Ren Lang

Background: Extensive vascular invasion or defects after resection significantly limit the feasibility of radical pancreatic surgery, underscoring the value of effective vascular reconstruction techniques. This study evaluates a novel approach to vascular reconstruction using allogeneic venous grafts (AVGs) in pancreaticoduodenectomy and pancreatectomy. Methods: This retrospective cohort study included 187 patients with biliary or pancreatic malignancies or lesions presenting with various types of venous invasion who underwent pancreatic resection with vascular reconstruction using AVGs. The R0 resection rate, long-term patency rate of reconstructed vessels, and overall survival (OS) outcomes were evaluated. Univariate and multivariate Cox regression and logistic regression analyses were performed to identify independent risk factors for postoperative vascular stenosis and poor survival. Results: All patients presented with variable extents of vascular defects and anatomical sites of vascular invasion, including the portal vein–superior mesenteric vein confluence ( n = 110), superior mesenteric vein ( n = 50), portal vein ( n = 25), inferior vena cava ( n = 1), and left renal vein ( n = 1). The postoperative complication rate was 33.2%, and the perioperative mortality rate was 7.0% both of which were comparable across different AVG reconstruction configurations. Excluding benign lesions, the overall R0 resection rate was 92.2% (166/180) across the malignant cohort, specifically 92.3% (132/143) in patients with pancreatic cancer, and 90.0% (27/30) in those with cholangiocarcinoma. The median follow-up duration was 88.0 months. Cumulative graft stenosis rates at 1-year, 2-year, and 3-year postoperatively were 18.4, 21.8, and 33.3%, respectively. Median overall survival and disease-free survival were 13 and 9 months, respectively, with 5-year overall survival and disease-free survival rates of 12.2 and 12.7%. The type of AVG used for vascular reconstruction had no significant short-term or long-term oncological outcomes (both p > 0.05). Conclusion: The use of AVGs is a feasible and effective technique for vascular reconstruction following major venous resection in patients with locally advanced pancreatic and biliary malignancies. Its application achieved favorable R0 resection rates and acceptable long-term patency and survival outcomes.

Background: Prognosis after liver transplantation for hepatocellular carcinoma (HCC) is driven mainly by tumor burden, vascular invasion, and alpha-fetoprotein. Routine pretransplant nutritional indices may add information on recipient condition, but direct comparisons are scarce and recurrence is not always analyzed with competing mortality. Methods: The development cohort included 254 adults undergoing first liver transplantation for pathologically confirmed HCC. Overall survival (OS) was the primary outcome; time to recurrence (TTR) was analyzed with death before recurrence as a competing event. Five nutritional indices were compared using Kaplan-Meier estimates, Cox regression, and restricted cubic splines. AGR underwent subgroup and Fine-Gray analyses. Conventional staging, AGR alone, a pretransplant clinical model, and the clinical model plus AGR were compared by discrimination, calibration, and decision curves. Time-dependent ROC curves included bootstrap 95% confidence intervals. Fixed development-model parameters were evaluated in an independent external validation cohort of 58 recipients. Results: Eighty-five patients died and 51 experienced recurrence. In the fully adjusted Cox model, each 1-standard-deviation (SD) increase in AGR was associated with lower overall mortality [hazard ratio (HR), 0.64; 95% confidence interval (CI), 0.48-0.84; P = 0.002]; GNRI was also associated with OS (HR, 0.75; 95% CI, 0.58-0.98; P = 0.037). The AGR-OS association was nonlinear (overall P < 0.001; nonlinear P = 0.004). The adjusted recurrence subdistribution HR was 0.66 per 1-SD increase in AGR (95% CI, 0.47-0.94; P = 0.022). Adding AGR increased the OS C-index from 0.750 to 0.780 in the development cohort and from 0.645 to 0.686 externally. At 3 years, the external AUC was 0.713 (95% CI, 0.574-0.834) for the clinical model and 0.764 (95% CI, 0.614-0.888) after AGR was added. Conclusions: AGR showed the most consistent associations with OS and recurrence among the five indices. Its addition produced modest improvements in the development cohort, with changes in the same direction during external validation. AGR may complement, but should not replace, established tumor-related prognostic factors.

