Journal of Hepatocellular Carcinoma最新文献

Purpose: Develop a magnetic resonance imaging (MRI)-based deep learning (DL)-radiomics (Rad)-clinical nomogram for predicting early recurrence of hepatocellular carcinoma (HCC) within six months after thermal ablation.

Materials and methods: Barcelona Clinic Liver Cancer (BCLC) stage 0-A HCC patients who underwent dynamic contrast-enhanced MRI before ablation were retrospectively included. Patients were categorized into non early recurrence and early recurrence groups. A clinical model was constructed through logistic regression analysis of clinical information and radiological features. DL score model and Rad score model were developed using DL features and manual features extracted from dynamic contrast-enhanced MRI, with principal component analysis and least absolute shrinkage and selection operator regression methods. The DL-Rad-Clinical nomogram was constructed through logistic regression analysis. The model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUC).

Results: A total of 224 patients were included in this study (training set: n = 156; test set: n = 68). The DL-Rad-Clinical nomogram was constructed, including Rad score, DL score, natural logarithm alpha-fetoprotein (LnAFP), and multiple low signal lesions as predictive factors. In the training set, the DL-Rad-Clinical nomogram demonstrated better predictive performance (AUC = 0.896, P < 0.05). In the test set, the DL-Rad-Clinical nomogram had a higher AUC value compared to other models, although the difference was not statistically significant (AUC = 0.774, P > 0.05).

Conclusion: The DL-Rad-Clinical nomogram helped in identifying HCC patients with early recurrence within six months following thermal ablation.

Background & aims: Prothrombin induced by vitamin K absence-II (PIVKA-II) levels have been reported to correlate with hepatocellular carcinoma (HCC) prognosis, but its utility for assessing early treatment response remains underexplored. This study evaluated early PIVKA-II changes for predicting response and survival in HCC patients undergoing immune checkpoint inhibitors (ICIs) and targeted therapy.

Methods: Eighty-two HCC patients were enrolled. Serum PIVKA-II levels were measured at baseline and after the first treatment cycle. Patients were stratified based on early PIVKA-II dynamics into a biochemical response group (≥50% reduction, n=40) and a non-response group (<50% reduction, n=42). Logistic regression and Cox proportional hazards models were used to identify predictors of objective response rate (ORR), progression-free survival (PFS), and overall survival (OS).

Results: Time-dependent ROC analysis established ≥50% PIVKA-II decline as the early response threshold. The PIVKA-II response group had a significantly higher proportion of patients with Child-Pugh A, a lower incidence of extrahepatic metastasis, and significantly higher ORR (82.5% vs 38.1%, P<0.001). Median PFS and OS were not reached in the PIVKA-II responder group, compared to 8.9 months and 16.7 months, respectively, in the non-responder group (both P < 0.001). Multivariate analysis confirmed early PIVKA-II response as an independent predictor of PFS (HR=0.687, P<0.001) and OS (HR=0.709, P<0.001). Notably, in AFP-negative patients, an early PIVKA-II response was predictive of ORR and was associated with significantly longer PFS and OS.

Conclusion: Early PIVKA-II response effectively predicts treatment response and prognosis in advanced HCC patients receiving ICI and targeted therapy, especially in AFP-negative patients.

Purpose: To investigate the predictive value of the preoperative lymphocyte-to-monocyte ratio (LMR) for long-term survival in patients with intermediate-to-advanced hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE), providing a reference for precise clinical decision-making.

Patients and methods: A retrospective analysis was conducted on clinical data from 313 patients with intermediate-to-advanced HCC treated with TACE at Sichuan Provincial People's Hospital between February 2016 and September 2021. Cox regression analysis was used to identify independent risk factors affecting overall survival (OS). The optimal cut-off value for LMR was determined using receiver operating characteristic (ROC) curve analysis. Survival curves were generated using the Kaplan-Meier method, and differences between groups were compared using the Log rank test.

