Oxaliplatin is currently used for chemotherapy in patients with hepatocellular carcinoma, but its increasing tolerance to tumours over time limits its clinical application. Studies have shown that high PD-L1 expression promotes the polarization of M2 macrophages. The increased infiltration of M2 macrophages, including those in HCC, is positively correlated with poor prognosis in various solid tumours. We found that oxaliplatin promoted the expression of PD-L1 in liver cancer cells, which might be attributed partly to the tolerance of tumours to oxaliplatin. Therefore, in this study, we explored the antitumour effect of attenuated Salmonella carrying siRNA-PD-L1 combined with oxaliplatin via Western blotting, immunohistochemistry, immunofluorescence, and flow cytometry. The results revealed that attenuated Salmonella carrying siRNA-PD-L1 combined with oxaliplatin more significantly inhibited tumour growth in tumour-bearing mice, suppressed the expression of PD-L1 in tumour tissue, increased the apoptosis of tumour cells and the expression of the tumour-related protein cleaved-caspase3, and increased the infiltration of M1 macrophages and T lymphocytes in tumour tissues. Moreover, the combination therapy increased the activation of T cells and the number of T lymphocytes and NK cells in the spleens of the mice and improved the overall antitumour immune response in the mice. Our results confirmed that attenuated Salmonella harbouring siRNA-PD-L1 combined with oxaliplatin had a significant antitumour effect and did not increase the incidence of toxic side effects, providing a theoretical reference for addressing oxaliplatin tolerance in the treatment of hepatocellular carcinoma.

BACKGROUND: To develop and validate a nomogram model based on Gd-EOB-DTPA enhanced MRI for differentiation between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase (HBP).
METHODS: A total of 75 patients with 49 HCCs and 26 FNHs randomly divided into a training cohort (n = 52: 34 HCC; 18 FNH) and an internal validation cohort (n = 23: 15 HCC; 8 FNH). A total of 37 patients (n = 37: 25 HCC; 12 FNH) acted as an external test cohort. The clinical and imaging characteristics between HCC and FNH groups in the training cohort were compared. The statistically significant parameters were included into the FAE software, and a multivariate logistic regression classifier was used to identify independent predictors and establish a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the prediction ability of the model, while the calibration and decision curves were used for model validation. Subanalysis was used to compare qualitative and quantitative characteristics of patients with chronic hepatitis and cirrhosis between the HCC and FNH groups.
RESULTS: In the training cohort, gender, age, enhancement rate in the arterial phase (AP), focal defects in uptake were significant predictors for HCC showing iso- or hyperintensity in the HBP. In the training cohort, area under the curve (AUC), sensitivity and specificity of the nomogram model were 0.989(95%CI: 0.967-1.000), 97.1% and 94.4%. In the internal validation cohort, the above three indicators were 0.917(95%CI: 0.782-1.000), 93.3% and 87.5%. In the external test cohort, the above three indicators were 0.960(95%CI: 0.905-1.000), 84.0% and 100.0%. The results of subanalysis showed that age was the independent predictor in the patients with chronic hepatitis and cirrhosis between HCC and FNH groups.
CONCLUSIONS: Gd-EOB-DTPA enhanced MRI nomogram model may be useful for discriminating HCC and FNH showing iso- or hyperintensity in the HBP before surgery.

UNASSIGNED: To develop a deep learning model based on contrast-enhanced magnetic resonance imaging (MRI) data to predict post-surgical overall survival (OS) in patients with hepatocellular carcinoma (HCC).
UNASSIGNED: This bi-center retrospective study included 564 surgically resected patients with HCC and divided them into training (326), testing (143), and external validation (95) cohorts. This study used a three-dimensional convolutional neural network (3D-CNN) ResNet to learn features from the pretreatment MR images (T1WIpre, late arterial phase, and portal venous phase) and got the deep learning score (DL score). Three cox regression models were established separately using the DL score (3D-CNN model), clinical features (clinical model), and a combination of above (combined model). The concordance index (C-index) was used to evaluate model performance.
UNASSIGNED: We trained a 3D-CNN model to get DL score from samples. The C-index of the 3D-CNN model in predicting 5-year OS for the training, testing, and external validation cohorts were 0.746, 0.714, and 0.698, respectively, and were higher than those of the clinical model, which were 0.675, 0.674, and 0.631, respectively (P = 0.009, P = 0.204, and P = 0.092, respectively). The C-index of the combined model for testing and external validation cohorts was 0.750 and 0.723, respectively, significantly higher than the clinical model (P = 0.017, P = 0.016) and the 3D-CNN model (P = 0.029, P = 0.036).
UNASSIGNED: The combined model integrating the DL score and clinical factors showed a higher predictive value than the clinical and 3D-CNN models and may be more useful in guiding clinical treatment decisions to improve the prognosis of patients with HCC.

