BACKGROUND AND OBJECTIVES

Hepatobiliarypancreatic tuberculosis (HBPTB) is a less common form of tuberculosis that often presents as malignancy or lithiasis. Advances in diagnostics and minimally invasive procedures have led to the detection of more patients with milder forms of TB requiring surgical management. Due to the low incidence rates and lack of standardized approaches, additional studies are needed to improve patient outcomes. This study examined the risk factors, diagnostic methods, and treatments for HBPTB patients at the University of the Philippines – Philippine General Hospital (UP-PGH) from January 1, 2014 to December 31, 2021.

This retrospective descriptive study utilized our institutional database to identify patients who underwent a surgical procedure for HBPTB and their associated risk factors. Inclusion criteria required biopsy or microbiologic proof of tuberculous involvement of the biliary tract or nearby structures.

Among a total of 45 patients, the most common admitting diagnosis were HBP tuberculosis (37.8%) and malignancy (35.6%). 47.6% of patients had a previous or concurrent TB exposure. Sixty percent had subclinical malnutrition indicated by normal weight and low albumin. The liver (37.8%) and the bile ducts (33.3%) were the most common organs involved. The most common surgical procedures done were ultrasound-guided liver biopsy, biliary enteric anastomosis, percutaneous transhepatic biliary drainage (PTBD), and endoscopic retrograde cholangiopancreatography with or without stenting (ERCP).

This study provides additional data for clinicians to tailor diagnostic and treatment plans accordingly. Striking a balance between surgical procedures and appropriate anti-tuberculous therapy (ATT) is essential for successful treatment. Local data can be useful to help identify tuberculosis patterns unique to Filipinos and highlight socio-economic factors contributing to this rare presentation of TB.

Humans ; Endoscopic Ultrasound-Guided Fine Needle Aspiration ; Pancreatic Neoplasms ; Pancreas ; Needles

Pancreatic cancer, notorious for its late diagnosis and aggressive progression, poses a substantial challenge owing to scarce treatment alternatives. This review endeavors to furnish a holistic insight into pancreatic cancer, encompassing its epidemiology, genomic characterization, risk factors, diagnosis, therapeutic strategies, and treatment resistance mechanisms. We delve into identifying risk factors, including genetic predisposition and environmental exposures, and explore recent research advancements in precursor lesions and molecular subtypes of pancreatic cancer. Additionally, we highlight the development and application of multi-omics approaches in pancreatic cancer research and discuss the latest combinations of pancreatic cancer biomarkers and their efficacy. We also dissect the primary mechanisms underlying treatment resistance in this malignancy, illustrating the latest therapeutic options and advancements in the field. Conclusively, we accentuate the urgent demand for more extensive research to enhance the prognosis for pancreatic cancer patients.
Humans ; Pancreatic Neoplasms/therapy* ; Prognosis ; Pancreas/pathology* ; Genetic Predisposition to Disease ; Genomics

OBJECTIVE: To propose a non-contrast CT-based algorithm for automated and accurate detection of pancreatic lesions at a low cost. METHODS: With Faster RCNN as the benchmark model, an advanced Faster RCNN (aFaster RCNN) model for pancreatic lesions detection based on plain CT was constructed. The model uses the residual connection network Resnet50 as the feature extraction module to extract the deep image features of pancreatic lesions. According to the morphology of pancreatic lesions, 9 anchor frame sizes were redesigned to construct the RPN module. A new Bounding Box regression loss function was proposed to constrain the training process of RPN module regression subnetwork by comprehensively considering the constraints of the lesion shape and anatomical structure. Finally, a detection frame was generated using the detector in the second stage. The data from a total of 728 cases of pancreatic diseases from 4 clinical centers in China were used for training (518 cases, 71.15%) and testing (210 cases, 28.85%) of the model. The performance of aFaster RCNN was verified through ablation experiments and comparison experiments with 3 classical target detection models SSD, YOLO and CenterNet. RESULTS: The aFaster RCNN model for pancreatic lesion detection achieved recall rates of 73.64% at the image level and 92.38% at the patient level, with an average precision of 45.29% and 53.80% at the image and patient levels, respectively, which were higher than those of the 3 models for comparison. CONCLUSION The proposed method can effectively extract the imaging features of pancreatic lesions from non-contrast CT images to detect the pancreatic lesions.
Humans ; Pancreas/diagnostic imaging* ; Algorithms ; China ; Pancreatic Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed

Objective: To analyze the clinical characteristics and prognosis of primary and secondary pancreatic diffuse large B-cell lymphoma (DLBCL) . Methods: Clinical data of patients with pancreatic DLBCL admitted at Shanghai Rui Jin Hospital affiliated with Shanghai Jiao Tong University School of Medicine from April 2003 to June 2020 were analyzed. Gene mutation profiles were evaluated by targeted sequencing (55 lymphoma-related genes). Univariate and multivariate Cox regression models were used to evaluate the prognostic factors of overall survival (OS) and progression-free survival (PFS) . Results: Overall, 80 patients were included; 12 patients had primary pancreatic DLBCL (PPDLBCL), and 68 patients had secondary pancreatic DLBCL (SPDLBCL). Compared with those with PPDLBCL, patients with SPDLBCL had a higher number of affected extranodal sites (P<0.001) and had higher IPI scores (P=0.013). There was no significant difference in the OS (P=0.120) and PFS (P=0.067) between the two groups. Multivariate analysis indicated that IPI intermediate-high/high risk (P=0.025) and double expressor (DE) (P=0.017) were independent adverse prognostic factors of OS in patients with pancreatic DLBCL. IPI intermediate-high/high risk (P=0.021) was an independent adverse prognostic factor of PFS in patients with pancreatic DLBCL. Targeted sequencing of 29 patients showed that the mutation frequency of PIM1, SGK1, BTG2, FAS, MYC, and MYD88 in patients with pancreatic DLBCL were all >20%. PIM1 (P=0.006 for OS, P=0.032 for PFS) and MYD88 (P=0.001 for OS, P=0.017 for PFS) mutations were associated with poor OS and PFS in patients with SPDLBCL. Conclusion: There was no significant difference in the OS and PFS between patients with PPDLBCL and those with SPDLBCL. IPI intermediate-high/high risk and DE were adverse prognostic factors of pancreatic DLBCL. PIM1, SGK1, BTG2, FAS, MYC, and MYD88 were common mutations in pancreatic DLBCL. PIM1 and MYD88 mutations indicated worse prognosis.
Humans ; Myeloid Differentiation Factor 88 ; Disease-Free Survival ; Retrospective Studies ; China/epidemiology* ; Prognosis ; Lymphoma, Large B-Cell, Diffuse/drug therapy* ; Antineoplastic Combined Chemotherapy Protocols ; Pancreas/pathology* ; Immediate-Early Proteins/therapeutic use* ; Tumor Suppressor Proteins

Humans ; Sarcoma, Ewing/pathology* ; Pancreas/pathology*

As a severe malignant tumor of the digestive system,the highly invasive pancreatic cancer lacks typical preliminary symptoms. Rapid metastatic dissemination and difficulty in early-stage diagnosis preclude the chance of radical curative resection,hence resulting in a poor overall prognosis in most patients. In recent years,the wide application of the artificial intelligence(AI),represented by machine learning and deep learning,has developed rapidly in the field of medicine. All sorts of models based on AI have been applied to the screening, early diagnosis, treatment, prognosis prediction of patients with pancreatic cancer.Three-dimentional visualization and augmented reality navigation technologies have also been developed and applied in pancreatic cancer surgery.This paper reviews the status quo of AI application in pancreatic cancer from various aspects,and anticipates its future application prospects.
Humans ; Artificial Intelligence ; Pancreatic Neoplasms/surgery* ; Pancreas ; Machine Learning

Pancreatic cancer is a malignant tumor of digestive system with poor prognosis,and surgical resection is still the only hope for a radical cure. Although the current consensus and guidelines describe in detail the standard and extended range of lymph node dissection,the selection of specific range of lymph node dissection and its impact on prognosis are still controversial. Current studies have not only proposed some improved extent of lymph node dissection, such as total mesopancreas excision and Heidelberg triangle dissection, but also suggested different extent of lymph node dissection for ventral and dorsal pancreatic head cancer. In addition, the prognosis of pancreatic head cancer in uncinate process and non-uncinate process is different after para-aortic lymph node dissection, which is worthy of further study. Neoadjuvant therapy or conversion therapy provides more surgical opportunities for patients with pancreatic cancer. For these patients, Heidelberg triangle dissection has potential value in improving prognosis. This paper summarizes the exploration and latest progress of standard and extended lymph node dissection, lymph node dissection of specific site of pancreatic head cancer and the extent of lymph node dissection after neoadjuvant/transformation therapy in recent years.
Humans ; Lymph Node Excision ; Pancreatic Neoplasms/pathology* ; Lymph Nodes/pathology* ; Pancreas/pathology* ; Prognosis

The prognosis for pancreatic cancer is extremely poor. To improve the prognosis of pancreatic cancer, it is urgently needed to improve early detection to advance treatment. And basically, it is also necessary to emphasise basic research to find novel therapies. By promoting the disease-centered multidisciplinary team model, researchers should achieve high-quality closed-loop process management of the entire life cycle which consists of prevention, screening, diagnosis, treatment, rehabilitation,and follow-up, with the objective of establishing a standard clinical process to improve the outcome in essence. This article summarized the progress of pancreatic cancer at different stages of the whole cycle management recently and shared the experience of pancreatic cancer treatment from the author's team in the past ten years.
Humans ; Pancreatic Neoplasms/therapy* ; Pancreas ; Prognosis

Postpancreatectomy acute pancreatitis (PPAP) is an acute inflammation of the remnant pancreas in the early postoperative period caused by a variety of factors. With the progress of related research,PPAP has been confirmed as an independent risk factor for many severe complications such as postoperative pancreatic fistula. In some cases, it progresses to necrotizing PPAP, increasing the risk of mortality. Currently, the International Study Group for Pancreatic Surgery has standardized and graded PPAP as an independent complication, taking into account factors including serum amylase, radiological features, and clinical impact. This review summarizes how the concept of PPAP was proposed, as well as the latest progress in the research related to its etiology, prognosis, prevention, and treatment. However, given the large heterogeneity of relevant studies and the fact that they were mostly retrospective, in the future, it is necessary to place more emphasis on PPAP and elucidate the problems through more standardized studies to optimize strategies for the prevention and management of complications after pancreatic surgery.
Humans ; Pancreatitis/complications* ; Retrospective Studies ; Acute Disease ; Pancreas ; Postoperative Complications/etiology* ; Pancreatic Fistula/etiology* ; Pancreaticoduodenectomy/adverse effects*