Search Results (63)

Pancreatic ductal adenocarcinoma (PDAC) presents significant challenges in diagnosis, prevention, and treatment. Predictive biomarkers offer the potential to revolutionize clinical management, particularly in the preoperative setting, but their implementation requires careful consideration of ethical implications. This scoping review analyzes the ethical landscape of using immunohistochemistry (IHC) for molecular subtyping in PDAC, focusing on its utility, accessibility, and potential impact on patient care. We conducted a systematic literature search in the PubMed, Scopus and Google Scholar databases (2015–2025) using COVIDENCE, which identified 130 references. Of these, 79 were reviewed in a full-text format, and 9 ultimately met the inclusion criteria for our analysis. IHC offers several advantages as a companion diagnostic tool. It is relatively inexpensive, widely available in most pathology laboratories, and can be readily integrated into existing clinical workflows. This contrasts with more complex molecular subtyping methods, such as gene expression profiling, which can be costly, require specialized equipment and expertise, and may not be readily accessible in all clinical settings. Furthermore, accurate analysis of gene expression requires the localized targeting of individual cells; therefore, digesting the sample for bulk analysis would be less informative than using spatial localization techniques such as IHC. Because biomarker regulation can occur at the level of transcription or translation, protein-level assessment via IHC is often more accurate than mRNA analysis. Standardized IHC protocols for biomarker assessment are therefore essential for translating the molecular subtyping of PDAC into clinically actionable treatment strategies, especially for aggressive subtypes like basal-like tumors. This readily deployable IHC-based approach can optimize therapy selection, maximizing patient benefits and minimizing exposure to ineffective and potentially toxic treatments. This review critically analyzes the ethical dimensions of this method, grounded in the principles of autonomy, beneficence, non-maleficence, and justice. The review urges the medical community to fully utilize the potential of IHC-driven molecular subtyping to improve outcomes in PDAC, while ensuring equitable and responsible access to the benefits of precision oncology for all patients. Full article

Pancreatic cystic lesions (PCLs) are increasingly identified via computerized tomography (CT) and magnetic resonance (MR), with a prevalence of 2–45%. Distinguishing mucinous PCLs (M-PCLs), which include intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs) that can progress to pancreatic ductal adenocarcinoma, from non-mucinous PCLs (NM-PCLs) is essential. Carcinoembryonic antigen (CEA) remains widely used but often demonstrates limited sensitivity and specificity. In contrast, endoscopic ultrasound-guided measurement of intracystic glucose more accurately differentiates PCL subtypes, as tumor-related metabolic changes lower cyst fluid glucose in mucinous lesions. Numerous prospective and retrospective studies suggest a glucose cut-off between 30 and 50 mg/dL, yielding a sensitivity of 88–95% and specificity of 76–91%, frequently outperforming CEA. Additional benefits include immediate point-of-care assessment via standard glucometers and minimal interference from blood contamination. DNA-based biomarkers, including KRAS and GNAS mutations, enhance specificity (up to 99%) but exhibit moderate sensitivity (61–71%) and necessitate specialized, expensive platforms. Molecular analyses can be crucial in high-risk lesions, yet their uptake is constrained by technical challenges. In practice, combining glucose assessment with targeted molecular assays refines risk stratification and informs the choice between surgical resection or active surveillance. Future investigations should establish standardized glucose thresholds, improve the cost-effectiveness of genetic testing, and integrate advanced biomarkers into routine protocols. Ultimately, these strategies aim to optimize patient management, limit unnecessary interventions for benign lesions, and ensure timely therapy for lesions at risk of malignant transformation. Full article

