Screening for Pancreatic Cancer: US Preventive Services Task Force Reaffirmation Recommendation Statement

Prediction of pancreatic cancer in patients with new onset hyperglycemia: A modified ENDPAC model

BACKGROUND/OBJECTIVES

The Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) model relies primarily on fasting glucose values. Health systems have increasingly shifted practice towards use of glycated hemoglobin (HbA1c) measurement. We modified the ENDPAC model using patients with new onset hyperglycemia.

METHODS

Four cohorts of patients 50-84 years of age with HbA1c results ≥6.2-6.5 % in 2011-2018 were identified. A combine cohort was formed. A widened eligibility criterion was applied to form additional four individual cohorts and one combined cohort. The primary outcome was the diagnosis of pancreatic cancer within 3 years after the first elevated HbA1c testing. The performance of the modified ENDPAC model was evaluated by AUC, sensitivity, positive predictive value, cases detected, and total number of patients screened.

RESULTS

The individual and combined cohorts consisted of 39,001-79,060 and 69,334-92,818 patients, respectively (mean age 63.5-65.0 years). The three-year PC incidence rates were 0.47%-0.54 %. The AUC measures were in the range of 0.75-0.77 for the individual cohorts and 0.75 for the combined cohorts. When the four individual cohorts were combined, more PC cases can be identified (149 by the combined vs. 113-116 by individual cohorts when risk score was 5+). Performance measures were compromised in nonwhites. Asian and Pacific islanders had lower sensitivity compared to other racial and ethnic groups (29 % vs. 50-60 %) when risk score was 5+.

CONCLUSIONS

The modified ENDPAC model targets a broader population and thus identifies more high-risk patients for cancer screening. The differential performance needs to be considered when the model is applied to non-white population.

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.

Exploring rural-urban differences in the receipt of nonelective cancer-specific gastrointestinal surgery using a multilevel mixed-effects approach

BACKGROUND

Rural communities constitute a populace marked by various social challenges influencing health outcomes. As such, nonelective surgeries for cancer may have a disproportionate impact on rural populations. We explored patient and county-level factors contributing to differences in the receipt of nonelective cancer-specific surgery between rural and urban residents.

METHODS

This retrospective study included adult patients captured in the Surveillance, Epidemiology, and End Results-Medicare data between January 2008 and December 2015 with an incident stage I to IV cancer of the stomach, liver/intrahepatic bile duct, pancreas, gallbladder/other biliary origin, or small intestine who underwent a cancer-specific surgery. The primary outcome was a nonelective cancer-directed surgery among rural vs urban residents. We conducted a multivariable mixed-effects logistic regression model to adjust for confounders while accounting for county-level clustering.

RESULTS

The sample included 10,136 patients who underwent a surgical intervention; 2941 (29%) were nonelective. The incidence of nonelective surgery was lower among rural than urban patients (351 [27%] and 2590 [29%]; P = .05). There was no statistically significant difference in the unadjusted and adjusted odds of nonelective surgery between rural and urban residents (odds ratio, 0.88; 95% CI, 0.76-1.03; P = .11; adjusted odds ratio [aOR], 0.86; 95% CI, 0.72-1.02; P = .080). In addition, high social vulnerability index (SVI) counties or Black race was significantly associated with increase odds of nonelective surgery (aOR, 1.33; 95% CI, 1.07-1.65; P = .009; aOR, 1.49; 95% CI, 1.26-1.77; P < .0001, respectively).

CONCLUSION

This study found no difference in the odds of receiving nonelective surgery for gastrointestinal foregut cancers between rural and urban populations. However, Black race and high SVI were associated with higher odds of the receipt of nonelective surgery. Further research is warranted to explore whether disparities in clinical outcomes exist despite the comparable likelihood of receiving nonelective surgery between rural and urban communities.

