Chronic pancreatitis is a progressive fibroinflammatory disease primarily caused by a complex interplay of environmental and genetic risk factors. It might result in pancreatic exocrine and endocrine insufficiency, chronic pain, reduced quality of life, and increased mortality. The diagnosis is based on the presence of typical symptoms and multiple morphological manifestations of the pancreas, including pancreatic duct stones and strictures, parenchymal calcifications, and pseudocysts. Management of chronic pancreatitis consists of prevention and treatment of complications, requiring a multidisciplinary approach focusing on lifestyle modifications, exocrine insufficiency, nutritional status, bone health, endocrine insufficiency, pain management, and psychological care. To optimise clinical outcomes, screening for complications and evaluation of treatment efficacy are indicated in all patients with chronic pancreatitis.

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

Early detection is crucial for improving survival rates of pancreatic ductal adenocarcinoma (PDA), yet current diagnostic methods can often fail at this stage. Recently, there has been significant interest in improving risk stratification and developing imaging biomarkers, through novel imaging techniques, and most notably, artificial intelligence (AI) technology. This review provides an overview of these advancements, with a focus on deep learning methods for early detection of PDA.

In previous studies, methylated DNA markers (MDMs) have been identified in pancreatic juice (PJ) for detecting pancreatic ductal adenocarcinoma (PDAC). In this prospective multicenter study, the sensitivity and specificity characteristics of this panel of PJ-MDMs was evaluated standalone and in combination with plasma carbohydrate antigen 19-9 (CA 19-9).

Paired PJ and plasma were assayed from 88 biopsy-proven treatment-naïve PDAC cases and 134 controls (53 with normal pancreas, 23 with chronic pancreatitis [CP], 58 with intraductal papillary mucinous neoplasm). Bisulfite-converted DNA from buffered PJ was analyzed using long-probe quantitative amplified signal assay targeting 14 MDMs (NDRG4, BMP3, TBX15, C13orf18, PRKCB, CLEC11A, CD1D, ELMO1, IGF2BP1, RYR2, ADCY1, FER1L4, EMX1, and LRRC4) and a reference gene (methylated B3GALT6). Logistic regression was used to fit the previously identified 3-MDM PJ panel (FER1L4, C13orf18, and BMP3). Discrimination accuracy was summarized using area under the receiver-operating characteristic curve (AUROC) with corresponding 95% confidence interval (CI).

Methylated FER1L4 had the highest individual AUROC of 0.83 (95% CI, 0.78-0.89). The AUROC for the 3-MDM PJ + plasma CA 19-9 model (0.95; 95% CI, 0.92-0.98) was higher than both the 3-MDM PJ panel (0.87; 95% CI, 0.82-0.92)) and plasma CA 19-9 alone (0.91; 95% CI, 0.87-0.96) (P = .0002 and .0135, respectively). At a specificity of 88% (95% CI, 81%-93%), the sensitivity of this model was 89% (95% CI, 80%-94%) for all PDAC stages and 83% (95% CI, 64%-94%) for stage I/II PDAC.

A panel combining PJ-MDMs and plasma CA 19-9 discriminates PDAC from both healthy and disease control groups with high accuracy. This provides support for combining PJ and blood-based biomarkers for enhancing diagnostic sensitivity and successful early PDAC detection.

Personalized and accurate survival risk prognostication remains a significant challenge in advanced pancreatic ductal adenocarcinoma (PDAC), despite extensive research on prognostic and predictive markers. Patients with PDAC are prone to muscle loss, fat consumption, and malnutrition, which is associated with inferior outcomes. This study investigated the use of three-dimensional (3D) anthropometric parameters derived from computed tomography (CT) scans and triglyceride glucose-body mass index (TyG-BMI) in relation to overall survival (OS) outcomes in advanced PDAC patients. Additionally, a predictive model for 1 year OS was developed based on body components and hematological indicators.

A retrospective analysis was conducted on 303 patients with locally advanced PDAC or synchronous metastases undergoing first-line chemotherapy, all of whom had undergone pretreatment abdomen-pelvis CT scans. Automatic 3D measurements of subcutaneous and visceral fat volume, skeletal muscle volume, and skeletal muscle density (SMD) were assessed at the L3 vertebral level by an artificial intelligence assisted diagnosis system (HY Medical). Various indicators including TyG-BMI, nutritional indicators [geriatric nutritional risk index (GNRI) and prealbumin], and inflammation indicators [(C-reactive protein (CRP) and neutrophil to lymphocyte ratio (NLR)] were also recorded. All patients underwent follow-up for at least 1 year and a dynamic nomogram for personalized survival prediction was constructed.