Background/Objectives: Acute pancreatitis (AP) remains a pressing clinical challenge, largely due to its potential to lead to life-threatening complications and increased mortality. Over the years, numerous tools have been proposed to evaluate the intensity of AP and estimate likely health outcomes. Despite their usefulness, many of these assessment models are complex and rely on a wide array of clinical inputs, making them less practical in everyday healthcare settings. In contrast, the Aggregate Systemic Inflammation Index (AISI), which is calculated using routine blood count parameters, provides a simpler and more inclusive approach to measuring systemic inflammation. This research focuses on examining how effectively AISI can be used to gauge disease severity and project clinical trajectories in individuals affected by pancreatitis. Methods: This retrospective study reviewed the medical records of 412 individuals diagnosed with acute pancreatitis, all of whom received care at the Internal Medicine Clinic of Ankara Bilkent City Hospital between 1 April 2019 and 1 September 2024. The investigation encompassed a thorough analysis of patients’ demographic characteristics, lab parameters, and clinical findings, with special attention given to inflammatory markers, including the Aggregate Systemic Inflammation Index (AISI), its revised version, the Platelet-to-Lymphocyte Ratio (PLR), the Neutrophil-to-Lymphocyte Ratio (NLR), and the Systemic Inflammatory Response Index (SIRI). Comparative analyses between groups were performed using independent sample t-tests and one-way ANOVA, complemented by Tukey’s post hoc tests where appropriate. Correlations among continuous variables were determined through Pearson’s analysis, and the prognostic accuracy of both AISI and its modified form was assessed using Receiver Operating Characteristic (ROC) curve methodology. Results: The mean age among participants was 63.47 ± 17.92 years, while the average AISI value was calculated as 1183.89 ± 1067.42. Both the original and modified versions of the AISI index showed strong positive correlations with several key clinical measures, including prolonged hospitalization, a Glasgow score of 2 or above, BISAP, Ranson scoring, the revised Atlanta classification, and APACHE II. AISI was also significantly linked to the presence of complications and overall mortality (p < 0.01). Analysis through ROC curves demonstrated that an AISI level above 236.626 effectively predicted hospital stays exceeding 10 days, with a sensitivity of 94.40% and a specificity of 91.00%. Moreover, both AISI and its modified form reliably distinguished patients who had a Ranson score of zero, with high diagnostic accuracy. Conclusions: AISI and its modified version demonstrate a strong association with both the intensity and clinical course of acute pancreatitis. Thanks to their simplicity, low cost, and broad usability in healthcare settings, these indices hold considerable promise as practical and dependable tools for assessing the severity and likely outcomes of this increasingly prevalent disease. Full article

Background: Robot-assisted pylorus-preserving pancreaticoduodenectomy (RPPPD) has been increasingly adopted, leveraging the advantages of robotic technology. RPPPD is rarely performed using the da Vinci SP system. In this study, we address the technical issues encountered during the early experiences with robotic pylorus-preserving pancreaticoduodenectomy (RPPPD) using the da SP Vinci system and propose effective solutions. Method: We retrospectively analyzed the outcomes of seven patients who underwent RPPPD using the da Vinci SP system. The primary technical challenges included limited instrument maneuverability, difficulty in maintaining clear surgical views, and the need for precise anastomosis. Results: Postoperatively, all patients were discharged without significant complications, with no clinically relevant pancreatic fistulas observed. Only minimal scarring was observed postoperatively. In addition, our results showed that operative time gradually decreased. The operation time was significantly shorter in the RPPPD using the SP system group compared to the RPPPD using the multiport system group. Conclusions: Implementing enhanced preoperative planning, advanced intraoperative imaging, and specialized robotic tools can significantly improve surgical efficiency and patient outcomes. Full article

Acute pancreatitis (AP) presents a significant clinical challenge due to its wide range of severity, from mild cases to life-threatening complications such as severe acute pancreatitis (SAP), necrosis, and multi-organ failure. Traditional scoring systems, such as Ranson and BISAP, offer foundational tools for risk stratification but often lack early precision. This review aims to explore the transformative role of artificial intelligence (AI) and machine learning (ML) in AP management, focusing on their applications in diagnosis, severity prediction, complication management, and treatment optimization. A comprehensive analysis of recent studies was conducted, highlighting ML models such as XGBoost, neural networks, and multimodal approaches. These models integrate clinical, laboratory, and imaging data, including radiomics features, and are useful in diagnostic and prognostic accuracy in AP. Special attention was given to models addressing SAP, complications like acute kidney injury and acute respiratory distress syndrome, mortality, and recurrence. AI-based models achieved higher AUC values than traditional models in predicting acute pancreatitis outcomes. XGBoost reached an AUC of 0.93 for early SAP prediction, higher than BISAP (AUC 0.74) and APACHE II (AUC 0.81). PrismSAP, integrating multimodal data, achieved the highest AUC of 0.916. AI models also demonstrated superior accuracy in mortality prediction (AUC 0.975) and ARDS detection (AUC 0.891) AI and ML represent a transformative advance in AP management, facilitating personalized treatment, early risk stratification, and allowing resource utilization to be optimized. By addressing challenges such as model generalizability, ethical considerations, and clinical adoption, AI has the potential to significantly improve patient outcomes and redefine AP care standards globally. Full article