Enhanced cytokine signaling and ferroptosis defense interplay initiates obesity-associated pancreatic ductal adenocarcinoma

Obesity is a significant risk factor for various cancers, including pancreatic cancer (PC), but the underlying mechanisms are still unclear. In our study, pancreatic ductal epithelial cells were cultured using serum from human subjects with diverse metabolic statuses, revealing that serum from patients with obesity alters inflammatory cytokine signaling and ferroptosis, where a mutual enhancement between interleukin 34 (IL-34) expression and ferroptosis defense was observed in these cells. Notably, oncogenic KRASG12D amplified their interaction and this leads to the initiation of pancreatic ductal adenocarcinoma (PDAC) in diet-induced obese mice via macrophage-mediated immunosuppression. Single-cell RNA sequencing (scRNA-seq) of human samples showed that cytokine signaling, ferroptosis defense, and immunosuppression are correlated with the patients' body mass index (BMI) during PDAC progression. Our findings provide a mechanistic link between obesity, inflammation, ferroptosis defense, and pancreatic cancer, suggesting novel therapeutic targets for the prevention and treatment of obesity-associated PDAC.

Pancreatic cancer screening in high-risk individuals

The incidence of pancreatic cancer is increasing, although globally it represents less than 3% of all cancers. Despite advances in medical and surgical management, survival rates have not significantly improved in recent years. Consequently, pancreatic cancer, though relatively uncommon, is the third leading cause of cancer-related deaths. This is primarily due to the disease´s late detection. Symptoms appear late and are nonspecific, and over 80% of cases are diagnosed at an advanced stage and unsuitable for curative surgery, resulting in a five-year survival rate below 10%. However, the exceptional cases that are diagnosed early show five-year survival rates exceeding 80%. Therefore, one of the keys to improving pancreatic cancer prognosis lies in early detection, making screening in high-risk individuals a potentially crucial strategy.

Early detection of pancreatic cancer by comprehensive serum miRNA sequencing with automated machine learning

BACKGROUND

Pancreatic cancer is often diagnosed at advanced stages, and early-stage diagnosis of pancreatic cancer is difficult because of nonspecific symptoms and lack of available biomarkers.

METHODS

We performed comprehensive serum miRNA sequencing of 212 pancreatic cancer patient samples from 14 hospitals and 213 non-cancerous healthy control samples. We randomly classified the pancreatic cancer and control samples into two cohorts: a training cohort (N = 185) and a validation cohort (N = 240). We created ensemble models that combined automated machine learning with 100 highly expressed miRNAs and their combination with CA19-9 and validated the performance of the models in the independent validation cohort.

RESULTS

The diagnostic model with the combination of the 100 highly expressed miRNAs and CA19-9 could discriminate pancreatic cancer from non-cancer healthy control with high accuracy (area under the curve (AUC), 0.99; sensitivity, 90%; specificity, 98%). We validated high diagnostic accuracy in an independent asymptomatic early-stage (stage 0-I) pancreatic cancer cohort (AUC:0.97; sensitivity, 67%; specificity, 98%).

CONCLUSIONS

We demonstrate that the 100 highly expressed miRNAs and their combination with CA19-9 could be biomarkers for the specific and early detection of pancreatic cancer.

Pancreatic Cancer Screening among High-risk Individuals

Pancreatic ductal adenocarcinoma (PDAC) continues to remain one of the leading causes of cancer-related death. Unlike other malignancies where universal screening is recommended, the same cannot be said for PDAC. The purpose of this study is to review which patients are at high risk of developing PDAC and therefore candidates for screening, methods/frequency of screening, and risk for these groups of patients.

Disparities in 36 cancers across 185 countries: secondary analysis of global cancer statistics

Cancer is a major public health problem and represents substantial disparities worldwide. This study reported estimates for 36 cancers across 185 countries by incidence, mortality, 5-year prevalence, mortality-to-prevalence ratio (MPR), and mortality-to-incidence ratio (MIR) to examine its association with human development index (HDI) and gross national income (GNI). Data were collected from the GLOBOCAN 2020. MPR and MIR were calculated by sex, age group, country, and cancer type and then summarized into totals. Segi's population and global cancer spectrum were used to calculate age- and type-standardized ratios. Correlation analyses were conducted to assess associations. Results showed that breast cancer was the most diagnosed cancer globally. Low- and middle-income countries had high MPR and MIR. Cancers of esophagus, pancreas, and liver had the highest ratios. Males and the older population had the highest ratios. HDI and GNI were positively correlated with incidence and mortality but negatively correlated with MPR/MIR. Substantial disparities in cancer burden were observed among 36 cancer types across 185 countries. Socioeconomic development may contribute to narrowing these disparities, and tailored strategies are crucial for regional- and country-specific cancer control.