We included 211 advanced PDAC patients [mean (standard deviation) age, 63.4 ± 11.2 years; 89 women (42.2) %)]. Factors such as low skeletal muscle index (SMI) (P = 0.011), high visceral to subcutaneous adipose tissue area ratio (VSR) (P < 0.001), high visceral fat index (VFI) (P < 0.001), low TyG-BMI (P = 0.004), and low prealbumin (P = 0.001) were identified as independent risk factors associated with 1 year OS. The area under the curve of the established dynamic nomogram was 0.846 and the calibration curve showed good consistency. High-risk patients (> 211.9 points calculated using the nomogram) had significantly reduced survival rates.

In this study, the proposed nomogram model (with web-based tool) enabled individualized prognostication of OS and could help to guide risk-adapted nutritional treatment for patients with unresectable PDAC or synchronous metastases.

In Japan, 5 years have passed since the initiation of precision cancer medicine, and recent data accumulation in familial pancreatic cancer (FPC) and hereditary pancreatic cancer is outstanding. Multigene germline panel tests (MGPTs) have revealed that 7%-18% of patients with pancreatic cancer (PC) harbor pathogenic germline variants (PGVs), almost equal to the levels of breast, ovarian, endometrial, and colorectal cancers, with a higher incidence in FPC (14%-26%). The majority of PGVs seen in PC patients are clinically actionable and associated with homologous recombination (HR) pathways (6%-10%, particularly BRCA1/2 in 5%-6%), and the clinical guidelines recommend or propose genetic testing for all PC patients. Consensus guidelines have been established for most of the hereditary syndromes associated with PC risks, and surveillances of the pancreas and other at-risk organs are recommended for PGV carriers. Hereditary breast and ovarian cancer (HBOC) is the commonest hereditary cancer syndrome that has moderately increasing life-time risks of PC (3%-7% in Western countries); however, recent Japanese research demonstrated a higher risk level (BRCA1: 16%, BRCA2: 14%). Moreover, recent evidence has suggested a risk linkage between PC and ovarian cancer in HBOC pedigrees. High scores of homologous recombination deficiency suggest biallelic dysfunction of BRCA or other HR-related genes, and the likely effectiveness of platinum agents and PARP inhibitors against PCs. Remote counseling and testing are possible option in the future genetic medicine. As PC ranks in the second commonest target of precision cancer medicine in Japan, we must treat the patients and manage their at-risk relatives efficiently.

This review provides a comprehensive update on the diagnostic approaches to chronic pancreatitis (CP), emphasizing recent advancements in imaging techniques, biomarker research, and multivariable scoring systems. Despite substantial progress in these areas, current diagnostic algorithms have limitations, particularly for early and non-calcific CP. Traditional criteria have focused on classic diagnostic signs, but "minimal change" CP is increasingly recognized through advanced imaging and function tests. This article aims to guide clinicians in applying current methods and available strategies for CP diagnosis and outline research efforts in the field.

Pancreatic cancer is a serious pathology whose incidence is increasing. Advances in imaging have made it possible to fortuitously highlight more and more cystic pancreatic lesions, some of which have a malignant potential, leading to the consideration of prophylactic excision. The various known precancerous lesions of the pancreas as well as current management recommendations are here reviewed.

Pancreatic cysts are common incidental findings. The understanding of pancreatic cysts has evolved tremendously over the past few decades. Molecular diagnostic and endoscopic techniques have led to more precise characterization of cyst types and interventions to improve patient outcomes. This article outlines these recent innovations in pancreatic cyst diagnosis and management.

Pancreatic cancer, often diagnosed at advanced stages, has poor survival rates. Effective screening aims to detect the disease early, improving outcomes. Current guidelines recommend screening high-risk groups, including those with a family history or genetic predispositions, using methods like endoscopic ultrasound and MRI. The American Gastroenterological Association and other organizations advise annual surveillance for high-risk individuals, typically starting at the age of 50 or 10 years younger than the youngest affected relative. For certain genetic syndromes, such as Peutz-Jeghers syndrome or hereditary pancreatitis, screening may begin as early as the age of 35 to 40 years.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal malignancy with increasing incidence and low survival rate, primarily due to the late detection of the disease. Radiomics has demonstrated its utility in recognizing patterns and anomalies not perceptible to the human eye. This systematic literature review aims to assess the application of radiomics in the analysis of pancreatic parenchyma images to identify early indicators predictive of PDAC.

A systematic search of original research papers was performed on three databases: PubMed, Embase, and Scopus. Two reviewers applied the inclusion and exclusion criteria, and one expert solved conflicts for selecting the articles. After extraction and analysis of the data, there was a quality assessment of these articles using the Methodological Radiomics Score (METRICS) tool. The METRICS assessment was carried out by two raters, and conflicts were solved by a third reviewer.