Background: Elemental or amino-acid-based formulas play a crucial role in pediatric nutrition, offering a therapeutic alternative when human milk is poorly tolerated or in specific disease states. Mostly used in various cases of allergy, maldigestion, or malnutrition, elemental formulas serve as a special therapeutic regimen in clinical practice to promote growth and development. Methods: This narrative review briefly surveys the literature of the past decade available on PubMed, Scopus, and Google Scholar, encompassing original research, review articles, and position papers. Results: The indications for using elemental formulas in pediatric clinical practice extend beyond cow’s milk allergy to conditions such as eosinophilic esophagitis, intestinal failure, Crohn’s disease, hepatic failure, chronic pancreatitis, and various neurological and other malnutrition-related disorders. Conclusions: Elemental formulas are associated with potential health benefits for pediatric patients in certain conditions, either as a first-line feeding option or under conditional use. Despite their drawbacks, they are regarded as a therapeutic tool with numerous indications, requiring careful implementation by a multidisciplinary team of healthcare experts. Future expert guidelines, including dosage, timing, and long-term effects of elemental diets, are needed for pediatric clinical practice. Full article

Pancreatic neuroendocrine tumors (pNETs) are rare but increasingly prevalent malignancies with varied prognoses and a diverse range of treatment options, including surgery, somatostatin analogues (SSAs), chemotherapy, targeted therapy, and peptide receptor radionuclide therapy (PRRT). This retrospective cohort study analyzed treatment patterns among 189 pNET patients treated between January 2010 and June 2021 at two Canadian cancer centres: the Verspeeten Family Cancer Centre (VFCC), which offers PRRT, and the Ottawa Hospital Cancer Centre (TOHCC), which does not at the time of the study. Data on demographics, tumor characteristics, and treatment modalities were collected, and statistical analyses were conducted using chi-square, Fisher’s exact test, and the Kruskal–Wallis test. Among eligible patients, 53% presented with stage IV disease. Surgical resection was the most common treatment, followed by SSAs, chemotherapy, PRRT, and targeted therapy. Stage IV patients at VFCC were significantly more likely to receive PRRT (60%) compared to TOHCC (6%) and underwent more PRRT cycles, with a higher prevalence of well-differentiated tumors observed at VFCC. With these differences it was clear that the non-PRRT centre was unable to provide patients with the same level of PRRT access during the study period compared to patients seen at the PRRT site. The findings underscore the critical role of PRRT availability in influencing treatment patterns and highlight the need for equitable access to specialized therapies across Canada to optimize outcomes for pNET patients. Full article

Endoscopic management of benign pancreaticobiliary disorders encompasses a range of procedures designed to address complications in gallstone disease, choledocholithiasis, and pancreatic disorders. Acute cholecystitis is typically treated with cholecystectomy or percutaneous drainage (PT-GBD), but for high-risk or future surgical candidates, alternative decompression methods, such as endoscopic transpapillary gallbladder drainage (ETP-GBD), and endoscopic ultrasound (EUS)-guided gallbladder drainage (EUS-GBD), are effective. PT-GBD is associated with significant discomfort as well as variable adverse event rates. EUS-GBD leverages lumen-apposing metal stents (LAMS) for direct access to the gallbladder, providing the ability to treat an inflamed GB internally. Choledocholithiasis is primarily managed with ERCP, utilizing techniques to include balloon extraction, mechanical lithotripsy, or advanced methods such as electrohydraulic or laser lithotripsy in cases of complex stones. Altered anatomy from bariatric procedures like Roux-en-Y gastric bypass may necessitate specialized approaches, including balloon-assisted ERCP or EUS-directed transgastric ERCP (EDGE). Post-operative complications, including bile leaks and strictures, are managed endoscopically using sphincterotomy and stenting. Post-liver transplant anastomotic and non-anastomotic strictures often require repeated stent placements or advanced techniques like magnetic compression anastomosis in refractory cases. In chronic pancreatitis (CP), endoscopic approaches aim to relieve pain and address structural complications like pancreatic duct (PD) strictures and calculi. ERCP with sphincterotomy and stenting, along with extracorporeal shock wave lithotripsy (ESWL), achieves effective ductal clearance for PD stones. When traditional approaches are insufficient, direct visualization with peroral pancreatoscopy-assisted lithotripsy is utilized. EUS-guided interventions, such as cystgastrostomy, pancreaticogastrostomy, and celiac plexus blockade, offer alternative therapeutic options for pain management and drainage of peripancreatic fluid collections. EUS plays a diagnostic and therapeutic role in CP, with procedures tailored for high-risk patients or those with complex anatomy. As techniques evolve, endoscopic management provides minimally invasive alternatives for patients with complex benign pancreaticobiliary conditions, offering high clinical success and fewer complications. Full article