Hormone replacement therapy and risk of pancreatic cancer in postmenopausal women: Evidence from the US National Inpatient Sample 2008-2018

Background

Pancreatic cancer is a serious, usually fatal disease and one of the most aggressive malignancies. Research into whether hormone replacement therapy (HRT) might protect against pancreatic cancer has yielded mixed results. This study aimed to investigate the potential association between HRT and the risk of pancreatic cancer in postmenopausal women.

Methods

This population-based, retrospective study extracted data from the US National Inpatient Sample (NIS) 2008-2018. Hospitalized females aged ≥55 years were eligible for inclusion. Associations between HRT, other study variables, and pancreatic cancer diagnosis were determined using univariate and multivariable regression analyses.

Results

After 1:4 matching by age, data of postmenopausal women with (n = 35,309) and without (n = 141,236) HRT were included in the analysis. The mean age was 73.4 years. Multivariable analyses showed that women with HRT had significantly decreased odds of pancreatic cancer (adjusted OR [aOR], 0.69, 95 % CI: 0.53-0.90). Compared to patients without HRT, patients with HRT in the 55-64-year-old group (aOR 0.48, 95 % CI: 0.32-0.74), 65-74-year-old group (aOR 0.49, 95 % CI: 0.34-0.71), non-hypertensive group (aOR 0.55, 95 % CI: 0.38-0.79), and non-hyperlipidemia group (aOR 0.59, 95 % CI: 0.42-0.82) had significantly decreased odds of pancreatic cancer.

Conclusions

In US postmenopausal women, HRT is associated with a reduced risk of pancreatic cancer, especially those aged 55-74 year. Further study is needed to clarify the mechanisms underlying the associations.

Emerging role of local treatment in the era of advanced systemic treatment in pancreatic cancer with liver metastasis: A systematic review and meta-analysis

BACKGROUND

Approximately 50% of pancreatic cancer cases are diagnosed with distant metastases, commonly in the liver, leading to poor prognosis. With modern chemotherapy regimens extending patient survival and stabilizing metastasis, there has been a rise in the use of local treatments. However, the effectiveness for local treatment remains unclear.

METHODS

PubMed, Embase, and Cochrane databases were searched for studies reporting the survival outcomes of pancreatic cancer cases with isolated synchronous or metachronous liver metastases who underwent curative-intent local treatment. Hazard ratios were combined using a random-effects model.

RESULTS

The full texts of 102 studies were screened, and 14 retrospective studies were included in the meta-analysis. Among patients with synchronous liver metastases, overall survival was significantly better in those who underwent curative-intent local treatment than in those who did not (hazard ratio [HR]: 0.35, 95% confidence interval [CI]: 0.24-0.52). Among patients with metachronous liver metastases, overall survival was also significantly better in those who underwent curative-intent local treatment than in those who did not (HR 0.37, 95% CI: 0.19-0.73).

CONCLUSIONS

Curative-intent local treatment may be a feasible option for highly selected pancreatic cancer cases with liver metastases. However, the optimal strategy for local treatments should be explored in future studies.

Survival benefit associated with screening of patients at elevated risk for pancreatic cancer

BACKGROUND & OBJECTIVES: Screening for pancreatic cancer is recommended for individuals with a strong family history, certain genetic syndromes, or a neoplastic cyst of the pancreas. However, limited data supports a survival benefit attributable to screening these higher-risk individuals.

METHODS

All patients enrolled in screening at a High-Risk Pancreatic Cancer Clinic (HRC) from July 2013 to June 2020 were identified from a prospectively maintained institutional database and compared to patients evaluated at a Surgical Oncology Clinic (SOC) at the same institution during the same period. Clinical outcomes of patients selected for surgical resection, particularly clinicopathologic stage and overall survival, were compared.