Ten articles for analysis were retrieved. CT scan was the diagnostic imaging used in all the articles. All the studies were retrospective and published between 2019 and 2024. The main objective of the articles was to generate radiomics-based machine learning models able to differentiate pancreatic tumors from healthy tissue. The reported diagnostic performance of the model chosen yielded very high results, with a diagnostic accuracy between 86.5% and 99.2%. Texture and shape features were the most frequently implemented. The METRICS scoring assessment demonstrated that three articles obtained a moderate quality, five a good quality, and, finally, two articles yielded excellent quality. The lack of external validation and available model, code, and data were the major limitations according to the qualitative assessment.

There is high heterogeneity in the research question regarding radiomics and pancreatic cancer. The principal limitations of the studies were mainly due to the nature of the trials and the considerable heterogeneity of the radiomic features reported. Nonetheless, the work in this field is promising, and further studies are still required to adopt radiomics in the early detection of PDAC.

To explore the risk factors of post pancreatectomy diabetes mellitus (PPTDM)in pancreatic ductal carcinoma (PDAC) patients and the value of perioperative fasting blood glucose (FBG) level expression on the long-term survival after surgery.

Between December 2015 and December 2019, a cohort of 509 patients diagnosed with PDAC and undergoing resection at our hospital was analyzed. They were stratified into two groups, Control group (Control) and study group (PPTDM), depending on the onset of postoperative diabetes mellitus. We analyzed the survival rates at 6 months, 12 months and 24 months post-operation in the two groups. We use univariate and logostic multivariate regressions to analyze the risk factors for PPTDM. ROC curve analysis was conducted to assess the diagnostic significance of perioperative FBG levels regarding patients' long-term survival rates. The Kaplan-Meier method was employed to assess the impact of both preoperative and postoperative FBG levels on the survival rates within 24 months for each patient group.

The comparison of general clinical data between the two groups shows marginal differences without statistical significance(P > 0.05); Patients in PPTDM group had significantly higher BMI, preoperative jaundice proportion, larger tumor diameter, higher TNM stage and higher proportion of distal pancreatectomy (DP), with P values of 0.023, 0.010, 0.040, 0.012 and 0.005, respectively. The levels of preoperative FBG and postoperative FBG in PPTDM patients exhibited statistically significant elevation compared to the control group (P < 0.05). There were no significant differences in surgery-related indicators between the two groups in operative time, number of dissected positive lymph nodes, total number of dissected lymph nodes, intraoperative blood loss and other related data (P > 0.05). Hospitalization duration of PPTDM patients was longer than control group (P = 0.047). PPTDM group had significantly higher expression concentrations of BUN, Cr, TG, LDL and Apo-B factors (P = 0.023, 0.024, 0.013, 0.045 and 0.017). 17 patients (5.03%) died in the PPTDM group and 4 patients (2.35%) in control group which had significantly difference (P = 0.020). In univariate and logostic multivariate regression analysis indicated tumor size, jaundice, BUN, Cr, TG, LDL, Apo-B concentrations and DP approach were significantly correlated to the risk for PPTDM (P < 0.05). ROC curve analysis results showed combining of preoperative and postoperation FBG showed the highest diagnostic efficacy, followed by postoperation FBG and preoperative FBG. The AUC areas of the three groups were 0.745, 0.623 and 0.588, respectively, and the critical values of the three groups were 9.81/9.95 mmol/L, 10.18 mmol/L and 10.23 mmol/L, respectively, with statistical significance (P < 0.05). Results were considered statistically significant if the p-value was less than 0.05.

PPTDM stands as a significant postoperative complication following pancreatic cancer surgery, characterized by a high incidence and severity. Several risk factors have garnered considerable attention among clinical surgeon. PPTDM may be an influential factor in postoperative prognosis of pancreatic cancer. The expression levels of preoperative and postoperative blood glucose hold diagnostic value for the long-term prognosis of pancreatic cancer patients. Early regulation and intervention by surgeons concerning perioperative FBG could potentially mitigate the risk of PPTDM.

Interpretation of variants identified during genetic testing is a significant clinical challenge. In this study, we developed a high-throughput CDKN2A functional assay and characterized all possible CDKN2A missense variants. We found that 17.7% of all missense variants were functionally deleterious. We also used our functional classifications to assess the performance of in silico models that predict the effect of variants, including recently reported models based on machine learning. Notably, we found that all in silico models performed similarly when compared to our functional classifications with accuracies of 39.5-85.4%. Furthermore, while we found that functionally deleterious variants were enriched within ankyrin repeats, we did not identify any residues where all missense variants were functionally deleterious. Our functional classifications are a resource to aid the interpretation of CDKN2A variants and have important implications for the application of variant interpretation guidelines, particularly the use of in silico models for clinical variant interpretation.

