Early Detection Initiative: A randomized controlled trial of algorithm-based screening in patients with new onset hyperglycemia and diabetes for early detection of pancreatic ductal adenocarcinoma

From classical approaches to artificial intelligence, old and new tools for PDAC risk stratification and prediction

Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therapeutic options. Risk stratification has traditionally been performed using epidemiological studies and genetic analyses, through which key risk factors, including smoking, diabetes, chronic pancreatitis, and inherited predispositions, have been identified. However, the multifactorial nature of PDAC has often been insufficiently addressed by these methods, leading to limited precision in individualized risk assessments. Advances in artificial intelligence (AI) have been proposed as a transformative approach, allowing the integration of diverse datasets-spanning genetic, clinical, lifestyle, and imaging data into dynamic models capable of uncovering novel interactions and risk profiles. In this review, the evolution of PDAC risk stratification is explored, with classical epidemiological frameworks compared to AI-driven methodologies. Genetic insights, including genome-wide association studies and polygenic risk scores, are discussed, alongside AI models such as machine learning, radiomics, and deep learning. Strengths and limitations of these approaches are evaluated, with challenges in clinical translation, such as data scarcity, model interpretability, and external validation, addressed. Finally, future directions are proposed for combining classical and AI-driven methodologies to develop scalable, personalized predictive tools for PDAC, with the goal of improving early detection and patient outcomes.

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

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.

Advancements in early detection of pancreatic cancer: the role of artificial intelligence and novel imaging techniques

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.

Early detection of pancreatic cancer: Study design and analytical considerations in biomarker discovery and early phase validation studies

OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease that is challenging to detect at an early stage. Biomarkers are needed that can detect PDAC early in the course of disease when interventions lead to the best outcomes. We highlight study design and statistical considerations that inform pancreatic cancer early detection biomarker evaluation.

METHODS

We describe experimental design strategies in this setting useful for streamlining biomarker evaluation at each Early Detection Research Network (EDRN) phase of biomarker development. We break the early EDRN phases into sub-phases, proposing objectives, study design strategies, and biomarker performance benchmarks.

RESULTS

The goal of early detection in populations at high-risk of PDAC is described. Evaluating biomarker behavior in patients under surveillance without disease can winnow candidate biomarkers. Potential resources for biomarker validation studies are described.

CONCLUSIONS

Multisite and multidisciplinary collaboration can facilitate study design strategies in this lethal but low incidence disease and streamline the path from biomarker discovery to clinical use. Improvements in analytical and experimental design methods could help accelerate biomarker evaluation through the phases of biomarker development.

Strong association between reduction of late-stage cancers and reduction of cancer-specific mortality in meta-regression of randomized screening trials across multiple cancer types

BACKGROUND

Late-stage cancer incidence has been proposed as an early surrogate for mortality in randomized controlled trials (RCTs) of cancer screening; however, its validity has not been systematically evaluated across screening RCTs of different cancers.

METHODS

We conducted a meta-regression analysis of cancer screening RCTs that reported both late-stage cancer incidence and cancer mortality. Based on a systematic literature review, we included 33 RCTs of screening programs targeting seven cancer types, including lung (n = 12), colorectal (n = 8), breast (n = 5), and prostate (n = 4), among others. We regressed the relative reduction of cancer mortality on the relative reduction of late-stage cancer incidence, inversely weighted for each RCT by the variance of estimated mortality reduction.

RESULTS

Across cancer types, the relative reduction of late-stage cancer incidence was linearly associated with the relative reduction of cancer mortality. Specifically, we observed this association for lung (R2 = 0.79 and 0.996 in three recent large trials), breast (R2 = 0.94), prostate (R2 = 0.98), and colorectal cancer (R2 = 0.75 for stage III/IV cancers and 0.93 for stage IV cancers). Trials with a 20% or greater reduction in late-stage cancers were more likely to achieve a significant reduction in cancer mortality. Our results also showed that no reduction of late-stage cancer incidence was associated with no or minimal reduction in cancer mortality.

CONCLUSIONS

Meta-regression of historical screening RCTs showed a strong linear association between reductions in late-stage cancer incidence and cancer mortality.