Liver transplantation is key for HCC treatment. Inflammatory indexes offer non-invasive, accessible prognostic insights for hepatocellular carcinoma patients.

Hepatobiliary and pancreatic cancers remain the most aggressive and lethal malignancies globally. Despite the rapid progression in systemic anti-tumor treatment, surgery remains the primary curative option but the clinical outcome is still unsatisfying. Limited surgical resection rate and high postoperative recurrence rate are the most important factors that restricting treatment outcome, and optimizing prognostic prediction strategies, innovating surgical techniques, and developing novel adjuvant and neoadjuvant therapies are important approaches to enhancing the therapeutic efficacy of surgery.Nowadays, continuous advancement of auxiliary examinations lays technical foundation for the precise diagnosis, classification, and prognostic prediction of hepatobiliary and pancreatic tumors. The application of machine learning (ML) models, deep learning (DL) models, and artificial intelligence (AI) in clinical practice further improves the accuracy of these predictions. The combination of these techniques enable non-invasive, high-dimensional evaluation of tumors and comprehensive data integration, and represent key future directions.Based on above backgrounds, this research topic was designed to address this translational need. It brings together studies that explore how novel surgical techniques, multidisciplinary adjuvant therapies, and ML/DL/AI-driven prognostic models can enhance mechanistic understanding and support the precise implementation of individualized evaluation and treatment for hepatobiliary and pancreatic cancers.The contributing articles submitted to this research topic including surgical innovation, ML/DL/AI-driven prediction model establishment, multidisciplinary adjuvant therapies, and molecular biomarker discovery. Collectively, these articles offer practical insights that may enhance clinical decision-making and outcomes in the management of hepatobiliary and pancreatic cancers.As the rapid development in minimal invasive technique, laparoscopic and robotic surgeries are gradually adopted in clinical practice worldwide. For distal pancreatectomy (DP), the comparison of outcomes between minimal invasive DP and traditional open DP was still limited. Wang et al. conducted a meta-analysis of propensity score-matched studies which bridge this gap. The study demonstrated that robotic DP offers significant potential advantages over open DP in reduced intraoperative blood loss, shorter hospital stays, higher spleen preservation rates, and a lower incidence of surgical site infections, highlighting the clinical utility of robotic DP. Meanwhile, Chen et al. also proposed a novel laparoscopic counterclockwise modular mesohepatectomy procedure which illustrated how to optimize the dissection sequence and utilizing Laennec's capsule theory to overcome the technical challenge of anatomical liver resection for centrally located tumors.Besides, researchers also focused on predicting and minimizing severe complications to improve the surgical outcome, especially for clinically relevant postoperative pancreatic fistula (CR-POPF). Zhang et al. proposed a logistic regression model including pancreatic duct diameter, pancreatic texture, CA19-9 levels, and body mass index for CR-POPF, providing a practical and effective model for CR-POPF risk assessment. Meanwhile, Ye et al. implemented a novel modified Blumgart pancreaticojejunostomy technique featuring an anterior anchoring suture approach, continuous mattress suturing technique and greater omental reinforcement. This approach not only reduced pancreatic tissue and parenchymal tearing, but also provided a physical barrier, thereby reducing the CR-POPF incidence after pancreaticoduodenectomy, particularly for high-risk patients.Advanced computational tools are redefining preoperative assessments, and the submitted articles provided a series of ML/DL/AI-driven prediction model for hepatobiliary and pancreatic cancers. Chae et al. explored the capabilities of large language model (LLM) DeepSeek-R1 in preoperative T-stage discrimination for gallbladder cancer. Strikingly, the LLM analyzing unstructured radiology reports achieved high accuracy (89.6%) and outperformed traditional blood-biomarker ML models, highlighting the potential of narrative clinical data. Zhao et al. developed a nomogram integrating contrast-enhanced CT radiomics and clinical factors to predict glypican-3 (GPC3)-positive HCC. This non-invasive tool successfully identifies target populations for GPC3-directed therapies, overcoming the limitations of invasive biopsies. Furthermore, Yang et al. constructed an interpretable gradient boosting machine (GBM) model to predict overall survival for patients with large HCC following curative hepatectomy, facilitating personalized risk assessments in these patients. Yin et al. also put forward a multi-algorithm ML prognostic model for distal cholangiocarcinoma incorporating surgical features and derived neutrophil-to-lymphocyte ratio, effectively enhancing the predictive accuracy for postoperative clinical outcomes.Effective management of hepatobiliary and pancreatic tumor requires systemic and locoregional therapies. Ran et al. evaluated a triplet adjuvant regimen (S-1, nab-paclitaxel, and gemcitabine) for resected PDAC, reporting significantly prolonged overall and disease-free survival alongside a favorable toxicity profile compared to traditional dual therapies. This research put forward an effective chemotherapy regimen for resectable PDAC and may provide potential survival benefits for these patients. Meanwhile, two case reports also reported potential strategy for the intervention of advanced pancreatic cancer and rare pathological type of pancreatic carcinoma. In a case of advanced pancreatic cancer complicated by malignant obstructive jaundice, Li et al. demonstrated the efficacy of a sequential multidisciplinary approach-combining PTCD, a biliary seed stent, tomotherapy, and targeted-immunotherapy-to restore biliary...

