics model was further constructed. Model prediction performance was subsequently assessed by the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curve. Additionally, Kaplan-Meier analysis was used to evaluate the model's value in predicting RFS. Correlations between radiomics features and pathological parameters were analyzed.

Results: The clinical-radiomics model predicted RFS at 1, 2, and 3 years more accurately than the clinical or radiomics model alone (training group, AUC = 0.834, 0.765 and 0.831, respectively; validation group, AUC = 0.715, 0.710 and 0.793, respectively). The predicted high-risk subgroup based on the clinical-radiomics nomogram had shorter RFS than predicted low-risk subgroup in data sets, enabling risk stratification of various clinical subgroups. Correlation analysis revealed that the rad-score was positively related to microvascular invasion (MVI) and Edmondson-Steiner grade.

Conclusion: The clinical-radiomics model effectively predicts RFS in HCC patients and identifies high-risk individuals for recurrence.

Purpose: Radiofrequency ablation (RFA) is a micro-invasive treatment for early-stage HCC patients. Stereotactic body radiation therapy (SBRT) has also been proven an effective and safe treatment for HCC patients. This multi-center study is to compare the efficacy of computed tomography (CT)-guided RFA and CT-based SBRT in naïve HCC patients with tumor diameters ≤5 cm.

Patients and methods: This retrospective cohort study included 1001 treatment-naïve HCC patients from three hospitals or medical centers. The patients received RFA (n = 481) or SBRT (n = 520) treatment between December 2011 and May 2019. Furthermore, subgroup analyses of all patients were conducted based on Couinaud's classification of liver segments.

Results: After matching, the local control (LC) rates of the SBRT group were better than those of the RFA group (p=0.024*), which mainly referred to the patients whose tumors were located in the S7/S8 (p=0.006*). Among patients with tumors located in S1, nineteen patients (19/21) underwent SBRT. The 1-, 3- and 5-year LC rates were 100%, 87.8% and 87.8% in the SBRT group, and the 1-, 3- and 5-year OS rates were 100%, 69.8% and 69.8%, respectively. Moreover, the OS rates in S5/S6 group in RFA were higher than those in SBRT group.

Conclusion: The LC rates were better in the SBRT group than in the RFA group for the patients with lesions localized in S7/S8, and SBRT could also be a therapeutic option for patients with lesions in S1. Moreover, patients with tumors located in S5/S6 were better candidates for RFA treatment than SBRT.

Purpose: To elucidate the therapeutic potential of 2,2'-bipyridine derivatives [NPS (1-6)] on hepatocellular carcinoma HepG2 cells.

Methods: The effects on cell survival, colony formation, cellular and nuclear morphology, generation of reactive oxygen species (ROS), change in the integrity of mitochondrial membrane potential (MMP), and apoptosis were investigated. Additionally, docking studies were conducted to analyze and elucidate the interactions between the derivatives and AKT and BRAF proteins.

Results: NPS derivatives (1, 2, 5 and 6) significantly impaired cell viability of HepG2 cell lines at nanogram range concentrations - 72.11 ng/mL, 154.42 ng/mL, 71.78 ng/mL, and 71.43 ng/mL, while other derivatives were also effective at concentrations below 1 µg/mL. These compounds reduced the colony formation capacity of HepG2 cells in a dose-dependent manner following treatment. Mechanistic studies revealed that these derivatives induce reactive oxygen species (ROS) accumulation and cause mitochondrial membrane depolarization, ultimately triggering apoptosis in HepG2 cells. In the presence of these derivatives, cells demonstrated that 75% of cells underwent apoptosis, compared to 25% in the control group. Additionally, there was a marked increase in mitochondrial depolarization (95% cells) and a threefold rise in ROS levels compared to the controls. Docking studies revealed interactions between the derivatives and the signaling proteins AKT (PDB ID: 6HHF) and BRAF (PDB ID: 8C7Y) with binding affinities ranging from -7.10 to -9.91, highlighting their pivotal role in targeting key players in hepatocellular carcinoma progression.

Conclusion: The findings of this study underscore the therapeutic potential of these derivatives against HepG2 cells and offer valuable insights for further experimental validation of their efficacy as inhibitors targeting AKT or BRAF signaling pathways.

Purpose: The combination of transarterial chemoembolization, molecular targeted therapy, and immunotherapy (triple therapy) has shown promising outcomes in the treatment of unresectable hepatocellular carcinoma (HCC). This study aimed to build a prognostic model to identify patients who could benefit from triple therapy.

Patients and methods: This retrospective study encompassed 242 patients with HCC who underwent triple therapy from two centers (Training cohort: 158 patients from the Center 1; External validation cohort: 84 patients from the Center 2). Independent predictors of overall survival (OS) and progression-free survival (PFS) were identified through Cox regression analyses, and prognostic models based on Cox proportional hazards models were developed. Prognosis was assessed using Kaplan - Meier curves.

Results: In the training cohort, independent predictors of PFS included vascular invasion and the C-reactive protein and alpha-fetoprotein in immunotherapy (CRAFITY) score. Independent predictors of OS were the CRAFITY score, extrahepatic metastasis, and the neutrophil-to-lymphocyte ratio. Prognostic prediction models were constructed based on these variables. The prognostic model for OS demonstrated a C-index of 0.715 (95% confidence interval (CI), 0.662-0.768) in the training cohort and 0.701 (95% CI, 0.628-0.774) in the validation cohort. Patients were divided into low- and high-risk categories using the predictive model (P<0.001). These findings were corroborated by the external validation cohort.