RESULTS

Among 826 HRC patients followed for a median (IQR) of 2.3 (0.8-4.2) years, 128 were selected for surgical resection and compared to 402 SOC patients selected for resection. Overall survival was significantly longer among HRC patients (median survival: not reached vs. 2.6 years, p < 0.001). Among 31 HRC and 217 SOC patients with a diagnosis of pancreatic ductal adenocarcinoma (PDAC), the majority of HRC patients were diagnosed with stage 0 disease (carcinoma in situ), while the majority of SOC patients were diagnosed with stage II disease (p < 0.001). Overall survival after resection of invasive PDAC was also significantly longer among HRC patients compared to SOC patients (median survival 5.5 vs. 1.6 years, p = 0.002).

CONCLUSION

Patients at increased risk for PDAC and followed with guideline-based screening exhibited downstaging of disease and improved survival from PDAC in comparison to patients who were not screened.

Economic evaluations of pancreatic cancer screening: a systematic review protocol

INTRODUCTION

The early detection of pancreatic cancer is an important step in reducing mortality by offering patients curative treatment. Screening strategies in risk populations and by means of different detection methods have been economically evaluated. However, a synthesis of screening studies to inform resource allocation towards early detection within the disease area has not been done. Therefore, studies evaluating the cost-effectiveness and costs of screening for pancreatic cancer should be systematically reviewed.

METHODS AND ANALYSIS

A systematic review of economic evaluations reporting the cost-effectiveness or costs of pancreatic cancer screening will be conducted. The electronic databases Medline, Web of Science and EconLit will be searched without geographical or time restrictions. Two independent reviewers will select eligible studies based on predefined criteria. The study quality will be assessed using the Consolidated Health Economic Evaluation Reporting Standards statement and the Bias in Economic Evaluation checklist. One reviewer will extract relevant data and a second reviewer will cross-check compliance with the extraction sheet. Key items will include characteristics of screened individuals, the screening strategies used, and costs, health effects and cost-effectiveness as study outputs. Differences of opinion between the reviewers will be solved by consulting a third reviewer.

ETHICS AND DISSEMINATION

Ethics approval is not required for this study since no original data will be collected. The results will be disseminated through presentations at conferences and publication in a peer-reviewed journal. The results of the systematic review will inform future economic evaluations of pancreatic screening, which provide guidance for decision-making in healthcare resource prioritisation.

PROSPERO REGISTRATION NUMBER

CRD42023475348.

Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment

INTRODUCTION

Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction.

METHODS

Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool.

RESULTS

Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals.

DISCUSSION

AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Five-year recurrence/survival after pancreatoduodenectomy for pancreatic adenocarcinoma: does pre-existing diabetes matter? Results from the Recurrence After Whipple's (RAW) study

BACKGROUND

Diabetes mellitus (DM) has a complex relationship with pancreatic cancer. This study examines the impact of preoperative DM, both recent-onset and pre-existing, on long-term outcomes following pancreatoduodenectomy (PD) for pancreatic ductal adenocarcinoma (PDAC).

METHODS

Data were extracted from the Recurrence After Whipple's (RAW) study, a multi-centre cohort of PD for pancreatic head malignancy (2012-2015). Recurrence and five-year survival rates of patients with DM were compared to those without, and subgroup analysis performed to compare patients with recent-onset DM (less than one year) to patients with established DM.

RESULTS

Out of 758 patients included, 187 (24.7%) had DM, of whom, 47 of the 187 (25.1%) had recent-onset DM. There was no difference in the rate of postoperative pancreatic fistula (DM: 5.9% vs no DM 9.8%; p = 0.11), five-year survival (DM: 24.1% vs no DM: 22.9%; p = 0.77) or five-year recurrence (DM: 71.7% vs no DM: 67.4%; p = 0.32). There was also no difference between patients with recent-onset DM and patients with established DM in postoperative outcomes, recurrence, or survival.

CONCLUSION

We found no difference in five-year recurrence and survival between diabetic patients and those without diabetes. Patients with pre-existing DM should be evaluated for PD on a comparable basis to non-diabetic patients.

Machine Learning Predicts Patients With New-onset Diabetes at Risk of Pancreatic Cancer

BACKGROUND

New-onset diabetes represent a high-risk cohort to screen for pancreatic cancer.

GOALS

Develop a machine model to predict pancreatic cancer among patients with new-onset diabetes.