OBJECTIVE: To investigate associations between tissue diffusion, stiffness, and different tumor microenvironment features in resected hepatocellular carcinoma (HCC).
METHODS: Seventy-two patients were prospectively included for preoperative magnetic resonance (MR) diffusion-weighted imaging and MR elastography examination. The mean apparent diffusion coefficient (ADC) and stiffness value were measured on the central three slices of the tumor and peri-tumor area. Cell density, tumor-stroma ratio (TSR), lymphocyte-rich HCC (LR-HCC), and CD8 + T cell infiltration were estimated in resected tumors. The interobserver agreement of MRI measurements and subjective pathological evaluation was assessed. Variables influencing ADC and stiffness were screened with univariate analyses, and then identified with multivariable linear regression. The potential relationship between explored imaging biomarkers and histopathological features was assessed with linear regression after adjustment for other influencing factors.
RESULTS: Seventy-two patients (male/female: 59/13, mean age: 56 ± 10.2 years) were included for analysis. Inter-reader agreement was good or excellent regarding MRI measurements and histopathological evaluation. No correlation between tumor ADC and tumor stiffness was found. Multivariable linear regression confirmed that cell density was the only factor associated with tumor ADC (Estimate = -0.03, p = 0.006), and tumor-stroma ratio was the only factor associated with tumor stiffness (Estimate = -0.18, p = 0.03). After adjustment for fibrosis stage (Estimate = 0.43, p < 0.001) and age (Estimate = 0.04, p < 0.001) in the multivariate linear regression, intra-tumoral CD8 + T cell infiltration remained a significant factor associated with peri-tumor stiffness (Estimate = 0.63, p = 0.02).
CONCLUSIONS: Tumor ADC surpasses tumor stiffness as a biomarker of cellularity. Tumor stiffness is associated with tumor-stroma ratio and peri-tumor stiffness might be an imaging biomarker of intra-tumoral immune microenvironment.
CONCLUSIONS: Tissue stiffness could potentially serve as an imaging biomarker of the intra-tumoral immune microenvironment of hepatocellular carcinoma and aid in patient selection for immunotherapy.
CONCLUSIONS: Apparent diffusion coefficient reflects cellularity of hepatocellular carcinoma. Tumor stiffness reflects tumor-stroma ratio of hepatocellular carcinoma and is associated with tumor-infiltrating lymphocytes. Tumor and peri-tumor stiffness might serve as imaging biomarkers of intra-tumoral immune microenvironment.

Metastasis remains a major challenge in the successful management of malignant diseases. The liver is a major site of metastatic disease and a leading cause of death from gastrointestinal malignancies such as colon, stomach, and pancreatic cancers, as well as melanoma, breast cancer, and sarcoma. As an important factor that influences the development of metastatic liver cancer, alternative splicing drives the diversity of RNA transcripts and protein subtypes, which may provide potential to broaden the target space. In particular, the dysfunction of splicing factors and abnormal expression of splicing variants are associated with the occurrence, progression, aggressiveness, and drug resistance of cancers caused by the selective splicing of specific genes. This review is the first to provide a detailed summary of the normal splicing process and alterations that occur during metastatic liver cancer. It will cover the role of alternative splicing in the mechanisms of metastatic liver cancer by examining splicing factor changes, abnormal splicing, and the contribution of hypoxia to these changes during metastasis.