Results: Univariate and multivariate regression analyses revealed that LMR (P=0.033), alpha-fetoprotein (AFP, P=0.007), tumor number (P=0.044), BCLC stage (P=0.013), systemic immune-inflammation index (SII, P=0.044), and fibrosis-4 index (FIB-4, P=0.040) were independent risk factors for OS. Kaplan-Meier survival analysis further demonstrated that, in addition to LMR, patients with AFP > 642.08 ng/mL, cholinesterase ≤ 4.55 kU/L, SII > 250.91, neutrophil-to-lymphocyte ratio (NLR) > 2.85, and FIB-4 > 4.51 also exhibited significantly lower survival rates (all P < 0.05). The optimal cut-off value for LMR was 2.71 (AUC=0.62). Patients with LMR ≤ 2.71 had a significantly lower 3-year survival rate (23.8%) compared to those with LMR > 2.71 (54.2%; log-rank χ² = 21.2, P<0.001).

Conclusion: This study confirms that pre-treatment LMR is an independent predictor of overall survival following TACE in a cohort predominantly composed of patients with intermediate-to-advanced HCC classified as BCLC stage C. LMR may serve as a valuable complement to traditional prognostic models, providing incremental value for prognostic assessment in this specific patient population.

Background and aims: Dual-phenotype hepatocellular carcinoma (DPHCC) is an uncommon, highly aggressive form of liver cancer defined by the concurrent expression of both hepatocellular and cholangiocytic markers. This biphenotypic nature contributes to early recurrence and significantly worse survival compared to classic HCC. The benefit of adjuvant transarterial chemoembolization (TACE) after resection for DPHCC is unclear. We aimed to evaluate whether postoperative TACE improves outcomes in patients with resected DPHCC.

Methods: We retrospectively evaluated 436 patients with confirmed DPHCC who underwent curative resection from 2013-2023 at a single center. Among them, 276 received adjuvant TACE and 160 had surgery alone. To minimize selection bias, we performed 1:2 propensity score matching, yielding a balanced cohort of 210 TACE-treated patients and 134 observation-only patients. Recurrence-free survival (RFS) and overall survival (OS) were assessed with Kaplan-Meier and Cox analyses (median follow-up 58 months).

Results: Adjuvant TACE significantly prolonged RFS and OS compared to observation. In the matched cohort, TACE reduced the hazard of recurrence by 32% (HR 0.678, P = 0.032) and the hazard of death by 47% (HR 0.533, P = 0.026). Multivariate analysis confirmed adjuvant TACE as an independent protective factor for RFS and OS. Toxicities were mostly mild (11.4% Grade 3-4; no treatment-related deaths).

Conclusion: In patients with DPHCC, the addition of adjuvant TACE after curative resection substantially lowers recurrence rates and prolongs long-term survival. These findings support incorporating TACE into postoperative management for this high-risk HCC subtype, warranting confirmation in prospective trials.

Clinical trial registration: This study has been registered with the Chinese Clinical Trial Registry Center (ChiCTR2500103222).

Background: Intratumoral tertiary lymphoid structures (iTLSs) are associated with favorable prognosis and immunotherapy response in hepatocellular carcinoma (HCC). This study aimed to develop an interpretable susceptibility-weighted imaging (SWI)-based radiomics model to non-invasively predict iTLSs in HCC.

Materials and methods: A retrospective cohort of 477 HCC patients undergoing preoperative SWI was used (training: 290; validation: 125; independent validation: 62). Radiomics models were constructed using five machine learning algorithms: logistic regression, random forest (RF), support vector machine, extreme gradient boosting, and K-nearest neighbors. Model performance was evaluated using the area under the ROC curve (AUC), model interpretability was examined using shapley additive explanations (SHAP), and survival analyses were performed to assess clinical relevance.

Results: In the independent validation cohort, the RF algorithm was identified as the optimal classifier, with an AUC of 0.771 (95% CI: 0.641-0.883), sensitivity of 78.6%, and specificity of 67.6%. It significantly outperformed the radiological model (p = 0.046), and showed comparable performance with the hybrid model in predicting iTLSs positivity (iTLSs+) (p > 0.05). SHAP analysis showed that radiomics features (logarithm_firstorder_Minimum and exponential_glszm_ZoneEntropy) were significant predictors of iTLSs+. Kaplan-Meier analysis demonstrated improved time-to-recurrence (TTR) in the iTLSs+ predictor group compared to the iTLSs-negativity (iTLSs-) predictor group (p < 0.05). Furthermore, patients in the iTLSs+ predictor group receiving tyrosine kinase inhibitors combined with immune checkpoint inhibitors (TKI-ICI) therapy exhibited significantly extended TTR (p < 0.05), while no benefit was observed in the iTLSs- predictor group.