STUDY

A retrospective cohort of patients with new-onset diabetes was assembled from multiple health care networks in the United States. An XGBoost machine learning model was designed from a portion of this cohort (the training set) and tested on the remaining part of the cohort (the test set). Shapley values were used to explain the XGBoost's model features. Model performance was compared with 2 contemporary models designed to predict pancreatic cancer among patients with new-onset diabetes.

RESULTS

In the test set, the XGBoost model had an area under the curve of 0.80 (0.76 to 0.85) compared with 0.63 and 0.68 for other models. Using cutoffs based on the Youden index, the sensitivity of the XGBoost model was 75%, the specificity was 70%, the accuracy was 70%, the positive predictive value was 1.2%, and the negative predictive value was >99%. The XGBoost model obtained a positive predictive value of at least 2.5% with a sensitivity of 38%. The XGBoost model was the only model that detected at least 50% of patients with cancer one year after the onset of diabetes. All 3 models had similar features that predicted pancreatic cancer, including older age, weight loss, and the rapid destabilization of glucose homeostasis.

CONCLUSION

Machine learning models isolate a high-risk cohort from those with new-onset diabetes at risk for pancreatic cancer.

Pancreatic Cancer Surveillance and Survival of High-Risk Individuals

Importance

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease with increasing incidence. The majority of PDACs are incurable at presentation, but population-based screening is not recommended. Surveillance of high-risk individuals for PDAC may lead to early detection, but the survival benefit is unproven.

Objective

To compare the survival of patients with surveillance-detected PDAC with US national data.

Design, Setting, and Participants

This comparative cohort study was conducted in multiple US academic medical centers participating in the Cancer of the Pancreas Screening program, which screens high-risk individuals with a familial or genetic predisposition for PDAC. The comparison cohort comprised patients with PDAC matched for age, sex, and year of diagnosis from the Surveillance, Epidemiology, and End Results (SEER) program. The Cancer of the Pancreas Screening program originated in 1998, and data collection was done through 2021. The data analysis was performed from April 29, 2022, through April 10, 2023.

Exposures

Endoscopic ultrasonography or magnetic resonance imaging performed annually and standard-of-care surgical and/or oncologic treatment.

Main Outcomes and Measures

Stage of PDAC at diagnosis, overall survival (OS), and PDAC mortality were compared using descriptive statistics and conditional logistic regression, Cox proportional hazards regression, and competing risk regression models. Sensitivity analyses and adjustment for lead-time bias were also conducted.

Results

A total of 26 high-risk individuals (mean [SD] age at diagnosis, 65.8 [9.5] years; 15 female [57.7%]) with PDAC were compared with 1504 SEER control patients with PDAC (mean [SD] age at diagnosis, 66.8 [7.9] years; 771 female [51.3%]). The median primary tumor diameter of the 26 high-risk individuals was smaller than in the control patients (2.5 [range, 0.6-5.0] vs 3.6 [range, 0.2-8.0] cm, respectively; P < .001). The high-risk individuals were more likely to be diagnosed with a lower stage (stage I, 10 [38.5%]; stage II, 8 [30.8%]) than matched control patients (stage I, 155 [10.3%]; stage II, 377 [25.1%]; P < .001). The PDAC mortality rate at 5 years was lower for high-risk individuals than control patients (43% vs 86%; hazard ratio, 3.58; 95% CI, 2.01-6.39; P < .001), and high-risk individuals lived longer than matched control patients (median OS, 61.7 [range, 1.9-147.3] vs 8.0 [range, 1.0-131.0] months; 5-year OS rate, 50% [95% CI, 32%-80%] vs 9% [95% CI, 7%-11%]).

Conclusions and Relevance

These findings suggest that surveillance of high-risk individuals may lead to detection of smaller, lower-stage PDACs and improved survival.

Utilization and Outcomes of Multigene Panel Testing in Patients With Pancreatic Ductal Adenocarcinoma

PURPOSE

Guidelines recommend germline genetic testing (GT) for patients with pancreatic ductal adenocarcinoma (PDAC). This study aims to evaluate the utilization and outcomes of multigene panel GT in patients with PDAC.

METHODS

This retrospective, multisite study included patients with PDAC diagnosed between May 2018 and August 2020 at Mayo Clinic Arizona, Florida, and Minnesota. Discussion, uptake, and outcomes of GT were compared before (May 1, 2018-May 1, 2019) and after (August 1, 2019-August 1, 2020) the guideline update, accounting for a transition period.