Recent advances in artificial intelligence (AI) research, particularly in image processing technologies, have shown promising applications across various domains, including health care. There is a significant effort to use AI for the early diagnosis and detection of diseases, offering cost-effective and timely solutions to enhance patient outcomes. This study introduces a deep learning network aimed at analyzing pathology images for the accurate diagnosis of pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC). Utilizing a novel dataset comprised of cases diagnosed with PDAC and/or chronic pancreatitis, this study applies deep learning algorithms to assess the effectiveness and reliability of the diagnostic process. The dataset was enhanced through image duplication and the creation of a second dataset with varied dimensions, facilitating the training of advanced transfer learning models including InceptionV3, DenseNet, ResNet, VGG, EfficientNet, and a specially designed deep neural network. The study presents a convolutional neural network model, optimized for the rapid and accurate detection of pancreatic cancer, and conducts a comparative analysis with other models to select the most accurate algorithm for a decision support system. The results from Dataset 1 show that EfficientNetB0 achieved a high success rate of 92%. In Dataset 2, VGG16 was found to have high performance, with a success rate of 92%. On the other hand, ResNet50 achieved a remarkable success rate of 96% despite a moderate training time and showed high precision, recall, F1 score, and accuracy. These results provide valuable data to demonstrate and share the relevance of different deep learning models in pancreatic cancer diagnosis. Full article

Recently, endoscopic ultrasound-guided tissue acquisition (EUS-TA) has been widely used to diagnose pancreatic ductal adenocarcinoma (PDAC). The histological examination of core tissues acquired using novel biopsy needles is the primary diagnostic approach for patients with PDAC. However, in patients with early-stage PDAC, such as Stages 0 and I, EUS-TA can be challenging, and its diagnostic accuracy may be limited. This presents a clinical dilemma: The earlier that clinicians attempt to accurately diagnose PDAC, the more difficult it becomes to do so using EUS-TA. Liquid-based cytology (LBC) is a technique for preparing pathological specimens from liquefied cytology specimens by placing the collected material in a special fixative preservative fluid. LBC offers advantages, such as specimen optimization with reduced blood interference, a high cell-collection rate, and the simplicity of the procedure in the endoscopy room. The use of LBC may improve diagnostic accuracy, particularly for early-stage PDAC. Therefore, we emphasize that cytology remains a valuable tool for the endoscopic diagnosis of PDAC. In this review, we discuss the role of LBC in the endoscopic diagnosis of PDAC. Full article

There is a rich body of literature on the distribution of cancer incidence and mortality in socioeconomically different world regions, but none of the studies has compared the spatial distribution of mortality and incidence to see if they are consistent with each other. All malignant neoplasms combined and cervical, colorectal, breast, pancreatic, lung, and oral cancers separately were studied in the Hungarian population aged 25–64 years for 2007–2018 at the municipality level by sex. In each case, the spatial distribution of incidence and mortality were compared with each other and with the level of deprivation using disease mapping, spatial regression, risk analysis, and spatial scan statistics. A positive association between deprivation and mortality was found for each type of cancer, but there was no significant association for male colorectal cancer (relative risk (RR) 1.00; 95% credible interval (CI) 0.99–1.02), pancreatic cancer (RR: 1.01; 95%CI 0.98–1.04), and female colorectal cancer incidence (RR: 1.01; 95%CI 0.99–1.03), whereas a negative association for breast cancer (RR: 0.98; 95%CI 0.96–0.99) was found. Disease mapping analyses showed only partial overlap between areas of high incidence and mortality, often independent of deprivation. Our results highlight not only the diverse relationship between cancer burden and deprivation, but also the inconsistent relationship between cancer incidence and mortality, pointing to areas with populations that require special public health attention. Full article

