Objectives: To compare the safety and efficacy of lenvatinib (LEN) combined with camrelizumab plus transcatheter arterial chemoembolization (TACE-LEN-C) and TACE combined with LEN (TACE-LEN) in patients with unresectable hepatocellular carcinoma (uHCC).Methods: Eighty-three patients with uHCC treated with TACE-LEN-C or TACE-LEN from September 2018 to May 2021 were enrolled in this retrospective study. Overall survival (OS), progression-free survival (PFS), local tumor response, and adverse events (AEs) were evaluated. Univariate and multivariate analyses were used to determine the factors affecting survival.Results: There were 31 patients in the TACE-LEN-C group and 52 patients in the TACE-LEN group. The median follow-up period was 14.2 months (range 7.2–25.2 months) in the whole study. The combination of triple therapy was found to significantly prolong the PFS (12.5 months vs. 6.6 months, P<0.001) and OS (18.9 months vs. 13.9 months, P<0.001. In terms of tumor response, the combination demonstrated a higher objective response rate (71% vs. 42.3% by the modified Response Evaluation Criteria in Solid Tumors, P=0.023) without a statistically significant difference in the disease control rate (93.5% in TACE-LEN-C, 80.8% in TACE-LEN, P=0.195). In the multivariate analysis, two independent factors affecting PFS were identified: number of tumors and treatment. Three independent factors affected OS: number of tumors, Barcelona Clinic Liver Cancer (BCLC) stage, and treatment. All the AEs were tolerable.Conclusion: TACE-LEN-C is a safe and effective treatment for patients with uHCC, and could be a potential treatment option.

Background: Pancreatic ductal adenocarcinoma (PDAC) is associated with a high rate of early recurrence (ER) after radical resection, which significantly affects long-term survival. Currently, no reliable system exists for predicting ER in these patients. This study aimed to develop a machine learning (ML) model combining intratumoral and peritumoral radiomic features with body composition to predict the ER risk in patients with PDAC following radical resection. Materials and methods: This study included patients with PDAC who underwent upfront surgery at four hospitals between June 2014 and December 2023. Preoperative clinical information, computed tomography (CT) images, and postoperative pathological data were collected. CT-quantified body composition was measured; radiomic features were extracted from the intratumoral and peritumoral regions. Six ML algorithms were used to develop predictive models, including radiomics, clinical, clinical-radiomics, and clinicopathological-radiomics models. The SHapley Additive exPlanations (SHAP) method was applied for model interpretability. Results: A total of 589 patients were evaluated, including 320 patients (mean age: 60.4 ± 8.3 years; 191 men) in the training cohort, 138 patients (mean age: 60.7 ± 8.9 years; 84 men) in the internal validation cohort, and 131 patients (mean age: 61.7 ± 10.9 years; 76 men) in the external validation cohort. The intra-peri-radiomics model, based on the random forest algorithm, achieved the best performance, with AUCs of 0.865, 0.849, and 0.839 in the training, internal validation, and external validation cohorts, respectively. Incorporating clinicopathological factors, the combined model showed superior performance, with AUCs of 0.936, 0.899, and 0.884 in the training, internal validation, and external validation cohorts, respectively. SHAP analysis revealed that radiomic features, adjuvant therapy, CA199, lymphovascular invasion, platelet-lymphocyte ratio, visceral fat index, CA125, visceral-subcutaneous fat tissue ratio, tumor size, and TNM stage significantly contributed to the prediction of ER. Conclusion: The developed ML model, integrating radiomic features and clinicopathological factors, offered superior predictive accuracy for ER in patients with PDAC post-surgery. SHAP visualization enhanced the model’s interpretability and facilitated clinical applications.

Objective: To evaluate the efficacy and safety of transarterial chemoembolization (TACE) combined with tyrosine kinase inhibitors (TKIs: sorafenib, lenvatinib, and apatinib) plus camrelizumab (TACE-TKIs-C) vs TACE combined with TKIs (TACE-TKIs) for advanced hepatocellular carcinoma (HCC). Methods: In this two-center retrospective study, patients with advanced HCC treated with TACE-TKIs-C or TACE-TKIs were enrolled between January 1, 2018, to October 1, 2020. A total of 260 eligible patients received TACE-TKIs-C (N=70) or TACE-TKIs (N=190). The differences in overall survival (OS), progression-free survival (PFS) and tumor response were compared between two groups. Propensity score matching (PSM) analysis was applied to reduce patient selection bias. The risk factors affecting OS or PFS were analyzed. Results: Fifty-three pairs of patients were matched after PSM analysis. Before PSM analysis, the median OS and PFS of TACE-TKIs-C were significantly longer than those of the TACE-TKIs (OS: not reached vs 12.0 months, P< 0.0001; PFS: 10.0 months vs 6.0 months, P< 0.0001). After PSM analysis, the median OS and PFS of TACE-TKIs-C were significantly longer than those of the TACE-TKIs (OS: Not reached vs 13.0 months, P< 0.0001; PFS: 9.0 months vs 6.0 months, P< 0.0001); the uni- and multivariate analysis revealed that TACE-TKIs-C treatment was a protective factor of OS and PFS. Grade 3 or 4 hypertension occurred in 14.3% of patients in the TACE-TKIs-C group and other high-grade toxic effects were infrequent. Conclusion: In patients with advanced HCC, TACE-TKIs-C may improve overall and progression-free survival outcomes over TACE-TKIs with manageable safety profile.