Refractory hypersplenism after liver transplantation (LT), defined in this study as persistent cytopenia and splenomegaly beyond 6 months after LT, is a clinically important postoperative condition. However, reliable methods for preoperative risk stratification remain limited. This study aimed to identify preoperative risk factors and to develop a multimodal deep learning model integrating clinical and CT-derived imaging features for predicting refractory hypersplenism after LT. Patients who underwent LT between 2013 and 2023 were retrospectively analyzed and classified into remission (n = 120) and refractory (n = 44) hypersplenism groups. Clinical, laboratory, and CT-derived splenic parameters were compared. Radiomic features were extracted from preoperative contrast-enhanced CT images. Logistic regression identified independent predictors, and single-modality (clinical or CT) and multimodal (clinical–radiomic) deep learning models were developed. Model performance was evaluated using AUC, F1-score, and accuracy across training, validation, and test cohorts. Preoperative platelet count (OR = 1.04, p = 0.006), splenic length (OR = 0.96, p < 0.001), and splenic vein diameter (OR = 0.81, p = 0.001) were independent predictors. The multimodal model showed the best overall performance (AUC = 0.857, F1 = 0.842), outperforming logistic regression (P = 0.022). Attention maps highlighted the spleen and splenic hilum were among the most influential regions for prediction. Preoperative splenic morphology and imaging features related to splenic hemodynamic burden were associated with refractory hypersplenism after LT. The proposed clinical–radiomic multimodal deep learning model showed promising predictive performance and may provide useful supplementary information for preoperative risk stratification. Further validation in larger multicenter cohorts is needed before broader clinical application.