National Liver Cancer Screening Trial (TRACER) study protocol

BACKGROUND

Professional guidelines recommend HCC screening in at-risk patients using semi-annual ultrasound with or without alpha-fetoprotein (AFP); however, this strategy has limited effectiveness due to low adherence and sensitivity. Increasing data support the potential role of blood-based biomarker panels, which could improve both aspects. The biomarker panel GALAD, comprised of sex, age, and 3 blood biomarkers (AFP, AFP-L3, and des-carboxy prothrombin des-carboxy prothrombin), has shown high sensitivity and specificity in biomarker phase II (case-control) and phase III (retrospective cohort) validation studies. However, prospective validation in a large phase IV biomarker clinical utility trial is necessary before its adoption in practice.

METHODS

The National Liver Cancer Screening Trial is an adaptive pragmatic randomized phase IV trial, which began enrollment in January 2024, comparing ultrasound-based versus biomarker-based screening in 5500 patients with chronic hepatitis B infection or cirrhosis from any etiology. Eligible patients are randomly assigned in a 1:1 ratio to semi-annual screening with ultrasound ± alpha-fetoprotein (arm A) or semi-annual screening with GALAD (arm B). Randomization is stratified by enrollment site, liver disease severity (per Child-Pugh class), liver disease etiology (viral, nonviral, and noncirrhotic HBV), and sex. Patients are being recruited from 15 sites (a mix of tertiary care academic referral centers, safety-net health systems, and large community health systems) over a 3-year period, and the primary endpoint, reduction in late-stage HCC, will be assessed at the end of year 5.5.

DISCUSSION

The results of this trial will inform the best strategy for HCC screening and early-stage detection in patients with chronic liver diseases. If GALAD shows superiority, HCC screening would primarily shift from an ultrasound-based strategy to the adoption of the biomarker panel.

TRIAL REGISTRATION

NCT06084234.

TRIAL STATUS

The TRACER Study is actively enrolling.

Designing Rigorous and Efficient Clinical Utility Studies for Early Detection Biomarkers

Before implementing a biomarker in routine clinical care, it must demonstrate clinical utility by leading to clinical actions that positively affect patient-relevant outcomes. Randomly controlled early detection utility trials, especially those targeting mortality endpoint, are challenging due to their high costs and prolonged duration. Special design considerations are required to determine the clinical utility of early detection assays. This commentary reports on discussions among the National Cancer Institute's Early Detection Research Network investigators, outlining the recommended process for carrying out single-organ biomarker-driven clinical utility studies. We present the early detection utility studies in the context of phased biomarker development. We describe aspects of the studies related to the features of biomarker tests, the clinical context of endpoints, the performance criteria for later phase evaluation, and study size. We discuss novel adaptive design approaches for improving the efficiency and practicality of clinical utility trials. We recommend using multiple strategies, including adopting real-world evidence, emulated trials, and mathematical modeling to circumvent the challenges in conducting early detection utility trials.

Clinical performance and utility: A microsimulation model to inform the design of screening trials for a multi-cancer early detection test

OBJECTIVES

Designing cancer screening trials for multi-cancer early detection (MCED) tests presents a significant methodology challenge, as natural histories of cell-free DNA-shedding cancers are not yet known. A microsimulation model was developed to project the performance and utility of an MCED test in cancer screening trials.

METHODS

Individual natural history of preclinical progression through cancer stages for 23 cancer classes was simulated by a stage-transition model under a broad range of cancer latency parameters. Cancer incidences and stage distributions at clinical presentation in simulated trials were set to match the data from Surveillance, Epidemiology, and End Results program. One or multiple rounds of annual screening using a targeted methylation-based MCED test (GalleriⓇ) was conducted to detect preclinical cancers. Mortality benefit of early detection was simulated by a stage-shift model.

RESULTS

In simulated trials, accounting for healthy volunteer effect and varying test sensitivity, positive predictive value in the prevalence screening round reached 48% to 61% in 6 natural history scenarios. After 3 rounds of annual screening, the cumulative proportions of stage I/II cancers increased by approximately 9% to 14%, the incidence of stage IV cancers was reduced by 37% to 46%, the reduction of stages III and IV cancer incidences was 9% to 24%, and the reduction of mortality reached 13% to 16%. Greater reductions of late-stage cancers and cancer mortality were achieved by five rounds of MCED screening.

CONCLUSIONS

Simulation results guide trial design and suggest that adding this MCED test to routine screening in the United States may shift cancer detection to earlier stages, and potentially save lives.