Currently, those recommended to undergo pancreatic cancer (PC) surveillance include appropriately aged individuals at high risk of PC due to an identifiable genetic susceptibility or those without identifiable genetic susceptibility who nonetheless have a strong family history of PC. With increases in identification of individuals at high risk for PC and increased use of PC surveillance in clinical practice, there has been increasing debate about who should undergo surveillance as well as how surveillance should be performed including use of imaging and blood-based testing. Furthermore, there is increasing interest in the outcomes of PC surveillance in high-risk individuals with some studies demonstrating that surveillance leads to downstaging of PC and improvements in survival. In this review, we summarize the current state of PC surveillance in high-risk individuals, providing an overview of the risk factors associated with PC, selection of high-risk individuals for PC surveillance, and the current, but non-uniform, recommendations for performing PC surveillance. Additionally, we review approaches to apply various imaging and blood-based tests to surveillance and the outcomes of PC surveillance.

This review provides a comprehensive overview of the current landscape in pancreatic cancer (PC) screening, diagnosis, and early detection. This emphasizes the need for targeted screening in high-risk groups, particularly those with familial predispositions and genetic mutations, such as BRCA1, BRCA2, and PALB2. This review highlights the sporadic nature of most PC cases and significant risk factors, including smoking, alcohol consumption, obesity, and diabetes. Advanced imaging techniques, such as Endoscopic Ultrasound (EUS) and Contrast-Enhanced Harmonic Imaging (CEH-EUS), have been discussed for their superior sensitivity in early detection. This review also explores the potential of novel biomarkers, including those found in body fluids, such as serum, plasma, urine, and bile, as well as the emerging role of liquid biopsy technologies in analyzing circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomes. AI-driven approaches, such as those employed in Project Felix and CancerSEEK, have been highlighted for their potential to enhance early detection through deep learning and biomarker discovery. This review underscores the importance of universal genetic testing and the integration of AI with traditional diagnostic methods to improve outcomes in high-risk individuals. Additionally, this review points to future directions in PC diagnostics, including next-generation imaging, molecular biomarkers, and personalized medicine, aiming to overcome current diagnostic challenges and improve survival rates. Ultimately, the review advocates the adoption of informatics and AI-driven strategies to enhance early detection, reduce morbidity, and save lives in the fight against pancreatic cancer.

Routine screening to detect silent but deadly cancers such as pancreatic ductal adenocarcinoma (PDAC) can significantly improve survival, creating an important need for a convenient screening test. High-resolution proton (1H) magnetic resonance spectroscopy (MRS) of plasma identifies circulating metabolites that can allow detection of cancers such as PDAC that have highly dysregulated metabolism.

We first acquired 1H MR spectra of human plasma samples classified as normal, benign pancreatic disease and malignant (PDAC). We next trained a system of artificial neural networks (ANNs) to process and discriminate these three classes using the full spectrum range and resolution of the acquired spectral data. We then identified and ranked spectral regions that played a salient role in the discrimination to provide interpretability of the results. We tested the accuracy of the ANN performance using blinded plasma samples.

We show that our ANN approach yields, in a cross validation-based training of 170 samples, a sensitivity and a specificity of 100% for malignant versus non-malignant (normal and disease combined) discrimination. The trained ANNs achieve a sensitivity and specificity of 87.5% and 93.1% respectively (AUC: ROC = 0.931, P-R = 0.854), with 45 blinded plasma samples. Further, we show that the salient spectral regions of the ANN discrimination correspond to metabolites of known importance for their role in cancers.

Our results demonstrate that the ANN approach presented here can identify PDAC from 1H MR plasma spectra to provide a convenient plasma-based assay for population-level screening of PDAC. The ANN approach can be suitably expanded to detect other cancers with metabolic dysregulation.

Cancers of the digestive system are major contributors to global cancer-associated morbidity and mortality, accounting for 35% of annual cases of cancer deaths. The etiologies, molecular features, and therapeutic management of these cancer entities are highly heterogeneous and complex. Over the last decade, genomic and functional studies have provided unprecedented insights into the biology of digestive cancers, identifying genetic drivers of tumor progression and key interaction points of tumor cells with the immune system. This knowledge is continuously translated into novel treatment concepts and targets, which are dynamically reshaping the therapeutic landscape of these tumors. In this review, we provide a concise overview of the etiology and molecular pathology of the six most common cancers of the digestive system, including esophageal, gastric, biliary tract, pancreatic, hepatocellular, and colorectal cancers. We comprehensively describe the current stage-dependent pharmacological management of these malignancies, including chemo-, targeted, and immunotherapy. For each cancer entity, we provide an overview of recent therapeutic advancements and research progress. Finally, we describe how novel insights into tumor heterogeneity and immune evasion deepen our understanding of therapy resistance and provide an outlook on innovative therapeutic strategies that will shape the future management of digestive cancers, including CAR-T cell therapy, novel antibody-drug conjugates and targeted therapies.

Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer-related death by 2030. Early identification is rare, with a 5-year overall survival (OS) of less than 10%. Advances in the understanding of PDAC tumor biology are needed to improve these outcomes. Circulating tumor DNA (ctDNA) represents a promising novel biomarker in the identification and management of PDAC. Drawn from peripheral blood and analyzed using a variety of techniques, the detection of ctDNA in PDAC has been associated with shorter OS, minimal residual disease presence, and shorter recurrence-free survival. The use of ctDNA has also been examined as an indicator of therapeutic resistance, susceptibility to targeted therapy, and therapeutic response. While promising, ctDNA analysis is limited by its low rates of detection in some settings and lack of predictive ability in others. Many studies examining the utility of ctDNA for the management of PDAC have been relatively small retrospective cohort studies. The current findings will need to be validated by incorporation of ctDNA analysis into cancer registries and larger prospective studies. Given the current, rapid evolution in the field, it is possible that with time, ctDNA will be more routinely incorporated into the clinical management of PDAC.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a five-year survival rate of approximately 13% for advanced stages. While the majority of PDAC cases are sporadic, a significant subset is attributable to hereditary and familial predispositions, accounting for approximately 25% of cases. This article synthesizes recent advancements in the understanding, detection, and management of hereditary pancreatic cancer (PC). Results: Our review highlights the critical role of genetic testing (GT) in identifying high-risk individuals (HRIs), with germline pathogenic variants (PVs) found in up to 20% of hereditary PDAC cases. Since the implementation of next-generation sequencing (NGS) panels in 2014, detection capabilities have been significantly enhanced. HRIs can be included in screening programs that facilitate the early detection of PDAC. Early detection strategies, including the use of microribonucleic acid (miRNAs) signatures and novel imaging techniques like hyperpolarized 13C-magnetic resonance spectroscopy (MRS) have shown promising results. The identification of germline pathogenic variants (PVs) or mutations in homologous recombination (HR) genes plays a predictive role in the response to various treatments, prolonging patient survival. Discussion: Universal germline testing for PDAC, as recommended by the National Comprehensive Cancer Network (NCCN), is now a standard practice, facilitating the identification of at-risk individuals and enabling targeted surveillance and intervention. Multidisciplinary management, integrating genetic counseling, imaging, and gastrointestinal services, is essential for optimizing outcomes. Conclusions: Advances in genetic testing and biomarker research are transforming the landscape of hereditary PC management. Early detection and personalized treatment strategies are pivotal in improving survival rates. Ongoing multi-institutional research efforts are crucial for validating biomarkers and developing preventive measures, ultimately aiming to reduce the burden of this aggressive cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease. Approximately 10% of affected individuals have an inherited component. Deleterious germline variants increase the lifetime risk for PDAC and are often associated with an elevated risk for extra-pancreatic malignancies. In this study, we aimed to determine the prevalence and impact of germline pathogenic variants (gPVs) in patients with PDAC and extra-pancreatic malignancies. Using tissue samples and longitudinal data from a nationwide pathology database, we identified patients with PDAC and a set of 7 extra-pancreatic malignancies to investigate the presence of gPVs in 25 cancer susceptibility genes with targeted next-generation sequencing. Of 473 patients with PDAC and at least 1 extra-pancreatic malignancy, 75 (16%) had gPVs. These were predominantly in ATM (n = 22), CDKN2A (n = 14), BRCA2 (n = 10), or CHEK2 (n = 10) genes. The combination of PDAC and ovarian carcinoma carried the highest prevalence of gPVs (4 of 10; 40%), followed by PDAC and melanoma (15 of 53; 28%), and PDAC and gastric cancer (2 of 9; 22%). Patients with PDAC and certain extra-pancreatic malignancies carry a higher burden of gPVs than unselected PDAC cohorts. This is a group that very likely benefits from genetic testing because germline status can have important diagnostic and therapeutic implications for affected individuals and their family members.

Hereditary cancer syndromes cause a high lifetime risk of early, aggressive cancers. Early recognition of individuals at risk can allow risk-reducing interventions that improve morbidity and mortality. Family health history applications that gather data directly from patients could alleviate barriers to risk assessment in the clinical appointment, such as lack of provider knowledge of genetics guidelines and limited time in the clinical appointment. New approaches allow linking these applications to patient health portals and their electronic health records (EHRs), offering an end-to-end solution for patient-input family history information and risk result clinical decision support for their provider.

We describe the design of the first large-scale evaluation of an EHR-integrable, patient-facing family history software platform based on the Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) standard. In our study, we leverage an established implementation science framework to evaluate the success of our model to facilitate scalable, systematic risk assessment for hereditary cancers in diverse clinical environments in a large pragmatic study at two sites. We will also evaluate the success of the approach to improve the efficiency of downstream genetic counseling resulting from pre-counseling pedigree generation.