RESULTS

The study identified 533 patients with PDAC, with 321 (60.2%) preguideline and 212 (39.8%) postguideline. Patient characteristics did not differ between the preguideline and postguideline periods. GT was discussed in 34.3% (110 of 321) of preguideline and 39.6% (84 of 212) of postguideline patients (odds ratio [OR], 1.26 [95% CI, 0.88 to 1.80]) and subsequently performed in 80.9% (89 of 110) of preguideline and 75.0% (63 of 84) of postguideline patients (OR, 1.10 [95% CI, 0.75 to 1.61]). Of 152 tested patients, 26 (17.1%) had a pathogenic variant (PV), of whom 17 (11.2%; 17 of 152) were PDAC-associated. Over the entire study period, GT was more likely in younger patients (65 v 70 years; P < .001), those seen by a medical oncologist (82.9% v 69.0%; P < .001), and those surviving more than 12 months from diagnosis (70.4% v 43.4%; P < .001). Demographics and personal/family cancer history were comparable between patients with and without a PDAC PV.

CONCLUSION

GT remains underutilized despite National Comprehensive Cancer Network guideline recommendations. Given the poor prognosis of PDAC and potential implications of GT, efforts to increase utilization are needed to provide surveillance and support to both patients with PDAC and at-risk family members.

The Added Value of Blood Glucose Monitoring in High-Risk Individuals Undergoing Pancreatic Cancer Surveillance

OBJECTIVES

The study aimed to investigate the added value of blood glucose monitoring in high-risk individuals (HRIs) participating in pancreatic cancer surveillance.

MATERIALS AND METHODS

High-risk individuals with a CDKN2A/p16 germline pathogenic variant participating in pancreatic cancer surveillance were included in this study. Multivariable logistic regression was performed to assess the relationship between new-onset diabetes (NOD) and pancreatic ductal adenocarcinoma (PDAC). To quantify the diagnostic performance of NOD as a marker for PDAC, receiver operating characteristic curve with area under the curve was computed.

RESULTS

In total, 220 HRIs were included between 2000 and 2019. Median age was 61 (interquartile range. 53-71) years and 62.7% of participants were female. During the study period, 26 (11.8%) HRIs developed NOD, of whom 5 (19.2%) later developed PDAC. The other 23 (82.1%) PDAC cases remained NOD-free. Multivariable analysis showed no statistically significant relationship between NOD and PDAC (odds ratio, 1.21; 95% confidence interval, 0.39-3.78) and 4 of 5 PDAC cases seemed to have NOD within 3 months before diagnosis. Furthermore, NOD did not differentiate between HRIs with and without PDAC (area under the curve, 0.54; 95% confidence interval, 0.46-0.61).

CONCLUSIONS

In this study, we found no added value for longitudinal glucose monitoring in CDKN2A pathogenic variant carriers participating in an imaging-based pancreatic cancer surveillance program.

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.

Identification of a serum proteomic biomarker panel using diagnosis specific ensemble learning and symptoms for early pancreatic cancer detection

BACKGROUND

The grim (<10% 5-year) survival rates for pancreatic ductal adenocarcinoma (PDAC) are attributed to its complex intrinsic biology and most often late-stage detection. The overlap of symptoms with benign gastrointestinal conditions in early stage further complicates timely detection. The suboptimal diagnostic performance of carbohydrate antigen (CA) 19-9 and elevation in benign hyperbilirubinaemia undermine its reliability, leaving a notable absence of accurate diagnostic biomarkers. Using a selected patient cohort with benign pancreatic and biliary tract conditions we aimed to develop a data analysis protocol leading to a biomarker signature capable of distinguishing patients with non-specific yet concerning clinical presentations, from those with PDAC.

METHODS

539 patient serum samples collected under the Accelerated Diagnosis of neuro Endocrine and Pancreatic TumourS (ADEPTS) study (benign disease controls and PDACs) and the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS, healthy controls) were screened using the Olink Oncology II panel, supplemented with five in-house markers. 16 specialized base-learner classifiers were stacked to select and enhance biomarker performances and robustness in blinded samples. Each base-learner was constructed through cross-validation and recursive feature elimination in a discovery set comprising approximately two thirds of the ADEPTS and UKCTOCS samples and contrasted specific diagnosis with PDAC.