Imaging for Early Detection of Pancreatic Ductal Adenocarcinoma: Updates and Challenges in the Implementation of Screening and Surveillance Programs

Pancreatic ductal adenocarcinoma (PDA) is one of the most aggressive cancers. It has a poor 5-year survival rate of 12%, partly because most cases are diagnosed at advanced stages, precluding curative surgical resection. Early-stage PDA has significantly better prognoses due to increased potential for curative interventions, making early detection of PDA critically important to improved patient outcomes. We examine current and evolving early detection concepts, screening strategies, diagnostic yields among high-risk individuals, controversies, and limitations of standard-of-care imaging.

Designing cancer screening trials for reduction in late-stage cancer incidence

Before implementing a biomarker test for early cancer detection into routine clinical care, the test must demonstrate clinical utility, that is, the test results should lead to clinical actions that positively affect patient-relevant outcomes. Unlike therapeutical trials for patients diagnosed with cancer, designing a randomized controlled trial (RCT) to demonstrate the clinical utility of an early detection biomarker with mortality and related endpoints poses unique challenges. The hurdles stem from the prolonged natural progression of the disease and the lack of information regarding the time-varying screening effect on the target asymptomatic population. To facilitate the study design of screening trials, we propose using a generic multistate disease history model and derive model-based effect sizes. The model links key performance metrics of the test, such as sensitivity, to primary endpoints like the incidence of late-stage cancer. It also incorporates the practical implementation of the biomarker-testing program in real-world scenarios. Based on the chronological time scale aligned with RCT, our method allows the assessment of study powers based on key features of the new program, including the test sensitivity, the length of follow-up, and the number and frequency of repeated tests. The calculation tool from the proposed method will enable practitioners to perform realistic and quick evaluations when strategizing screening trials for specific diseases. We use numerical examples based on the National Lung Screening Trial to demonstrate the method.

Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods

PURPOSE

To evaluate robustness of a radiomics-based support vector machine (SVM) model for detection of visually occult PDA on pre-diagnostic CTs by simulating common variations in image acquisition and radiomics workflow using image perturbation methods.

METHODS

Eighteen algorithmically generated-perturbations, which simulated variations in image noise levels (σ, 2σ, 3σ, 5σ), image rotation [both CT image and the corresponding pancreas segmentation mask by 45° and 90° in axial plane], voxel resampling (isotropic and anisotropic), gray-level discretization [bin width (BW) 32 and 64)], and pancreas segmentation (sequential erosions by 3, 4, 6, and 8 pixels and dilations by 3, 4, and 6 pixels from the boundary), were introduced to the original (unperturbed) test subset (n = 128; 45 pre-diagnostic CTs, 83 control CTs with normal pancreas). Radiomic features were extracted from pancreas masks of these additional test subsets, and the model's performance was compared vis-a-vis the unperturbed test subset.

RESULTS

The model correctly classified 43 out of 45 pre-diagnostic CTs and 75 out of 83 control CTs in the unperturbed test subset, achieving 92.2% accuracy and 0.98 AUC. Model's performance was unaffected by a three-fold increase in noise level except for sensitivity declining to 80% at 3σ (p = 0.02). Performance remained comparable vis-a-vis the unperturbed test subset despite variations in image rotation (p = 0.99), voxel resampling (p = 0.25-0.31), change in gray-level BW to 32 (p = 0.31-0.99), and erosions/dilations up to 4 pixels from the pancreas boundary (p = 0.12-0.34).

CONCLUSION

The model's high performance for detection of visually occult PDA was robust within a broad range of clinically relevant variations in image acquisition and radiomics workflow.

Circulating tumor cells as prognostic marker in pancreatic cancer

In this editorial we comment on the article by Zhang et al published in the recent issue of the World Journal of Clinical Oncology. Pancreatic cancer is the fourth most common cause of cancer-related mortality and has the lowest survival rate among all solid cancers. It causes 227000 deaths annually worldwide, and the 5-year survival rate is very low due to early metastasis, which is 4.6%. Cancer survival increases with better knowledge of risk factors and early and accurate diagnosis. Circulating tumor cells (CTCs) are tumor cells that intravasate from the primary tumor or metastasis foci into the peripheral blood circulation system spontaneously or during surgical operations. Detection of CTC in blood is promising for early diagnosis. In addition, studies have associated high CTC levels with a more advanced stage, and more intensive treatments should be considered in cases with high CTC. In tumors that are considered radiologically resectable, it may be of critical importance in detecting occult metastases and preventing unnecessary surgeries.