Conclusion: The SWI-based radiomics model provided a non-invasive tool for predicting iTLSs+ in HCC and identifying patients who might benefit from TKI-ICI therapy, and it showed potential for future integration into clinical decision-making workflows.

Background: The prognosis of early recurrence of hepatocellular carcinoma (HCC) remains poor. This study aimed to develop and validate a radiomics model and determine potential biomarkers involved in biological pathways for early recurrence of HCC.

Methods: A total of 271 HCC patients from the First Affiliated Hospital of Guilin Medical University were enrolled as the training cohort. Recurrence related radiomics features were determined by analyzing contrast-enhanced CT images, which were used for the construction of Rad-score. For external validation, we utilized both imaging and transcriptome data from 34 HCC patients in TCGA database. The identified radiomics-related genes were further validated using two independent datasets (OEP000321 and GSE14520) and immunohistochemical analysis of EEF1E1 in 38 HCC tissue samples from training cohort.

Results: Rad-scores based on six radiomics features showed predictive value for early HCC recurrence in both cohorts (A465, A466, A839, V105, V250, V291). Relevant radiomics features are associated with metabolism, proliferation, and immune pathways. The most relevant recurrence-related radiomics gene module was determined via weighted correlation network analysis (WGCNA), which contained LRP12, GPD1L, GARS, EEF1E1, and DGKG. The model based on these genes could efficiently predict early HCC recurrence and was verified in the OEP000321 and GSE14520 datasets. Moreover, EEF1E1 was significantly associated with the Rad-score, illustrated prognostic value at the transcription level, and validated by immunohistochemical staining at the protein level.

Conclusion: Rad-score and radiomics gene signatures from enhanced CT effectively predicted early recurrence in HCC, while EEF1E1 might serve as an efficient biomarker for early recurrence prediction for hepatocellular carcinoma.

Purpose: This study aimed to develop a predictive model for the prognosis of patients with hepatocellular carcinoma (HCC) after resection.

Methods: Eighty-two HCC patients were randomly divided into a training cohort (n = 62) and a validation cohort (n = 20). Clinicopathological, multiproteomics features based on CO-Detection by Indexing (Codex), and radiomics features extracted from magnetic resonance imaging (MRI) were used to construct four models: clinicopathological model, radiomics model, proteomics model, and combined model. Model performance was evaluated using the C-index, calibration curves, receiver operating characteristic (ROC) curves, survival curves, and decision curve analysis (DCA).

Results: The combined model, integrating clinicopathological, radiomics, and multi-proteomic features, demonstrated the best performance of overall survival (OS) prediction in both the training cohort (C-index = 0.821, 95% CI: 0.745-0.897) and validation cohort (C-index = 0.791, 95% CI: 0.628-0.954). The calibration curve showed high accuracy of the combined nomogram in predicting OS.

Conclusion: This study innovatively integrates CODEX-based multiproteomics, radiomics, and clinicopathological features to construct a prognostic prediction model for HCC. The combined model demonstrates improved prognostic predictive efficacy compared with single-modality models. This approach establishes a theoretical foundation for personalized diagnosis and treatment. However, its clinical utility requires further validation through large-scale, multi-center studies.

Objective: This study aimed to evaluate setup errors associated with three respiratory motion management techniques in stereotactic body radiation therapy (SBRT) for individuals with hepatic malignancies.

Methods: A retrospective analysis was conducted on data from 55 individuals with hepatic malignancies who underwent SBRT. Respiratory motion was managed using the Active Breathing Coordinator (ABC) in 11 cases, the BodyFIX system in 6 cases, and a thermoplastic body film combined with an airbag in 38 cases. Cone-beam computed tomography (CBCT) was conducted prior to each treatment session and registered with the reference computed tomography (CT) images acquired during the treatment planning phase to quantify setup errors in three dimensions: left-right (LR), superior-inferior (SI), and anterior-posterior (AP).