Our research study will provide evidence regarding a new care delivery model that is scalable and sustainable for a variety of medical centers and clinics.

This study was registered on ClinicalTrials.gov under NCT05079334 on 15 October 2021.

ATM germline pathogenic variants (GPVs) are associated with a moderately increased risk of female breast cancer, pancreatic cancer, and prostate cancer. Resources for managing ATM heterozygotes in clinical practice are limited.

An international workgroup developed a clinical practice resource to guide management of ATM heterozygotes using peer-reviewed publications and expert opinion.

Although ATM is a moderate (intermediate) penetrance gene, cancer risks may be considered as a continuous variable, influenced by family history and other modifiers. ATM GPV heterozygotes should generally be offered enhanced breast surveillance according to their personalized risk estimate and country-specific guidelines and, generally, risk-reducing mastectomy is not recommended. Prostate cancer surveillance should be considered. Pancreatic cancer surveillance should be considered based on assessment of family history, ideally as part of a clinical trial, with existence of country-specific guidelines. For ATM GPV heterozygotes who develop cancer, radiation therapy decisions should not be influenced by the genetic result. Although poly-adenosine diphosphate ribose polymerase inhibitors are licensed for use in metastatic castration-resistant prostate cancer and ATM GPVs, the evidence-base is currently weak.

Systematic prospective data collection is needed to establish the spectrum of ATM-associated cancer and determine the outlines of surveillance, response to cancer treatment, and survival.

Pancreatic ductal adenocarcinoma (PDAC) is the most malignant tumor. Zinc finger and SCAN domain-containing protein 23 (ZSCAN23) is a new member of the SCAN domain family. The expression regulation and biological function remain to be elucidated. In this study, we explored the epigenetic regulation and the function of ZSCAN23 in PDAC. ZSCAN23 was methylated in 60.21% (171/284) of PDAC and its expression was regulated by promoter region methylation. The expression of ZSCAN23 inhibited cell proliferation, colony formation, migration, invasion, and induced apoptosis and G1/S phase arrest. ZSCAN23 suppressed Panc10.05 cell xenograft growth in mice. Mechanistically, ZSCAN23 inhibited Wnt signaling by interacting with myosin heavy chain 9 (MYH9) in pancreatic cancer cells. ZSCAN23 is frequently methylated in PDAC and may serve as a detective marker. ZSCAN23 suppresses PDAC cell growth both in vitro and in vivo.

Pancreatic cancer is a highly aggressive malignancy with a profoundly poor prognosis. Clinically, the condition most frequently manifests with symptoms including painless jaundice, abdominal discomfort, and back pain. Early diagnosis and the implementation of effective therapeutic strategies are critical for improving patient survival outcomes. However, merely 10-20% of patients are diagnosed at an early stage, with the majority presenting at advanced stages, often with metastasis. Consequently, early detection and intervention are crucial for enhancing prognosis. The widespread adoption of endoscopic ultrasonography (EUS) technology in recent years has significantly enhanced the diagnostic accuracy for pancreatic space-occupying lesions. EUS is increasingly recognized for its pivotal role in alleviating malignant biliary obstruction (MBO), gastric outlet obstruction (GOO), and refractory pain in advanced pancreatic cancer. This article aims to provide an overall review of the current applications of EUS in the diagnosis and treatment of pancreatic cancer, exploring its advantages and limitations in early screening, diagnosis, and palliative care. Furthermore, this review explores potential future directions in the field, aiming to provide valuable insights to inform and enhance the clinical management of pancreatic cancer.

Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention.

Pancreatic cancer remains one of the most lethal forms of cancer. Currently, there is a lack of effective drug treatments for pancreatic cancer. However, as a newly discovered form of non-apoptotic cell death, ferroptosis has garnered increasing attention in relation to pancreatic cancer. Understanding the role of ferroptosis in the tumorigenesis and treatment of pancreatic cancer may enable more effective clinical trials and treatments for pancreatic cancer and may minimize side effects or restrict the emergence of drug resistance. In this review, we summarize the current knowledge regarding the process and underlying mechanisms of ferroptosis, as well as its dual role in both promoting tumorigenesis and facilitating treatment strategies for pancreatic cancer. Additionally, how ferroptosis is implicated in the development of pancreatitis and insulin resistance, indicating that ferroptosis may play an important role in the risk of pancreatitis- and insulin-resistance-related pancreatic cancers, is also addressed.