Pancreatic Cancer Action Network's SPARK: A Cloud-Based Patient Health Data and Analytics Platform for Pancreatic Cancer

PURPOSE

Pancreatic cancer currently holds the position of third deadliest cancer in the United States and the 5-year survival rate is among the lowest for major cancers at just 12%. Thus, continued research efforts to better understand the clinical and molecular underpinnings of pancreatic cancer are critical to developing both early detection methodologies as well as improved therapeutic options. This study introduces Pancreatic Cancer Action Network's (PanCAN's) SPARK, a cloud-based data and analytics platform that integrates patient health data from the PanCAN's research initiatives and aims to accelerate pancreatic cancer research by making real-world patient health data and analysis tools easier to access and use.

MATERIALS AND METHODS

The SPARK platform integrates clinical, molecular, multiomic, imaging, and patient-reported data generated from PanCAN's research initiatives. The platform is built on a cloud-based infrastructure powered by Velsera. Cohort exploration and browser capabilities are built using Velsera ARIA, a specialized product for leveraging clinicogenomic data to build cohorts, query variant information, and drive downstream association analyses. Data science and analytic capabilities are also built into the platform allowing researchers to perform simple to complex analysis.

RESULTS

Version 1 of the SPARK platform was released to pilot users, who represented diverse end users, including molecular biologists, clinicians, and bioinformaticians. Included in the pilot release of SPARK are deidentified clinical (including treatment and outcomes data), molecular, multiomic, and whole-slide pathology images for over 600 patients enrolled in PanCAN's Know Your Tumor molecular profiling service.

CONCLUSION

The pilot release of the SPARK platform introduces qualified researchers to PanCAN real-world patient health data and analytical resources in a centralized location.

Advances in the management of pancreatic cancer

Pancreatic cancer remains among the malignancies with the worst outcomes. Survival has been improving, but at a slower rate than other cancers. Multimodal treatment, including chemotherapy, surgical resection, and radiotherapy, has been under investigation for many years. Because of the anatomical characteristics of the pancreas, more emphasis on treatment selection has been placed on local extension into major vessels. Recently, the development of more effective treatment regimens has opened up new treatment strategies, but urgent research questions have also become apparent. This review outlines the current management of pancreatic cancer, and the recent advances in its treatment. The review discusses future treatment pathways aimed at integrating novel findings of translational and clinical research.

Utilising Pancreatic Exocrine Insufficiency in the Detection of Resectable Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is usually diagnosed late, leading to a high mortality rate. Early detection facilitates better treatment options. The aim of this UK-based case-control study was to determine whether two validated tests for pancreatic exocrine insufficiency (PEI), namely, the 13C-mixed triglyceride breath test (13C-MTGBT) and a faecal elastase (FE-1) test, can discriminate between patients with resectable PDAC versus healthy volunteers (HVs) along with a comparison group with chronic pancreatitis (CP). Discrimination between disease states and HVs was tested with receiver operator characteristic (ROC) curves. In total, 59 participants (23 PDAC (16 men), 24 HVs (13 men) and 12 CP (10 men)) were recruited, with a similar age in each population, and a combined median (IQR) age of 66 (57-71). The areas under the ROC curve for discriminating between PDAC and HVs were 0.83 (95% CI: 0.70-0.96) for the 13C-MTGBT, and 0.85 (95% CI: 0.75-0.95) for the FE-1 test. These were similar to CP vs. HV. In conclusion, PEI occurs in resectable PDAC to a similar extent as in CP; further large-scale, prospective studies using these tests in the primary care setting on high-risk groups are warranted.

Accuracy of the END-PAC Model in Predicting the Risk of Developing Pancreatic Cancer in Patients with New-Onset Diabetes: A Systematic Review and Meta-Analysis

OBJECTIVES

To investigate the performance of the END-PAC model in predicting pancreatic cancer risk in individuals with new-onset diabetes (NOD).

METHODS

The PRISMA statement standards were followed to conduct a systematic review. All studies investigating the performance of the END-PAC model in predicting pancreatic cancer risk in individuals with NOD were included. Two-by-two tables, coupled forest plots and summary receiver operating characteristic plots were constructed using the number of true positives, false negatives, true negatives and false positives. Diagnostic random effects models were used to estimate summary sensitivity and specificity points.