Hepatocellular carcinoma (HCC) is a highly refractory cancer and the fourth leading cause of cancer-related mortality worldwide. Despite the development of a detailed treatment strategy for HCC, the survival rate remains unsatisfactory. Oncolytic virus has been extensively researched as a new cancer therapeutic agent in the treatment of HCC. Researchers have designed a variety of recombinant viruses based on natural oncolytic diseases, which can increase the targeting of oncolytic viruses to HCC and their survival in tumors, as well as kill tumor cells and inhibit the growth of HCC through a variety of mechanisms. The overall efficacy of oncolytic virus therapy is known to be influenced by anti-tumor immunity, toxic killing effect and inhibition of tumor angiogenesis, etc. Therefore, a comprehensive review of the multiple oncolytic mechanisms of oncolytic viruses in HCC has been conducted. So far, a large number of relevant clinical trials are under way or have been completed, and some encouraging results have been obtained. Studies have shown that oncolytic virus combined with other HCC therapies may be a feasible method, including local therapy, chemotherapy, molecular targeted therapy and immunotherapy. In addition, different delivery routes for oncolytic viruses have been studied so far. These studies make oncolytic virus a new and attractive drug for the treatment of HCC.

Background: Diabetes is prevalent among patients with hepatocellular carcinoma (HCC) and is associated with a poor prognosis. Although the hypoglycemic drug metformin has shown antitumor effects, its potential positive effect on patients with HCC and diabetes undergoing transarterial chemoembolization (TACE) remains unclear. Therefore, this study aimed to investigate the efficacy and safety of metformin in patients with HCC and type II diabetes who are receiving TACE. Materials and methods: This retrospective study involved 372 consecutive patients with HCC and type II diabetes across three medical centers between January 2014 and June 2021. All patients underwent TACE. Propensity score matching (PSM) was used to reduce selection bias. Cox proportional hazards regression was employed to compare all-cause death between the metformin and nonmetformin groups while competing risk regression was performed to assess cancer-specific death. Results: Among 372 patients included in the study, 208 patients (177 male patients and 31 female patients) with a mean age of 59.6 (10.3) years received metformin, and 164 patients (139 male patients and 25 female patients) with a mean age of 60.3 (10.0) years did not. Before PSM, patients with metformin had significantly longer median overall survival (mOS) and median progression-free survival (mPFS) than those without metformin (mOS: 34 months, 95% CI: 25.6–42.4 vs. 20 months, 95% CI: 15.3–24.7; P <0.001; mPFS: 11 months, 95% CI: 9.3–12.7 vs. 8 months, 95% CI: 5.9–10.1; P <0.001). Similar results were observed after PSM. Multivariate regression analysis indicated that metformin was associated with a reduced risk of all-cause mortality (HR: 0.589, 95% CI: 0.454–0.763; P <0.001) and tumor progression (HR: 0.667, 95% CI: 0.526–0.845; P =0.001) before PSM. After excluding deaths related to other factors, metformin continued to demonstrate a reduction in cancer-specific mortality risk among the patients. Subgroup analysis further revealed that patients using metformin had lower all-cause mortality risk and tumor progression risk than those without metformin in most subgroups. Adverse event evaluation suggested that metformin could lead to elevated nausea incidence. Conclusion: Metformin may confer survival benefits to patients with HCC and type II diabetes undergoing TACE. Metformin may simultaneously address multiple aspects of treatment in these patients.

Purpose: Type II diabetes mellitus (T2DM) has been found to increase the mortality of patients with hepatocellular carcinoma (HCC). Therefore, this study aimed to establish and validate a machine learning-based explainable prediction model of prognosis in patients with HCC and T2DM undergoing transarterial chemoembolization (TACE). Patients and Methods: The prediction model was developed using data from the derivation cohort comprising patients from three medical centers, followed by external validation utilizing patient data extracted from another center. Further, five predictive models were employed to establish prognosis models for 1-, 2-, and 3-year survival, respectively. Prediction performance was assessed by the receiver operating characteristic (ROC), calibration, and decision curve analysis curves. Lastly, the SHapley Additive exPlanations (SHAP) method was used to interpret the final ML model. Results: A total of 636 patients were included. Thirteen variables were selected for the model development. The final random survival forest (RSF) model exhibited excellent performance in the internal validation cohort, with areas under the ROC curve (AUCs) of 0.824, 0.853, and 0.810 in the 1-, 2-, and 3-year survival groups, respectively. This model also demonstrated remarkable discrimination in the external validation cohort, achieving AUCs of 0.862, 0.815, and 0.798 in the 1-, 2-, and 3-year survival groups, respectively. SHAP summary plots were also created to interpret the RSF model. Conclusion: An RSF model with good predictive performance was developed by incorporating simple parameters. This prognostic prediction model may assist physicians in early clinical intervention and improve prognosis outcomes in patients with HCC and comorbid T2DM after TACE.