RESULTS

The signature which was developed using diagnosis-specific ensemble learning demonstrated predictive capabilities outperforming CA19-9, the only biomarker currently accepted by the FDA and the National Comprehensive Cancer Network guidelines for pancreatic cancer, and other individual biomarkers and combinations in both discovery and held-out validation sets. An AUC of 0.98 (95% CI 0.98-0.99) and sensitivity of 0.99 (95% CI 0.98-1) at 90% specificity was achieved with the ensemble method, which was significantly larger than the AUC of 0.79 (95% CI 0.66-0.91) and sensitivity 0.67 (95% CI 0.50-0.83), also at 90% specificity, for CA19-9, in the discovery set (p = 0.0016 and p = 0.00050, respectively). During ensemble signature validation in the held-out set, an AUC of 0.95 (95% CI 0.91-0.99), sensitivity 0.86 (95% CI 0.68-1), was attained compared to an AUC of 0.80 (95% CI 0.66-0.93), sensitivity 0.65 (95% CI 0.48-0.56) at 90% specificity for CA19-9 alone (p = 0.0082 and p = 0.024, respectively). When validated only on the benign disease controls and PDACs collected from ADEPTS, the diagnostic-specific signature achieved an AUC of 0.96 (95% CI 0.92-0.99), sensitivity 0.82 (95% CI 0.64-0.95) at 90% specificity, which was still significantly higher than the performance for CA19-9 taken as a single predictor, AUC of 0.79 (95% CI 0.64-0.93) and sensitivity of 0.18 (95% CI 0.03-0.69) (p = 0.013 and p = 0.0055, respectively).

CONCLUSION

Our ensemble modelling technique outperformed CA19-9, individual biomarkers and indices developed with prevailing algorithms in distinguishing patients with non-specific but concerning symptoms from those with PDAC, with implications for improving its early detection in individuals at risk.

Pancreatic cancer screening is effective in individuals at risk with predisposing germline gene variants, but not in gene variant-negative familial pancreatic cancer families

OBJECTIVE

To evaluate the diagnostic yield of pancreatic cancer screening in individuals at risk (IAR) from familial pancreatic cancer (FPC) families with respect to the presence or absence of pathogenic germline variants predisposing to pancreatic adenocarcinoma (PDAC).

DESIGN

In a 20 years period, IAR from FPC families were enrolled in a prospective screening program of the national case collection for FPC of Germany, including magnet resonance imaging (MRI) and endoscopic ultrasound (EUS). The diagnostic yield was analyzed regarding significant pancreatic lesions such as PDAC, high-grade pancreatic-intraepithelial-neoplasia (PanIN3) and intraductal-papillary-mucinous-neoplasia (IPMN) with high-grade dysplasia. Screening results were compared between carriers of pathogenic variants and variant-negative IAR.

RESULTS

337 IAR, including 74 (22%) variant-carriers and 263 IAR of variant-negative FPC families (mean age 49; standard deviation [SD] + 8.9) were followed 64 (SD + 55) months. IAR underwent 5.1 (SD + 3.9) screening visits with 1733 MRI (5.1,SD + 3.9 per IAR) and 728 EUS (2.2,SD + 1.7 per IAR). In 12 (4%) cases, significant pancreatic lesions were detected, including 4 PDAC, 3 PanIN3 and 5 high-grade IPMN. Three of 4 IAR with PDAC died after a mean of 27 months postoperatively, and one IAR is alive without evidence of disease after 31 months. The diagnostic yield for significant lesions was 13.5% (10/74) for variant carriers compared to 0.8% (2/263) for IAR of variant-negative FPC families (p < 0.001). Logistic regression analysis revealed that a negative variant status was almost always accompanied by the absence of a significant lesion over time with a negative predictive value of 99.2% (95% CI 97.3%-99.9%).

CONCLUSION

The diagnostic yield seems to justify PDAC screening in IAR of FPC-families with pathogenic germline variants in PDAC predisposing genes, not in IAR of variant-negative families.