RESULTS

A total of 26,752 individuals from four studies were included. The median follow-up was 3 years and the pooled risk of pancreatic cancer was 0.8% (95% CI 0.6-1.0%). END-PAC score ≥ 3, which classifies the patients as high risk, was associated with better predictive performance (sensitivity: 55.8% (43.9-67%); specificity: 82.0% (76.4-86.5%)) in comparison with END-PAC score 1-2 (sensitivity: 22.2% (16.6-29.2%); specificity: 69.9% (67.3-72.4%)) and END-PAC score < 1 (sensitivity: 18.0% (12.8-24.6%); specificity: 50.9% (48.6-53.2%)) which classify the patients as intermediate and low risk, respectively. The evidence quality was judged to be moderate to high.

CONCLUSIONS

END-PAC is a promising model for predicting pancreatic cancer risk in individuals with NOD. The score ≥3 should be considered as optimum cut-off value. More studies are needed to assess whether it could improve early pancreatic cancer detection rate, pancreatic cancer re-section rate, and pancreatic cancer treatment outcomes.

Where the metabolome meets the microbiome for pancreatic cancer detection

Risk prediction tools for pancreatic cancer are urgently sought to facilitate screening. Irajizad et al.1 describe the performance of a risk predication model based on circulating microbial- and non-microbial metabolites for assessment of 5-year pancreatic cancer risk.

A review study on early detection of pancreatic ductal adenocarcinoma using artificial intelligence assisted diagnostic methods

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, chemo-refractory and recalcitrant cancer and increases the number of deaths. With just around 1 in 4 individuals having respectable tumours, PDAC is frequently discovered when it is in an advanced stage. Accordingly, ED of PDAC improves patient survival. Subsequently, this paper reviews the early detection of PDAC, initially, the work presented an overview of PDAC. Subsequently, it reviews the molecular biology of pancreatic cancer and the development of molecular biomarkers are represented. This article illustrates the importance of identifying PDCA, the Immune Microenvironment of Pancreatic Cancer. Consequently, in this review, traditional and non-traditional imaging techniques are elucidated, traditional and non-traditional methods like endoscopic ultrasound, Multidetector CT, CT texture analysis, PET-CT, magnetic resonance imaging, diffusion-weighted imaging, secondary signs of pancreatic cancer, and molecular imaging. The use of artificial intelligence in pancreatic cancer, novel MRI techniques, and the future directions of AI for PDAC detection and prognosis is then described. Additionally, the research problem definition and motivation, current trends and developments, state of art of survey, and objective of the research are demonstrated in the review. Consequently, this review concluded that Artificial Intelligence Assisted Diagnostic Methods with MRI images can be proposed in future to improve the specificity and the sensitivity of the work, and to classify malignant PDAC with greater accuracy.

Current status of molecular diagnostic approaches using liquid biopsy

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal cancers, and developing an efficient and reliable approach for its early-stage diagnosis is urgently needed. Precancerous lesions of PDAC, such as pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMN), arise through multiple steps of driver gene alterations in KRAS, TP53, CDKN2A, SMAD4, or GNAS. Hallmark mutations play a role in tumor initiation and progression, and their detection in bodily fluids is crucial for diagnosis. Recently, liquid biopsy has gained attention as an approach to complement pathological diagnosis, and in addition to mutation signatures in cell-free DNA, cell-free RNA, and extracellular vesicles have been investigated as potential diagnostic and prognostic markers. Integrating such molecular information to revise the diagnostic criteria for pancreatic cancer can enable a better understanding of the pathogenesis underlying inter-patient heterogeneity, such as sensitivity to chemotherapy and disease outcomes. This review discusses the current diagnostic approaches and clinical applications of genetic analysis in pancreatic cancer and diagnostic attempts by liquid biopsy and molecular analyses using pancreatic juice, duodenal fluid, and blood samples. Emerging knowledge in the rapidly advancing liquid biopsy field is promising for molecular profiling and diagnosing pancreatic diseases with significant diversity.