Conclusion: The developed prognostic model serves as a reliable and convenient tool to predict outcomes in patients with unresectable HCC undergoing triple therapy. It aids clinicians in making informed treatment decisions.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and is associated with high mortality rates due to late detection and aggressive progression. Peritumoral radiomics, an emerging technique that quantitatively analyzes the tissue surrounding the tumor, has shown significant potential in enhancing the management of HCC. This paper examines the role of peritumoral radiomics in improving diagnostic accuracy, guiding personalized treatment strategies, and refining prognostic assessments. By offering unique insights into the tumor microenvironment, peritumoral radiomics enables more precise patient stratification and informs clinical decision-making. However, the integration of peritumoral radiomics into routine clinical practice faces several challenges. Addressing these challenges through continued research and innovation is crucial for the successful implementation of peritumoral radiomics in HCC management, ultimately leading to improved patient outcomes.

Purpose: Predicting the pathological response after neoadjuvant conversion therapy for initially unresectable hepatocellular carcinoma (HCC) is essential for surgical decision-making and survival outcomes but remains a challenge. We aimed to develop a radiomics model to predict pathological responses.

Methods: We included 203 patients with HCC who underwent hepatectomy after neoadjuvant conversion therapy between 2015 and 2023 and separated them into a training set (100 patients from Center A) and a validation set (103 patients from Center B). Pathological complete response (pCR)-related radiomic features were extracted from the largest tumor layer in the arterial and portal vein phases of the CT. A synthetic minority oversampling technique (SMOTE) was used to balance the minority groups in the training set. The SMOTE radiomics model was constructed using a logistic regression model in the SMOTE training set and its performance was verified in the validation set.

Results: The AUC of the preoperative modified response evaluation criteria in solid tumors (mRECIST) assessment for pCR was 0.656 and 0.589 in the training and validation sets, respectively. The SMOTE radiomics model was established based on ten radiomic features and showed good pCR-predictive performance in the SMOTE training set (AUC, 0.889; accuracy, 87.7%) and the validation set (AUC: 0.843, accuracy: 86.4%). The RFS of the radiomics-predicted-pCR group was significantly better than that of the predicted-non-pCR group in the training cohort (P = 0.001, 2-year RFS: 69.5% and 30.1% respectively) and the validation cohort (P = 0.012, 2-year RFS: 65.9% and 38.0% respectively).

Conclusion: The SMOTE radiomics model has great potential for predicting pathological response and evaluating RFS in patients with unresectable HCC after neoadjuvant conversion therapy.

Purpose: This research was designed to determine the long-term outcomes in patients with liver cirrhosis who achieved sustained virological response (SVR) after direct-acting anti-viral drugs (DAAs) based regimens.

Patients and methods: This study involved 193 patients with HCV-related cirrhosis who had previously completed DAAs regimens and accomplished SVR. Clinical, laboratory, and radiological features at the first and 3rd-year follow-up after the end of treatment were analyzed. Overall survival (OS) and incidence of liver decompensation or hepatocellular carcinoma (HCC) were determined at the 5-year follow-up.

Results: About 68.4% of our patients with HCV-related cirrhosis were males and their mean age was 54.8 ± 7.7 years. Follow-up at the first and the 3rd-year showed significant improvements in albumin (P = 0.001), liver enzymes (P = 0.001), alpha-fetoprotein (AFP) (P < 0.001), platelet count (P = 0.001), the model for end-stage liver disease (MELD) score (P = 0.001 and 0.01), FIB4 and Aspartate Aminotransferase-to-Platelet Ratio Index (APRI) scores (p < 0.001). The liver stiffness (LS) also significantly improved (p = 0.001). At the 5th year, the mean OS was 58.3 months, with 14.5% and 17.6% of patients developing de-novo HCC and decompensation, respectively. The mean OS at the 5th-year follow-up was shorter in patients who developed HCC and those with liver decompensation (p = 0.001). Alfa-fetoprotein and LS are predictive factors for HCC development.

Conclusion: Despite achieving SVR, continuous surveillance for HCC and new-onset decompensation is mandatory in patients with liver cirrhosis.

Background: Immune checkpoint inhibitor (ICI) therapy is a promising treatment for cancer. However, the response rate to ICI therapy in hepatocellular carcinoma (HCC) patients is low (approximately 30%). Thus, an approach to predict whether a patient will benefit from ICI therapy is required. This study aimed to design a classifier based on circulating indicators to identify patients suitable for ICI therapy.

Methods: This retrospective study included HCC patients who received immune checkpoint inhibitor therapy between March 2017 and September 2023 at Nanjing Drum Tower Hospital and Jinling Hospital. The levels of the 17 serum biomarkers and baseline patients' characters were assessed to discern meaningful circulating indicators related with survival benefits using random forest. A prognostic model was then constructed to predict survival of patients after treatment.

Results: A total of 369 patients (mean age 56, median follow-up duration 373 days,) were enrolled in this study. Among the 17 circulating biomarkers, 11 were carefully selected to construct a classifier. Receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.724. Notably, patients classified into the low-risk group exhibited a more positive prognosis (P = 0.0079; HR, 0.43; 95% CI 0.21-0.87). To enhance efficacy, we incorporated 11 clinical features. The extended model incorporated 12 circulating indicators and 5 clinical features. The AUC of the refined classifier improved to 0.752. Patients in the low-risk group demonstrated superior overall survival compared with those in the high-risk group (P = 0.026; HR 0.39; 95% CI 0.11-1.37).

Conclusion: Circulating biomarkers are useful in predicting therapeutic outcomes and can help in making clinical decisions regarding the use of ICI therapy.