This paper introduces a novel one-dimensional convolutional neural network that utilizes clinical data to accurately detect choledocholithiasis, where gallstones obstruct the common bile duct. Swift and precise detection of this condition is critical to preventing severe complications, such as biliary colic, jaundice, and pancreatitis. This cutting-edge model was rigorously compared with other machine learning methods commonly used in similar problems, such as logistic regression, linear discriminant analysis, and a state-of-the-art random forest, using a dataset derived from endoscopic retrograde cholangiopancreatography scans performed at Olive View–University of California, Los Angeles Medical Center. The one-dimensional convolutional neural network model demonstrated exceptional performance, achieving 90.77% accuracy and 92.86% specificity, with an area under the curve of 0.9270. While the paper acknowledges potential areas for improvement, it emphasizes the effectiveness of the one-dimensional convolutional neural network architecture. The results suggest that this one-dimensional convolutional neural network approach could serve as a plausible alternative to endoscopic retrograde cholangiopancreatography, considering its disadvantages, such as the need for specialized equipment and skilled personnel and the risk of postoperative complications. The potential of the one-dimensional convolutional neural network model to significantly advance the clinical diagnosis of this gallstone-related condition is notable, offering a less invasive, potentially safer, and more accessible alternative. Full article

Background: Preoperative malnutrition is a significant factor in patients with pancreatic tumors undergoing pancreaticoduodenectomy. The aim of this study was to assess the association between preoperative malnutrition and delayed discharge within a ten-day timeframe and potential correlations between preoperative malnutrition and postoperative surgical complications. Methods: A retrospective cohort study was conducted, recruiting a final sample of 79 patients with benign or malignant cephalic pancreatic tumors from 2015 to 2022. The risk of malnutrition was assessed using the Malnutrition Universal Screening Tool, while length of hospital stay and relevant clinical data were extracted from clinical documentation. Results: The preoperative malnutrition risk was high in 21.52% of the sample, moderate in 36.71%, and low in 41.77%. Body mass index (BMI) (p = 0.007) and postoperative surgical complications (p < 0.001) were significantly correlated with delayed discharge. No statistically significant differences were found between levels of malnutrition risk and delayed discharge (p = 0.122), or postoperative surgical complications (p = 0.874). Conclusions: Postoperative complications and BMI emerge as significant risk factors. The limited sample size may have compromised the collection of homogeneous and significant data. Future studies should evaluate the implementation of personalized nutritional screening tools, nutritional assessment plans, and the involvement of specialized health professionals. Full article

