The intratumor microbiome imbalance in pancreatic cancer promotes a tolerogenic immune response and triggers immunotherapy resistance. Here we show that Lactobacillus rhamnosus GG probiotics, outfitted with a gallium-polyphenol network (LGG@Ga-poly), bolster immunotherapy in pancreatic cancer by modulating microbiota-immune interactions. Upon oral administration, LGG@Ga-poly targets pancreatic tumors specifically, and selectively eradicates tumor-promoting Proteobacteria and microbiota-derived lipopolysaccharides through a gallium-facilitated disruption of bacterial iron respiration. This elimination of intratumor microbiota impedes the activation of tumoral Toll-like receptors, thus reducing immunosuppressive PD-L1 and interleukin-1β expression by tumor cells, diminishing immunotolerant myeloid populations, and improving the infiltration of cytotoxic T lymphocytes in tumors. Moreover, LGG@Ga-poly hampers pancreatic tumor growth in both preventive and therapeutic contexts, and amplifies the antitumor efficacy of immune checkpoint blockade in preclinical cancer models in female mice. Overall, we offer evidence that thoughtfully designed biomaterials targeting intratumor microbiota can efficaciously augment immunotherapy for the challenging pancreatic cancer.

BACKGROUND: The early detection of pancreatic cancer is an important step in reducing mortality by offering patients curative treatment. Screening strategies in risk populations and by means of different detection methods have been economically evaluated. However, a synthesis of screening studies to inform resource allocation towards early detection within the disease area has not been done. Therefore, studies evaluating the cost-effectiveness and costs of screening for pancreatic cancer should be systematically reviewed.
METHODS: A systematic review of economic evaluations reporting the cost-effectiveness or costs of pancreatic cancer screening will be conducted. The electronic databases Medline, Web of Science and EconLit will be searched without geographical or time restrictions. Two independent reviewers will select eligible studies based on predefined criteria. The study quality will be assessed using the Consolidated Health Economic Evaluation Reporting Standards statement and the Bias in Economic Evaluation checklist. One reviewer will extract relevant data and a second reviewer will cross-check compliance with the extraction sheet. Key items will include characteristics of screened individuals, the screening strategies used, and costs, health effects and cost-effectiveness as study outputs. Differences of opinion between the reviewers will be solved by consulting a third reviewer.
BACKGROUND: Ethics approval is not required for this study since no original data will be collected. The results will be disseminated through presentations at conferences and publication in a peer-reviewed journal. The results of the systematic review will inform future economic evaluations of pancreatic screening, which provide guidance for decision-making in healthcare resource prioritisation.
UNASSIGNED: CRD42023475348.

BACKGROUND: Pancreatic adenocarcinomas (PAADs) often exhibit a \"cold\" or immunosuppressive tumor milieu, which is associated with resistance to immune checkpoint blockade therapy; however, the underlying mechanisms are incompletely understood. Here, we aimed to improve our understanding of the molecular mechanisms occurring in the tumor microenvironment and to identify biomarkers, therapeutic targets, and potential drugs to improve PAAD treatment.
METHODS: Patients were categorized according to immunologically hot or cold PAAD subtypes with distinct disease outcomes. Cox regression and weighted correlation network analysis were performed to construct a novel gene signature, referred to as \'Downregulated in hot tumors, Prognostic, and Immune-Related Genes\' (DPIRGs), which was used to develop prognostic models for PAAD via machine learning (ML). The role of DPIRGs in PAAD was comprehensively analyzed, and biomarker genes able to distinguish PAAD immune subtypes and predict prognosis were identified by ML. The expression of biomarkers was verified using public single-cell transcriptomic and proteomic resources. Drug candidates for turning cold tumors hot and corresponding target proteins were identified via molecular docking studies.
RESULTS: Using the DPIRG signature as input data, a combination of survival random forest and partial least squares regression Cox was selected from 137 ML combinations to construct an optimized PAAD prognostic model. The effects and molecular mechanisms of DPIRGs were investigated by analysis of genetic/epigenetic alterations, immune infiltration, pathway enrichment, and miRNA regulation. Biomarkers and potential therapeutic targets, including PLEC, TRPV1, and ITGB4, among others, were identified, and the cell type-specific expression of the biomarkers was validated. Drug candidates, including thalidomide, SB-431542, and bleomycin A2, were identified based on their ability to modulate DPIRG expression favorably.
CONCLUSIONS: By combining multiple ML algorithms, we developed a novel prognostic model with excellent performance in PAAD cohorts. ML also proved to be powerful for identifying biomarkers and potential targets for improved PAAD patient stratification and immunotherapy.