OBJECTIVE: To compare the efficacy of transarterial chemoembolization (TACE) combined with tyrosine kinase inhibitors (TKIs) plus immune checkpoint inhibitors (ICIs) (TACE-TKI-ICI) versus TKIs plus ICIs (TKI-ICI) for unresectable hepatocellular carcinoma (HCC) with first- or lower-order portal vein tumor thrombosis (PVTT).
METHODS: A retrospective study was performed in HCC patients with first- or lower-order PVTT receiving TKIs (Lenvatinib or sorafenib) plus ICIs (camrelizumab, sintilimab, or atezolizumab) with or without TACE from four institutions between January 2019 and January 2022. Propensity score-based method was performed to minimize bias by confounding factors. Tumor response, progression-free survival (PFS), overall survival (OS), and adverse events (AEs) were evaluated and compared between the two groups.
RESULTS: After inverse probability of treatment weighting, two balanced pseudopopulations were created: 106 patients in the TACE-TKI-ICI group and 109 patients in the TKI-ICI group. The objective response rate was higher in the TACE-TKI-ICI group (50.9% vs. 28.4%, P < 0.001). The median PFS and OS were significantly longer in the TACE-TKI-ICI group than in the TKI-ICI group (PFS: 9.1 vs. 5.0 months, P = 0.005; OS: 19.1 vs. 12.7 months, P = 0.002). In Cox regression, TACE-TKI-ICI treatment was an independent predictor of favorable OS. Treatment-related grade 3/4 AEs were comparable between the two groups (22.6% vs. 17.9%, P = 0.437).
CONCLUSIONS: TACE-TKI-ICI therapy contributed to better tumor control, PFS and OS than TKI-ICI therapy in unresectable HCC patients with first- or lower-order PVTT.

OBJECTIVE: We assessed the predictive capacity of computed tomography (CT)-enhanced radiomics models in determining microvascular invasion (MVI) for isolated hepatocellular carcinoma (HCC) ≤ 5 cm within peritumoral margins of 5 and 10 mm.
METHODS: Radiomics software was used for feature extraction. We used the least absolute shrinkage and selection operator (LASSO) algorithm to establish an effective model to predict patients\' preoperative MVI status.
RESULTS: The area under the curve (AUC) values in the validation sets for the 5- and 10-mm radiomics models concerning arterial tumors were 0.759 and 0.637, respectively. In the portal vein phase, they were 0.626 and 0.693, respectively. Additionally, the combined radiomics model for arterial tumors and the peritumoral 5-mm margin had an AUC value of 0.820. The decision curve showed that the combined tumor and peritumoral radiomics model exhibited a somewhat superior benefit compared to the traditional model, while the fusion model demonstrated an even greater advantage, indicating its significant potential in clinical application.
CONCLUSIONS: The 5-mm peritumoral arterial model had superior accuracy and sensitivity in predicting MVI. Moreover, the combined tumor and peritumoral radiomics model outperformed both the individual tumor and peritumoral radiomics models. The most effective combination was the arterial phase tumor and peritumor 5-mm margin combination. Using a fusion model that integrates tumor and peritumoral radiomics and clinical data can aid in the preoperative diagnosis of the MVI of isolated HCC ≤ 5 cm, indicating considerable practical value.
UNASSIGNED: The radiomics model including a 5-mm peritumoral expansion is a promising noninvasive biomarker for preoperatively predicting microvascular invasion in patients diagnosed with a solitary HCC ≤ 5 cm.
CONCLUSIONS: • Radiomics features extracted at a 5-mm distance from the tumor could better predict hepatocellular carcinoma microvascular invasion. • Peritumoral radiomics can be used to capture tumor heterogeneity and predict microvascular invasion. • This radiomics model stands as a promising noninvasive biomarker for preoperatively predicting MVI in individuals.