A detailed study of palmitate metabolism in pancreatic islets subject to different experimental conditions, like varying concentrations of glucose, as well as fed or starved conditions, has allowed us to explore the interaction between the two main plasma nutrients and its consequences on hormone secretion. Palmitate potentiates glucose-induced insulin secretion in a concentration-dependent manner, in a physiological range of both palmitate (0–2 mM) and glucose (6–20 mM) concentrations; at glucose concentrations lower than 6 mM, no metabolic interaction with palmitate was apparent. Starvation (48 h) increased islet palmitate oxidation two-fold, and the effect was resistant to its inhibition by glucose (6–20 mM). Consequently, labelled palmitate and glucose incorporation into complex lipids were strongly suppressed, as well as glucose-induced insulin secretion and its potentiation by palmitate. 2-bromostearate, a palmitate oxidation inhibitor, fully recovered the synthesis of complex lipids and insulin secretion. We concluded that palmitate potentiation of the insulin response to glucose is not attributable to its catabolic mitochondrial oxidation but to its anabolism to complex lipids: islet lipid biosynthesis is dependent on the uptake of plasma fatty acids and the supply of α-glycerol phosphate from glycolysis. Islet secretion of glucagon and somatostatin showed a similar dependence on palmitate anabolism as insulin. The possible mechanisms implicated in the metabolic coupling between glucose and palmitate were commented on. Moreover, possible mechanisms responsible for islet gluco- or lipotoxicity after a long-term stimulation of insulin secretion were also discussed. Our own data on the simultaneous stimulation of insulin, glucagon, and somatostatin by glucose, as well as their modification by 2-bromostearate in perifused rat islets, give support to the conclusion that increased FFA anabolism, rather than its mitochondrial oxidation, results in a potentiation of their stimulated release. Starvation, besides suppressing glucose stimulation of insulin secretion, also blocks the inhibitory effect of glucose on glucagon secretion: this suggests that glucagon inhibition might be an indirect or direct effect of insulin, but not of glucose. In summary, there seems to exist three mechanisms of glucagon secretion stimulation: 1. glucagon stimulation through the same secretion coupling mechanism as insulin, but in a different range of glucose concentrations (0 to 5 mM). 2. Direct or indirect inhibition by secreted insulin in response to glucose (5–20 mM). 3. Stimulation by increased FFA anabolism in glucose intolerance or diabetes in the context of hyperlipidemia, hyperglycemia, and hypo-insulinemia. These conclusions were discussed and compared with previous published data in the literature. Specially, we discussed the mechanism for inhibition of glucagon release by glucose, which was apparently contradictory with the secretion coupling mechanism of its stimulation. Full article

Background and Objectives: The pancreas, ensconced within the abdominal cavity, requires a plethora of sophisticated imaging modalities for its comprehensive evaluation, with ultrasonography serving as a primary investigative technique. A myriad of pancreatic pathologies, encompassing pancreatic neoplasia and a spectrum of inflammatory diseases, are detectable through these imaging strategies. Nevertheless, the intricate anatomical confluence and the pancreas’s deep-seated topography render the visualization and accurate diagnosis of its pathologies a formidable endeavor. The objective of our paper is to review the best diagnostic imagistic tools for the pancreas. Materials and Methods: we have gathered several articles using Prisma guidelines to determine the best imagistic methods. The imperative of pancreatic scanning transcends its diagnostic utility, proving to be a pivotal element in a multitude of clinical specialties, notably surgical oncology. Within this domain, multidetector computed tomography (MDCT) of the pancreas holds the distinction of being the paramount imaging modality, endorsed for its unrivaled capacity to delineate the staging and progression of pancreatic carcinoma. In synergy with MDCT, there has been a notable advent of avant-garde imaging techniques in recent years. These advanced methodologies, including ultrasonography, endoscopic ultrasonography, contrast-enhanced ultrasonography, and magnetic resonance imaging (MRI) conjoined with magnetic resonance cholangiopancreatography (MRCP), have broadened the horizon of tumor characterization, offering unparalleled depth and precision in oncological assessment. Other emerging diagnostic techniques, such as elastography, also hold a lot of potential and promise for the future of pancreatic imaging. Fine needle aspiration (FNA) is a quick, minimally invasive procedure to evaluate lumps using a thin needle to extract tissue for analysis. It is less invasive than surgical biopsies and usually performed as an outpatient with quick recovery. Its accuracy depends on sample quality, and the risks include minimal bleeding or discomfort. Results, guiding further treatment, are typically available within a week. Elastography is a non-invasive medical imaging technique that maps the elastic properties and stiffness of soft tissue. This method, often used in conjunction with ultrasound or MRI, helps differentiate between hard and soft areas in tissue, providing valuable diagnostic information. It is particularly useful for assessing liver fibrosis, thyroid nodules, breast lumps, and musculoskeletal conditions. The technique is painless and involves applying gentle pressure to the area being examined. The resulting images show tissue stiffness, indicating potential abnormalities. Elastography is advantageous for its ability to detect diseases in early stages and monitor treatment effectiveness. The procedure is quick, safe, and requires no special preparation, with results typically available immediately. Results: The assembled and gathered data shows the efficacy of various techniques in discerning the nature and extent of neoplastic lesions within the pancreas. Conclusions: The most common imaging modalities currently used in diagnosing pancreatic neoplasms are multidetector computed tomography (MDCT), endoscopic ultrasound (EUS), and magnetic resonance imaging (MRI), alongside new technologies, such as elastography. Full article