Results: In the LR direction, the BodyFIX group had a 1.07 mm lower setup error than the ABC group, and the airbag group showed a 2.13 mm reduction compared to ABC and 1.06 mm compared to BodyFIX. In the SI direction, BodyFIX showed a 4.66 mm reduction and the airbag group a 5.45 mm reduction versus ABC. In the AP direction, reductions were 1.99 mm for BodyFIX and 2.86 mm for the airbag group compared to ABC. All differences were statistically significant. The airbag group also had relatively small planning target volume (PTV) margins.

Conclusion: The airbag-based respiratory motion management technique demonstrated superior positioning accuracy, improved reproducibility, and the potential for PTV margin reduction in SBRT for hepatic malignancies. Further investigations are needed to verify the superiority of this approach in different populations and settings.

Aim: To investigate the effect of IDO1 expression levels in HCC on the distribution, infiltration, and anti-tumor immune response of mature DCs.

Methods: Multiplex immunohistochemical staining was applied to detect the expression level of IDO1 and the infiltration of DCs in the HCC tissue microarray, including total 96 human HCC samples and 82 samples of matched adjacent normal tissues. In vitro, CCK-8, Key Fluor 488 Click-iT Edu, wound healing, and transwell assays were performed to explore the effect of IDO1 on the viability, proliferation, migration and invasion ability of HCC cell line SK-HEP1. In vivo, a subcutaneous xenograft tumor model of nude mice was established by subcutaneously inoculating SK-HEP1 and treated with IDO1 catalytic inhibitor epacadostat (EPA) to observe the effect of IDO1 on tumor growth and immune cells infiltration.

Results: Results of clinical tissue microarrays showed that compared with corresponding paracancerous tissues, the infiltration of mature DCs was significantly reduced in HCC cancer tissues with high expression of IDO1. Meanwhile, IDO1 was highly expressed in HCC cancer tissues with pathological grade I-II, high AFP levels (≥200µg/L), HBV-positivie, cirrhosis, distant metastasis and recurrence. Survival analysis showed that low IDO1 and high mature DCs cell infiltration were significantly associated with superior overall survival (OS). Correlation analysis further showed that IDO1 was negatively correlated with mature DCs. The in vitro cellular and in vivo animal experiments in this study showed that inhibition IDO1 helped to decrease the malignant biological behavior of HCC and enhance the response of immune cells to tumor cells.

Conclusion: IDO1 suppresses anti-tumor immunity in HCC, at least in part, by curtailing mDC infiltration. Targeting IDO1 may represent a promising immunotherapeutic strategy. However, its immunomodulatory effects must be validated in immunocompetent or humanized animal systems before clinical translation.

Background: Systematic reviews (SRs) are indispensable for presenting reliable evidence of the effectiveness of treatments. However, methodological flaws can affect their reliability and validity.

Aim: This cross-sectional study aimed to evaluate the methodological quality of SRs on liver cancer (LC) treatments and identify potential factors affecting their reliability.

Methods: A comprehensive literature search was carried out on four databases to identify eligible SRs published between January 2014 and October 2023. We appraised the methodological quality of included SRs by Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR 2) tool. Multivariable regression analysis was employed to investigate the factors influencing the methodological quality.

Results: A total of 119 SRs were included and appraised. Only one SR (0.8%) was rated as high overall quality. One (0.8%), nine (7.6%), and 108 (90.8%) were appraised as moderate, low, and critical low quality, respectively. SRs published more recently, with higher journal impact factors, or with corresponding author from Europe have better performance.

Conclusion: The methodological quality of SRs on LC treatments was unsatisfactory. Future SR authors should improve quality of SRs through registering an a priori protocol, providing explanation for selection of study designs, using a comprehensive literature search strategy, listing all excluded studies and justifying their reasons, describing the included studies in adequate detail, and reporting funding resources of primary studies.