UNASSIGNED: We aimed to investigate risk factors for early postoperative recurrence in patients with hepatocellular carcinoma (HCC) and determine the effect of surgical methods on early recurrence to facilitate predicting the risk of early postoperative recurrence in such patients and the selection of appropriate treatment methods.
UNASSIGNED: We retrospectively analyzed clinical data concerning 428 patients with HCC who had undergone radical surgery at Mianyang Central Hospital between January 2015 and August 2022. Relevant routine preoperative auxiliary examinations and regular postoperative telephone or outpatient follow-ups were performed to identify early postoperative recurrence. Risk factors were screened, and predictive models were constructed, including patients\' preoperative ancillary tests, intra- and postoperative complications, and pathology tests in relation to early recurrence. The risk of recurrence was estimated for each patient based on a prediction model, and patients were categorized into low- and high-risk recurrence groups. The effect of anatomical liver resection (AR) on early postoperative recurrence in patients with HCC in the two groups was assessed using survival analysis.
UNASSIGNED: In total, 353 study patients were included. Multifactorial logistic regression analysis findings suggested that tumor diameter (≥5/<5 cm, odds ratio [OR] 2.357, 95% confidence interval [CI] 1.368-4.059; P = 0.002), alpha fetoprotein (≥400/<400 ng/L, OR 2.525, 95% CI 1.334-4.780; P = 0.004), tumor number (≥2/<2, OR 2.213, 95% CI 1.147-4.270; P = 0.018), microvascular invasion (positive/negative, OR 3.230, 95% CI 1.880-5.551; P < 0.001), vascular invasion (positive/negative, OR 4.472, 95% CI 1.395-14.332; P = 0.012), and alkaline phosphatase level (>125/≤125 U/L, OR 2.202, 95% CI 1.162-4.173; P = 0.016) were risk factors for early recurrence following radical HCC surgery. Model validation and evaluation showed that the area under the curve was 0.813. Hosmer-Lemeshow test results (X 2 = 1.225, P = 0.996 > 0.05), results from bootstrap self-replicated sampling of 1,000 samples, and decision curve analysis showed that the model also discriminated well, with potentially good clinical utility. Using this model, patients were stratified into low- and high-risk recurrence groups. One-year disease-free survival was compared between the two groups with different surgical approaches. Both groups benefited from AR in terms of prevention of early postoperative recurrence, with AR benefits being more pronounced and intraoperative bleeding less likely in the high-risk recurrence group.
UNASSIGNED: With appropriate surgical techniques and with tumors being realistically amenable to R0 resection, AR is a potentially useful surgical procedure for preventing early recurrence after radical surgery in patients with HCC.

UNASSIGNED: To develop and validate a radiomics machine learning (Rad-ML) model based on preoperative MRI to predict extrahepatic metastasis (EHM) in hepatocellular carcinoma (HCC) patients receiving transarterial chemoembolization (TACE) treatment.
UNASSIGNED: A total of 355 HCC patients who received multiple TACE procedures were split at random into a training set and a test set at a 7:3 ratio. Radiomic features were calculated from tumor and peritumor in arterial phase and portal venous phase, and were identified using intraclass correlation coefficient, maximal relevance and minimum redundancy, and least absolute shrinkage and selection operator techniques. Cox regression analysis was employed to determine the clinical model. The best-performing algorithm among eight machine learning methods was used to construct the Rad-ML model. A nomogram combining clinical and Rad-ML parameters was used to develop a combined model. Model performance was evaluated using C-index, decision curve analysis, calibration plot, and survival analysis.
UNASSIGNED: In clinical model, elevated neutrophil to lymphocyte ratio and alpha-fetoprotein were associated with faster EHM. The XGBoost-based Rad-ML model demonstrated the best predictive performance for EHM. When compared to the clinical model, both the Rad-ML model and the combination model performed better (C-indexes of 0.61, 0.85, and 0.86 in the training set, and 0.62, 0.82, and 0.83 in the test set, respectively). However, the combined model\'s and the Rad-ML model\'s prediction performance did not differ significantly. The most influential feature was peritumoral waveletHLL_firstorder_Minimum in AP, which exhibited an inverse relationship with EHM risk.
UNASSIGNED: Our study suggests that the preoperative MRI-based Rad-ML model is a valuable tool to predict EHM in HCC patients treated with TACE.

OBJECTIVE: Our meta-analysis was performed to explore the prognostic factors for overall survival among post-hepatectomy patients with spontaneous ruptured hepatocellular carcinoma (SRHCC).
METHODS: PubMed, EMBASE, the Cochrane Library, and Web of Science were all searched up for relevant studies regarding prognostic factors with SRHCC. RevMan5.3 software and Stata 14.0 software were used for statistical analysis.
RESULTS: A total of nineteen studies with 1876 resected SRHCC patients were finally identified. Pooled results indicated that preoperative AFP (high vs low) (P = 0.003), concurrent liver cirrhosis (yes vs no) (P = 0.02), preoperative liver function (child A vs non-child A) (P = 0.0007), tumor size (large vs small) (P < 0.00001), tumor number (solitary vs multiple) (P = 0.002), satellite foci (yes vs no) (P = 0.0006), micro-vascular invasion (yes vs no) (P < 0.00001), type of hepatectomy (major or minor) (P = 0.04), surgical margin (R + vs R -) (P < 0.00001), and type of hepatectomy (emergency hepatectomy vs staged hepatectomy) (P = 0.005) were prognostic factors for overall survival among post-hepatectomy SRHCC patients.
CONCLUSIONS: Apart from some conventional prognostic factors identified in resected patients with SRHCC, numerous prognostic factors have also been unmasked, which might provide clinical reference to stratify patients with different therapeutic regimes.