The development of the pancreatic head originates from the fusion of the ventral and dorsal pancreatic primordia during embryonic development. Theoretically, the origin of pancreatic head cancer also exists from the ventral pancreas and the dorsal pancreas. Among 49 patients with pancreatic head cancer, pancreatic head cancer was divided into pancreatic head cancer originating from the ventral (PHCv) or dorsal pancreas (PHCd) through imaging and pathological classification. The clinical data was collected and compared between the PHCv group and the PHCd group. The results showed that the patients from the PHCd group had worse long-term survival than those from the PHCv group (10 months vs 14.5 months). Similarly, the progression-free survival (PFS) results also indicate that patients from the PHCd group had a shorter time than those from the PHCv group (5 months vs 9.5 months). Further stratified analysis of potentially related factors showed that microvascular invasion is related to poor prognosis, and patients with pancreatic head cancer derived from the dorsal pancreas are more likely to develop microvascular invasion.

The aim of this study was to evaluate the cutoff value for identifying malignance in main pancreatic duct (MPD)-involved intraductal papillary mucinous neoplasm (IPMN) with an MPD diameter ranging from 5 to 10 mm. Clinical-radiological characteristics of 142 patients, including MPD-involved IPMNs (n = 53) and branch-duct (BD)-IPMNs (n = 89) were analyzed. Logistic regression analysis was used to determine the risk factors of malignant IPMNs and invasive carcinoma. ROC curves were used to identify different cutoffs in terms of preoperative MPD values to predict the presence of invasive carcinoma as well as malignant IPMNs, and the prediction performance was evaluated. For MPD-involved IPMNs (5 mm < MPD < 10 mm), MPD diameter of 7.5 mm for discriminating malignant IPMNs (area under curve [AUC] = 0.67) and 7.7 mm for discriminating invasive IPMNs (AUC = 0.56) were found to be the optimal cutoff values at receiver operating characteristic curve (ROC) analysis. MPD > 7.5 mm and carbohydrate antigen19-9 (Ca19-9) > 37 U/ml were found to be predictors of malignant IPMNs at univariate, and MPD > 7.5 mm was a predictor in multivariate analysis in MPD-involved IPMNs. The AUC of the ROC curve of MPD (7.5 mm) combined with Ca19-9 in identifying malignant IPMNs was 0.73 in MPD-involved IPMNs. MPD (7.5 mm) combined with Ca19-9 performed well in identifying malignant IPMNs in MPD-involved IPMNs.

OBJECTIVE: To explore the predictive potential of serial computed tomography (CT) radiology reports for pancreatic cancer survival using natural language processing (NLP).
METHODS: Deep-transfer-learning-based NLP models were retrospectively trained and tested with serial, free-text CT reports, and survival information of consecutive patients diagnosed with pancreatic cancer in a Korean tertiary hospital was extracted. Randomly selected patients with pancreatic cancer and their serial CT reports from an independent tertiary hospital in the United States were included in the external testing data set. The concordance index (c-index) of predicted survival and actual survival, and area under the receiver operating characteristic curve (AUROC) for predicting 1-year survival were calculated.
RESULTS: Between January 2004 and June 2021, 2,677 patients with 12,255 CT reports and 670 patients with 3,058 CT reports were allocated to training and internal testing data sets, respectively. ClinicalBERT (Bidirectional Encoder Representations from Transformers) model trained on the single, first CT reports showed a c-index of 0.653 and AUROC of 0.722 in predicting the overall survival of patients with pancreatic cancer. ClinicalBERT trained on up to 15 consecutive reports from the initial report showed an improved c-index of 0.811 and AUROC of 0.911. On the external testing set with 273 patients with 1,947 CT reports, the AUROC was 0.888, indicating the generalizability of our model. Further analyses showed our model\'s contextual interpretation beyond specific phrases.
CONCLUSIONS: Deep-transfer-learning-based NLP model of serial CT reports can predict the survival of patients with pancreatic cancer. Clinical decisions can be supported by the developed model, with survival information extracted solely from serial radiology reports.

OBJECTIVE: Hepatic metastasis frequently occurs in patients who have undergone radical pancreatic resection for pancreatic cancer. Besides chemotherapy, various local treatment approaches targeting hepatic lesions have been explored. However, research on radiofrequency ablation (RFA) as a localized therapy for hepatic metastasis is limited. Therefore, we conducted this retrospective study to provide clinical evidence.
METHODS: This is a single-center, retrospective, cohort study. After radical pancreaticoduodenectomy, 32 patients developed metachronous hepatic metastasis with fewer than 3 lesions, the largest of which was less than 3 cm in diameter. These patients underwent combined treatment with chemotherapy and RFA. After 8 weeks of chemotherapy, patients received RFA for hepatic lesions. Additional chemotherapy was administered, and the patients\' tumor status and survival were monitored. The primary endpoint of this study was overall survival (OS). Factors affecting OS were analyzed using the Cox risk model.
RESULTS: Among the 32 patients, the mean OS was 28.4 months. Univariate and multivariate Cox regression analysis revealed that the time (in months) of liver metastasis (HR = 0.04, 95% CI: 0.01 to 0.19; P < 0.001), the number of liver metastases (HR = 7.08, 95% CI: 1.85 to 27.08, P = 0.004), and PD (progressive disease) response to the second round of chemotherapy (HR = 29.50, 95% CI: 1.46 to 597.27; P = 0.027) were independent predictors of poorer survival.
CONCLUSIONS: Combined therapy with RFA and chemotherapy is safe in patients with hepatic metastasis after radical pancreaticoduodenectomy. Early recurrence (≤12 months), three liver metastatic lesions, and a poor response to the second round of chemotherapy were associated with poor survival.