Background: Inducing transplant tolerance to achieve long-term graft survival without immunosuppression remains a central objective in liver transplantation. Methods: Using a rat liver transplantation model, we evaluated the effects of infusing rapamycin-modified tolerogenic dendritic cells (Rapa-tolDCs) on graft survival and tolerance induction. Underlying molecular and cellular mechanisms were investigated through activation and inhibition experiments to assess the generation, signaling pathways, and suppressive functions of regulatory T and B cell populations. Results: Infusion of Rapa-tolDCs induced donor-specific tolerance and markedly prolonged graft survival (median survival time of 65 days, maximum of 102 days). Mechanistically, Rapa-tolDCs, characterized by low expression of Siglec1 and Spp1, functioned independently of the PI3K–mTOR pathway and promoted the differentiation and proliferation of CD8+CD45RClow/− regulatory T cells (CD8+CD45RClow/− Tregs). We identified that these Tregs acquired MHC-II molecules from donor cells via trogocytosis, becoming immune chimeric cells that predominantly secreted interleukin-10 (IL-10) to mediate immune suppression. The generation of these MHC-II+CD8+ Tregs was regulated by the Wnt5a/Fzd4/RhoD signaling axis. Furthermore, elevated levels of Foxp3+ Tregs and IL-10+ Bregs were found to contribute to prolonged graft survival following Rapa-tolDC infusion. Conclusions: These findings delineate a complete mechanistic pathway from cell therapy to donor-specific tolerance and provide a foundational strategy for clinical tolerance induction and post-transplant immunomodulation.

Background: Chronic inflammation is increasingly recognized as a fundamental driver of pancreatic ductal adenocarcinoma (PDAC) initiation and progression. Although numerous bioinformatics studies have characterized genetic alterations in PDAC, the key inflammatory regulators that bridge tumor cells and the immunosuppressive stroma remain unclear. Methods: We conducted an integrative multi-omics analysis of TCGA, GEO, and ArrayExpress datasets to define inflammation-associated molecular signatures in PDAC. Differentially expressed genes were analyzed through pathway enrichment, protein–protein interaction modeling, and immune infiltration profiling. Immunotherapeutic relevance was assessed using the IMvigor210 cohort and TIDE algorithm, while drug repurposing candidates were identified via molecular docking. Single-cell RNA sequencing and in vitro functional assays were employed to validate gene expression patterns and mechanistic functions within the PDAC microenvironment. Results: Our multi-cohort analysis revealed a robust inflammation-associated gene network in PDAC, with SERPINE1 emerging as a consistent central hub. Elevated SERPINE1 expression was tightly linked to a profoundly immunosuppressive tumor microenvironment and predicted diminished responsiveness to immunotherapy across datasets. Structure-based molecular docking further identified Lenvatinib and Dasatinib as previously unappreciated candidate inhibitors of SERPINE1, suggesting actionable therapeutic opportunities. Single-cell transcriptomic profiling resolved nine major cellular compartments and pinpointed fibroblasts as the principal stromal niche orchestrating SERPINE1-driven crosstalk between inflammation and immune evasion, a cellular origin that has not been systematically defined before. Translational analyses demonstrated consistently elevated SERPINE1 in tumor tissues, and functional validation using CRISPR-mediated knockout in PDAC cell lines significantly impaired proliferation and migration while inducing robust apoptosis, thereby establishing SERPINE1 as a previously underappreciated but essential driver of PDAC aggressiveness. Conclusions: This integrative multi-omics and single-cell analysis establishes SERPINE1 as a central orchestrator of inflammation-driven stromal remodeling and immune evasion in PDAC. Its strong prognostic power, combined with newly revealed druggability, positions SERPINE1 as a tractable therapeutic axis for precision immunotherapy and rational drug repurposing. These findings provide a mechanistically grounded and clinically actionable entry point into targeting the inflammatory tumor microenvironment of pancreatic cancer.

Purpose This study aimed to explore the value of neutrophil-to-platelet ratio (NPR) in predicting postoperative prognosis of distal cholangiocarcinoma(dCCA). Methods The optimal cutoff value of NPR was determined using a receiver operator characteristic curve (ROC) analysis. Patients were divided into low- and high-NPR groups, and perioperative data and long-term survival were compared. Independent risk factors for postoperative tumor recurrence and long-term survival were identified using univariate and multivariate analyses. An NPR-based prediction model was established and verified using ROC, calibration curve, and decision curve analyses. Results A total of 168 patients were included. The area under the ROC curve of preoperative NPR was 0.617, and the optimal cutoff value was 0.014. Postoperative disease-free survival and overall survival were significantly longer in the low-NPR group. Univariate and multivariate analyses indicated that NPR > 0.014 was an independent risk factor for postoperative recurrence (RR = 1.745, 95%CI: 1.027–2.965) and long-term survival (RR = 1.837, 95%CI: 1.127–2.993). The NPR-based prediction model could improve the predictive ability for the postoperative prognosis. Patients with a high preoperative NPR may have an advanced TNM stage and portal vein invasion. Conclusion Elevated NPR is an independent risk factor for postoperative tumor recurrence and a poor long-term prognosis in patients with dCCA.