Circulating Cell-Free Nucleic Acids as Biomarkers for Diagnosis and Prognosis of Pancreatic Cancer

A lack of reliable early diagnostic tools represents a major challenge in the management of pancreatic cancer (PCa), as the disease is often only identified after it reaches an advanced stage. This highlights the urgent need to identify biomarkers that can be used for the early detection, staging, treatment monitoring, and prognosis of PCa. A novel approach called liquid biopsy has emerged in recent years, which is a less- or non-invasive procedure since it focuses on plasmatic biomarkers such as DNA and RNA. In the blood of patients with cancer, circulating tumor cells (CTCs) and cell-free nucleic acids (cfNAs) have been identified such as DNA, mRNA, and non-coding RNA (miRNA and lncRNA). The presence of these molecules encouraged researchers to investigate their potential as biomarkers. In this article, we focused on circulating cfNAs as plasmatic biomarkers of PCa and analyzed their advantages compared to traditional biopsy methods.

Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.

[Prevention of pancreatic cancer]

The incidence of pancreatic cancer is rising in Germany. At present pancreatic cancer is the third commonest cause of cancer death but is expected to become the second in 2030 and finally the leading cause of cancer death in 2050. Pancreatic ductal adenocarcinoma (PC) is generally diagnosed at far advanced stages and 5-year-survival has remained poor. Modifiable risk factors of PC are tobacco smoking, excess body weight, alcohol use, type 2-diabetes and the metabolic syndrome. Smoking cessation and -in case of obesity- intentional weight loss can reduce PC risk by as much as 50 %. Early detection of asymptomatic sporadic PC at stage IA - stage IA-PC now has a 5-year-survival rate of about 80 %- has become a realistic chance for people older than 50 years with new-onset diabetes.

Difference of risk of pancreatic cancer in new-onset diabetes and long-standing diabetes: population-based cohort study

CONTEXT

Considering the absence of methods to find pancreatic cancer early, surveillance of high-risk groups is needed for early diagnosis.

OBJECTIVE

The study aimed to investigate the effect in the incidence of pancreatic cancer and the differences between new-onset DM (NODM) and long-standing DM (LSDM) since NODM group is a representative high-risk group.

METHODS

The Korean National Health Insurance Service-National Sample Cohort between 2002 and 2013 data was used. Regarding 88,396 people with DM (case group), we conducted a 1:1 propensity score matching to select a matched non-DM population (control group). To investigate the interaction between DM and the time variable distinguishing NODM and LSDM, we performed a multi-variable time-dependent Cox regression analysis.

RESULTS

The incidence of pancreatic cancer was higher in the DM group compared to the non-DM group (0.52% vs. 0.16%, P < 0.001). The DM group had shown different risk of pancreatic cancer development according to the duration since the DM diagnosis (NODM hazard ratio (HR): 3.81, 95% confidence interval (CI): 2.97-4.88, P < 0.001; LSDM HR: 1.53, 95% CI: 1.11-2.11, P < 0.001). When the NODM and the LSDM groups were compared, the risk of pancreatic cancer was higher in the NODM group than LSDM group (HR: 1.55, P = 0.020). In subgroup analysis, NODM group showed that men (HR = 4.42 95% CI: 3.15-6.19, P < 0.001) and patients who were in their 50 s (HR = 7.54, 95% CI: 3.24-17.56, P < 0.001) were at a higher risk of developing pancreatic cancer than matched same sex or age control group (non-DM population), respectively.

CONCLUSION

The risk of pancreatic cancer was greater in people with DM than non-DM population. Among people with DM, NODM showed a higher risk of pancreatic cancer than long standing DM.

United Kingdom Early Detection Initiative (UK-EDI): protocol for establishing a national multicentre cohort of individuals with new-onset diabetes for early detection of pancreatic cancer

INTRODUCTION

Pancreatic cancer is a leading cause of cancer deaths worldwide. Screening for this disease has potential to improve survival. It is not feasible, with current screening modalities, to screen the asymptomatic adult population. However, screening of individuals in high-risk groups is recommended. Our study aims to provide resources and data that will inform strategies to screen individuals with new-onset diabetes (NOD) for pancreatic cancer.

METHODS AND ANALYSIS

The United Kingdom Early Detection Initiative (UK-EDI) for pancreatic cancer is a national, prospective, observational cohort study that aims to recruit 2500 individuals with NOD (<6 months postdiagnosis) aged 50 years and over, with follow-up every 6 months, over a 3-year period. For study eligibility, diagnosis of diabetes is considered to be clinical measurement of haemoglobin A1c ≥48 mmol/mol. Detailed clinical information and biospecimens will be collected at baseline and follow-up to support the development of molecular, epidemiological and demographic biomarkers for earlier detection of pancreatic cancer in the high-risk NOD group. Socioeconomic impacts and cost-effectiveness of earlier detection of pancreatic cancer in individuals with NOD will be evaluated. The UK-EDI NOD cohort will provide a bioresource for future early detection research to be conducted.