Pancreatic cancer serves as the third leading cause of cancer-associated morbidity and mortality in the United States, with a 5-year survival rate of only 12% with an expected increase in incidence and mortality in the coming years. Pancreatic ductal adenocarcinomas constitute most pancreatic malignancies. Certain genetic syndromes, including Lynch syndrome, hereditary breast and ovarian cancer syndrome, hereditary pancreatitis, familial adenomatous polyposis, Peutz-Jeghers syndrome, familial pancreatic cancer mutation, and ataxia telangiectasia, confer a significantly higher risk. Screening for pancreatic malignancies currently targets patients with germline mutations or those with significant family history. Screening the general population is not currently viable owing to overall low incidence and lack of specific tests. Endoscopic ultrasound (EUS) and its applied advances are increasingly being used for surveillance, diagnosis, and management of pancreatic malignancies and have now become an indispensable tool in their management. For patients with risk factors, EUS in combination with magnetic resonance imaging/magnetic resonance cholangiopancreatography is used for screening. The role of endoscopic modalities has been expanding with the increased utilization of endoscopic retrograde cholangiopancreatography, EUS-directed therapies include EUS-guided fine-needle aspiration and EUS-fine-needle biopsy (FNB). EUS combined with FNB has the highest specificity and sensitivity for detecting pancreatic cancer amongst available modalities. Studies also recognize that artificial intelligence assisted EUS in the early detection of pancreatic cancer. At the same time, surgical resection has been historically considered the only curative treatment for pancreatic cancer, over 80% of patients present with unresectable disease. We also discuss EUS-guided therapies of physicochemicals (radiofrequency ablation, brachytherapy, and intratumor chemotherapy), biological agents (gene therapies and oncolytic viruses), and immunotherapy. We aim to perform a detailed review of the current burden, risk factors, role of screening, diagnosis, and endoscopic advances in the treatment modalities available for pancreatic cancer.

Several studies suggest that new-onset diabetes mellitus is an early manifestation of pancreatic ductal adenocarcinoma (PDAC). Therefore, the International Cancer of the Pancreas Screening Consortium recommends glucose and hemoglobin A1c (HbA1c) monitoring in high-risk individuals (HRIs) undergoing surveillance. However, evidence that such monitoring improves PDAC detection is lacking. Our aim was to investigate the association between serum glucose and HbA1c values and the development of PDAC in HRIs undergoing surveillance.

Participants were recruited from the familial pancreatic cancer surveillance cohort, which follows hereditary predisposed HRIs yearly by magnetic resonance imaging and/or endoscopic ultrasound and blood sampling. Those who underwent fasting glucose and/or HbA1c monitoring at least once were eligible candidates.

Four hundred four HRIs met the inclusion criteria. During a median follow-up of 41 months (range 14-120), 9 individuals developed PDAC and 4 (without PDAC) were diagnosed with new-onset diabetes mellitus. Glucose levels ranged from 3.4 to 10.7 mmol/L (mean 5.6 ± 0.7) and HbA1c levels from 25 to 68 mmol/mol (mean 37.7 ± 4.1). The mean values did not differ significantly between PDAC cases and controls. The percentage of individuals with at least one elevated value were comparable between PDAC cases and controls for glucose (33% and 27%, P = 0.707) and HbA1c (22% and 14%, P = 0.623). No consistent glucose or HbA1c trends over time suggested a correlation with PDAC development.

In this HRI surveillance cohort, measuring glucose and HbA1c values did not contribute to PDAC detection. Larger and longer-term studies are needed to determine the final role of glucose and HbA1c monitoring in PDAC surveillance.

Gastrointestinal cancers pose a significant global health challenge. N-glycosylation modulates various cellular processes, including key cancer-related mechanisms. Elucidating its involvement in the onset and advancement of these cancers can offer critical insights for enhancing diagnostic and therapeutic approaches. This review outlines the core process of protein N-glycosylation and highlights its contribution to the progression of gastrointestinal cancers, encompassing cell proliferation, survival, invasion, metastasis, and immune evasion, mainly through its impact on critical signaling pathways. Notably, aberrant N-glycosylation patterns have emerged as crucial biomarkers for the diagnosis and prognosis of various gastrointestinal cancers, providing the foundation for more personalized therapeutic approaches. Therapeutic strategies targeting N-glycosylation, such as glycosyltransferase inhibitors and glycoengineering, show significant promise in mitigating tumor aggressiveness and enhancing immune recognition. However, the clinical implementation of N-glycosylation biomarkers requires the standardization of glycosylation analysis techniques and solutions to challenges in sample processing and data interpretation. Future research efforts should concentrate on overcoming these obstacles to unlock the full potential of N-glycosylation in enhancing cancer management and advancing patient outcomes.

Pancreatic cystic lesions (PCLs) are a heterogeneous group of lesions with increasing incidence, usually identified incidentally on imaging studies (multidetector computed tomography (MDCT), magnetic resonance imaging (MRI), or endoscopic ultrasound (EUS)) [...].

Pancreatic ductal adenocarcinoma (PDAC) is associated with a significant percentage of germline pathogenic variants (GPVs). Unlike in the United States, routine universal genetic testing is not performed in Europe. The aim of the study is to evaluate the diagnostic yield of germline genetic testing in all patients with PDAC.