Background: Neutrophil extracellular traps (NETs) play a crucial role in thrombosis formation and tumor progression. However, the association between NETs and portal vein thrombosis (PVT) is not well understood. Methods: We retrospectively and prospectively developed and validated a predictive model for postoperative PVT following radical surgery for pancreatic ductal adenocarcinoma (PDAC) from Jan. 2011 to Dec. 2022 and Jan. 2023 to Dec. 2023. 30 clinicopathological variables, including the novel biomarker NETs, were evaluated. 11 ML algorithms were compared based on several evaluation indexes including AUC. SHapley Additive exPlanations (SHAP) analysis was utilized for feature ranking and interpretability. Results: In the retrospective study, a total of 306 patients were included, among which 70 (22.9%) developed PVT. In the prospective study, a total of 64 patients were enrolled and the incidence of PVT was 25%. eXtreme gradient boosting (XGBoost) demonstrated superior discriminative performance among 11 evaluated ML-models. After feature reduction based on ranked feature importance, a final interpretable XGBoost model comprising 7 variables was established. This model accurately predicted postoperative PVT in both training (AUC = 0.906) and validation (AUC = 0.823) cohorts, and has been translated into a convenient tool to facilitate its utility in clinical settings. Conclusion: In patients with PDAC, higher NETs levels are correlated with an increased risk of developing PVT and are indicative of poor prognosis. Assessment of Cit-H3 and D-dimer levels may aid clinicians in predicting PVT.

The Albumin-Bilirubin Score (ALBI) serves as an indicator of nutritional status and is a widely recognized prognostic biomarker in cancer patients. The purpose of this research is to evaluate the association between ALBI and survival outcomes in patients with distal cholangiocarcinoma (dCCA) after radical surgery, and develop a nomogram model based on the ALBI to predict individual survival. A total of 177 individuals with dCCA receiving surgery from Jan 2011 to Jan 2022 were enrolled in the research. The association between ALBI and clinicopathologic factors was investigated. The impact of ALBI on recurrence-free survival (RFS) and overall survival (OS) was evaluated by Kaplan–Meier curves and Cox proportional hazards models. Nomograms based on ALBI and other prognostic variables screened by multivariate analysis were produced in predicting RFS and OS of dCCA patients following radical surgery, and the nomograms were evaluated by the consistency index (C-index), calibration curve and decision curve analysis (DCA) curve. The optimal cut-off value for ALBI was -1.67, and the area under the ROC curve (AUC) was 0.71. The High-ALBI group had a considerably shorter RFS and OS (P < 0.001). Multivariate analysis revealed that the ALBI, degree of differentiation, portal vein invasion, and lymph node invasion were significant prognostic factors for RFS, and that the ALBI, CA19-9, degree of differentiation, lymph node invasion, and portal vein invasion were significant prognostic factors for OS. The nomograms were constructed based on these significant prognostic factors. The calibration curves displayed good consistency between actual and predicted probability. Nomograms based on these variables have better discriminant ability in predicting RFS and OS compared with the American Joint Committee on Cancer (AJCC) TNM stage. Moreover, the scores predicted by the nomogram enabled patient stratification into low-points and high-points groups. Kaplan–Meier curves demonstrated that patients in the high-points group had considerably better prognoses than those in the low-points group (p < 0.001). ALBI was an independent prognostic factor in predicting RFS and OS of dCCA patients after radical surgery. The nomograms based on ALBI can provide reliable, personalized survival prediction for dCCA.