ETHICS AND DISSEMINATION

The UK-EDI study has been reviewed and approved by the London-West London and GTAC Research Ethics Committee (Ref 20/LO/0058). Study results will be disseminated through presentations at national and international symposia and publication in peer-reviewed, Open Access journals.

Risk Factors for Pancreatic Cancer in Patients with New-Onset Diabetes: A Systematic Review and Meta-Analysis

(1) Background: Patients with new-onset diabetes (NOD) are at risk of pancreatic ductal adenocarcinoma (PDAC), but the most relevant additional risk factors and clinical characteristics are not well established. (2) Objectives: To compare the risk for PDAC in NOD patients to persons without diabetes. Identify risk factors of PDAC among NOD patients. (3) Methods: Medline, Embase, and Google Scholar were last searched in June 2022 for observational studies on NOD patients and assessing risk factors for developing PDAC. Data were extracted, and Meta-Analysis was performed. Pooled effect sizes with 95% confidence intervals (CI) were estimated with DerSimonian & Laird random effects models. (4) Findings: Twenty-two studies were included, and 576,210 patients with NOD contributed to the analysis, of which 3560 had PDAC. PDAC cases were older than controls by 6.14 years (CI 3.64–8.65, 11 studies). The highest risk of PDAC involved a family history of PDAC (3.78, CI 2.03–7.05, 4 studies), pancreatitis (5.66, CI 2.75–11.66, 9 studies), cholecystitis (2.5, CI 1.4–4.45, 4 studies), weight loss (2.49, CI 1.47–4.22, 4 studies), and high/rapidly increasing glycemia (2.33, CI 1.85–2.95, 4 studies) leading to more insulin use (4.91, CI 1.62–14.86, 5 studies). Smoking (ES 1.20, CI 1.03–1.41, 9 studies) and alcohol (ES 1.23, CI 1.09–1.38, 9 studies) have a smaller effect. (5) Conclusion: Important risk factors for PDAC among NOD patients are age, family history, and gallstones/pancreatitis. Symptoms are weight loss and rapid increase in glycemia. The identified risk factors could be used to develop a diagnostic model to screen NOD patients.

Risk Factors for Pancreatic Cancer in Patients with New-Onset Diabetes: A Systematic Review and Meta-Analysis

(1) Background: Patients with new-onset diabetes (NOD) are at risk of pancreatic ductal adenocarcinoma (PDAC), but the most relevant additional risk factors and clinical characteristics are not well established. (2) Objectives: To compare the risk for PDAC in NOD patients to persons without diabetes. Identify risk factors of PDAC among NOD patients. (3) Methods: Medline, Embase, and Google Scholar were last searched in June 2022 for observational studies on NOD patients and assessing risk factors for developing PDAC. Data were extracted, and Meta-Analysis was performed. Pooled effect sizes with 95% confidence intervals (CI) were estimated with DerSimonian & Laird random effects models. (4) Findings: Twenty-two studies were included, and 576,210 patients with NOD contributed to the analysis, of which 3560 had PDAC. PDAC cases were older than controls by 6.14 years (CI 3.64–8.65, 11 studies). The highest risk of PDAC involved a family history of PDAC (3.78, CI 2.03–7.05, 4 studies), pancreatitis (5.66, CI 2.75–11.66, 9 studies), cholecystitis (2.5, CI 1.4–4.45, 4 studies), weight loss (2.49, CI 1.47–4.22, 4 studies), and high/rapidly increasing glycemia (2.33, CI 1.85–2.95, 4 studies) leading to more insulin use (4.91, CI 1.62–14.86, 5 studies). Smoking (ES 1.20, CI 1.03–1.41, 9 studies) and alcohol (ES 1.23, CI 1.09–1.38, 9 studies) have a smaller effect. (5) Conclusion: Important risk factors for PDAC among NOD patients are age, family history, and gallstones/pancreatitis. Symptoms are weight loss and rapid increase in glycemia. The identified risk factors could be used to develop a diagnostic model to screen NOD patients.