Individuals with newly diagnosed PDAC from three Spanish hospitals were enrolled, regardless of family history. Thirteen known susceptibility genes for PDAC were studied using a multigene panel or whole-exome sequencing.

One hundred seventy-nine PDAC patients underwent genetic testing. Fourteen (7.8%) had a GPV or likely pathogenic variant In the genes studied: six in ATM, six in BRCA2, one in PALB2, and one in TP53. Of these, seven (50%) did not meet the clinical criteria for genetic study and would have been classified as sporadic PDAC. Presenting with a personal history of any other neoplasm was associated with some GPV, with an odds ratio (OR) of 3.5 (1.1-11.5). A family history of PDAC and breast cancer was also associated with some GPV, with oRs of 3.7 (1.08-13.6) and 8.5 (2.6-26.6), respectively. None of the patients over 60 years without a relevant family history of malignancies presented a GPV associated with PDAC.

In our PDAC cohort, a noteworthy number of GPVs were identified, and half of these patients would have been classified as sporadic based solely on clinical criteria. Genetic testing should always be considered, particularly in patients under 60 years or those with a history of other malignancies, especially where economic resources need optimization.

At its best, the practice of medicine involves careful integration of experience and evidence. Generating evidence to address controversies in radiology - and translating such evidence to practice - requires appropriate selection of methods, and an understanding of the strengths, shortcomings, and biases inherent to different research designs and analyses. Equipped with such knowledge, the radiologic community can ensure that both research and clinical practice in our discipline excels, and that those questions that will be the most critical to answer will be formulated for successful investigation in the years to come.

Pancreatic ductal adenocarcinoma (PDAC) poses significant challenges due to its high mortality, making it a critical area of research. This retrospective observational study aimed to analyze real-world data from comprehensive genome profiling (CGP) of Japanese patients with PDAC, mainly focusing on differences in gene detection rates among panels and the implications for homologous recombination deficiency (HRD) status. This study enrolled 2568 patients with PDAC who had undergone CGP between June 2019 and December 2021 using data from the nationwide Center for Cancer Genomics and Advanced Therapeutics database. Two types of CGP assays (tissue and liquid biopsies) were compared and a higher detection rate of genetic abnormalities in tissue specimens was revealed. HRD-related gene alterations were detected in 23% of patients, with BRCA1/2 mutations accounting for 0.9% and 2.9% of patients, respectively. Treatment outcome analysis indicated that patients with BRCA1/2 mutations had a longer time to treatment discontinuation with FOLFIRINOX than gemcitabine plus nab-paclitaxel as first-line therapy (9.3 vs. 5.6 months, p = 0.028). However, no significant differences were observed in the treatment response among the other HRD-related genes. Logistic regression analysis identified younger age and family history of breast, prostate, and ovarian cancers as predictive factors for HRD-related gene alterations. Despite the lack of progression-free survival data and the inability to discriminate between germline and somatic mutations, this study provides valuable insights into the clinical implications of CGP in Japanese patients with PDAC. Further research is warranted to optimize panel selection and elucidate the efficacy of platinum-based therapies depending on the HRD status.

A blood test that enables surveillance for early-stage pancreatic ductal adenocarcinoma (PDAC) is an urgent need. Independent laboratories have reported PDAC biomarkers that could improve biomarker performance over CA19-9 alone, but the performance of the previously reported biomarkers in combination is not known. Therefore, we conducted a coordinated case/control study across multiple laboratories using common sets of blinded training and validation samples (132 and 295 plasma samples, respectively) from PDAC patients and non-PDAC control subjects representing conditions under which surveillance occurs. We analyzed the training set to identify candidate biomarker combination panels using biomarkers across laboratories, and we applied the fixed panels to the validation set. The panels identified in the training set, CA19-9 with CA199.STRA, LRG1, TIMP-1, TGM2, THSP2, ANG, and MUC16.STRA, achieved consistent performance in the validation set. The panel of CA19-9 with the glycan biomarker CA199.STRA improved sensitivity from 0.44 with 0.98 specificity for CA19-9 alone to 0.71 with 0.98 specificity (p < 0.001, 1000-fold bootstrap). Similarly, CA19-9 combined with the protein biomarker LRG1 and CA199.STRA improved specificity from 0.16 with 0.94 sensitivity for CA19-9 to 0.65 with 0.89 sensitivity (p < 0.001, 1000-fold bootstrap). We further validated significantly improved performance using biomarker panels that did not include CA19-9. This study establishes the effectiveness of a coordinated study of previously discovered biomarkers and identified panels of those biomarkers that significantly increased the sensitivity and specificity of early-stage PDAC detection in a rigorous validation trial.