Background: Pancreatic cancers are known for their aggressive nature. This aggressiveness may be attributed to the presence of cancer stem cells (CSCs), which promote relapse, metastasis, and resistance to chemotherapy. Targeting CSCs is essential to reverse this aggressiveness in pancreatic malignancies. Literature highlights the association of PD-L1 expression with CSCs in various cancers, suggesting immunotherapy as a promising therapeutic approach. This study is aimed at investigating the potential of immunotherapy in pancreatic cancers by examining its association with selected CSC marker expression. Method: A retrospective cohort study was conducted involving 56 patients with confirmed diagnoses of pancreatic cancers at Aga Khan University Hospital from January 2015 to October 2022. After exclusions, based on refusal to provide consent or incomplete follow-up data, 38 patients were enrolled in the study. Immunohistochemistry was performed on formalin-fixed paraffin-embedded (FFPE) tumor tissue samples to assess the expression of CSC markers (CD133, CD44, and L1CAM) and immune checkpoint inhibitor marker (PD-L1). Statistical analysis was employed to determine associations between marker expression, clinical factors, and overall survival. Results: The study revealed that 86.8% of pancreatic cancer cases exhibited positive PD-L1 expression. Moreover, a significant association of PD-L1 expression was observed with the presence of CD44 protein (p = 0.030), as well as with the overall survival of patients (p = 0.023). Conclusion: Our findings show a significant association of PD-L1 with CD44 marker expression as well as patient survival. This research shows the potential to serve as the foundation for investigating the efficacy of immunotherapy in reducing CD44-expressing CSCs in pancreatic cancer, potentially enhancing patient outcomes.

BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postoperative liver metastasis in panNET patients.
METHODS: Clinical data, pathology slides, and radiographic images were collected from 163 panNET patients post-R0 resection at Fudan University Shanghai Cancer Center (FUSCC) and FUSCC Pathology Consultation Center. Digital image analysis and deep learning identified liver metastasis-related features in Ki67-stained whole slide images (WSIs) and enhanced CT scans to create a nomogram. The model\'s performance was validated in both internal and external test cohorts.
RESULTS: Multivariate logistic regression identified nerve infiltration as an independent risk factor for liver metastasis (p < 0.05). The Pathomics score, which was based on a hotspot and the heterogeneous distribution of Ki67 staining, showed improved predictive accuracy for liver metastasis (AUC = 0.799). The deep learning-radiomics (DLR) score achieved an AUC of 0.875. The integrated nomogram, which combines clinical, pathological, and imaging features, demonstrated outstanding performance, with an AUC of 0.985 in the training cohort and 0.961 in the validation cohort. High-risk group had a median recurrence-free survival of 28.5 months compared to 34.7 months for the low-risk group, showing significant correlation with prognosis (p < 0.05).
CONCLUSIONS: A new predictive model that integrates computational pathologic scores and deep learning-radiomics can better predict postoperative liver metastasis in panNET patients, aiding clinicians in developing personalized treatments.

Spleen-preserving distal pancreatectomy offers an alternative surgical approach to the traditional distal pancreatectomy combined with splenectomy for removing benign and low-grade malignant lesions in the distal pancreas, avoiding complications associated with splenectomy. This procedure can be accomplished either by resecting and ligating the splenic vessels (Warshaw technique) or by preserving them (Kimura technique). Currently, the widespread use of minimally invasive surgery has established laparoscopic and robotic approaches for spleen-preserving distal pancreatectomy as valid and safe options for treating such conditions. Our protocol aims to describe how the Warshaw and Kimura techniques of spleen-preserving distal pancreatectomy can be performed robotically. The first patient is a 36-year-old female with a neuroendocrine tumor (NET) in the pancreatic body who underwent a spleen-preserving distal pancreatectomy with the ligation of the splenic vessels (WT). The second patient is a 76-year-old male with chronic pancreatitis presenting with a dilated main pancreatic duct in the tail of the pancreas who underwent a spleen-preserving distal pancreatectomy with a vessel-preserving approach (KT).