Ultrasound-Activated PROTAC Prodrugs Overcome Immunosuppression to Actuate Efficient Deep-Tissue Sono-Immunotherapy in Orthotopic Pancreatic Tumor Mouse Models

The degradation of oncoproteins mediated by proteolysis-targeting chimera (PROTAC) has emerged as a potent strategy in cancer therapy. However, the clinical application of PROTACs is hampered by challenges such as poor water solubility and off-target adverse effects. Herein, we present an ultrasound (US)-activatable PROTAC prodrug termed NPCe6+PRO for actuating efficient sono-immunotherapy in a spatiotemporally controllable manner. Specifically, US irradiation, which exhibits deep-tissue penetration capability, results in Ce6-mediated generation of ROS, facilitating sonodynamic therapy (SDT) and inducing immunogenic cell death (ICD). Simultaneously, the generated ROS cleaves the thioketal (TK) linker through a ROS-responsive mechanism, realizing the on-demand activation of the PROTAC prodrug in deep tissues. This prodrug activation results in the degradation of the target protein BRD4, while simultaneously reversing the upregulation of PD-L1 expression associated with the SDT process. In the orthotopic mouse model of pancreatic tumors, NPCe6+PRO effectively suppressed tumor growth in conjunction with US stimulation.

Public Fear of Pancreatic Diseases: Causes and Clinical Outcomes at a Single Korean Center

Background/Aims

The public fear of pancreatic diseases including pancreatic cancer (PC) appears to be growing. The aims of this study were to evaluate the causes of fear of pancreatic diseases and assess clinical outcomes of such individuals.

Methods

This was a retrospective study of 249 individuals who visited the Pancreatobiliary Diseases Center at Ewha Womans University Seoul Hospital due to the fear of pancreatic diseases between January 2019 and August 2021. Those referred from other departments or external medical facilities were excluded. Collected data included demographic details, comorbidities, causes of fear of pancreatic diseases, and the presence of pancreatic lesions in imaging studies.

Results

The median age was 55 years (range, 22 to 82 years). One hundred eleven subjects (44.6%) were male. The causes of fear of pancreatic diseases were abdominal pain (n=144, 57.8%), back pain (n=114, 45.8%), body weight change (n=35, 14.1%), family history of pancreatic diseases (n=32, 12.9%), and others (n=39, 15.7%). Within the group with family history of pancreatic diseases, 25 subjects had a first-degree relative with PC. Of the 200 subjects who underwent imaging, there was no evidence of pancreatic diseases in 182 (91.0%). Pancreatic lesions identified were cystic lesions (n=15, 7.5%), non-specific calcification (n=1, 0.5%), lipoma (n=1, 0.5%), and solid tumor (n=1, 0.5%), later diagnosed as unresectable PC.

Conclusions

Abdominal pain and back pain were the major causes of fear of pancreatic diseases. The prevalence of PC among those who underwent imaging was 0.5%. Such characteristics should be considered when consulting individuals with fear of pancreatic diseases.

Changing Trends in the Global Disease Burden of Pancreatic Cancer from 1990 to 2030

AIM

To explore the global burden of pancreatic cancer (PC) from 1990 to 2019, evaluate independent effects of age, period, and cohort on the incidence of PC, and predict the incidence of PC in the next decade.

METHODS

Data were obtained from the Global Burden of Disease Study 2019. We calculated the age-standardized disability-adjusted life years (DALY) rate, age-standardized mortality rate (ASMR), age-standardized incidence rate (ASIR), and age-standardized prevalence rate (ASPR) of PC. Joinpoint Poisson regression analysis was performed to identify the temporal trends in the incidence of PC. Then, a two-factor model was constructed using the Poisson log-linear model, and a three-factor model was constructed using the intrinsic estimator (IE) method to estimate the independent effects of age, period, and cohort on the incidence of PC. Finally, the Bayesian age-period-cohort (BAPC) model was also used to predict the age-standardized global incidence rate of PC and age-standardized new PC cases from 2020 to 2030.

RESULTS

Overall, the DALY rate, ASMR, ASIR, and ASPR all increased from 1990 to 2019. The ASIR in males increased from 6 per 100,000 in 1990 to 7.5 per 100,000 in 2019 and was predicted to rise to 8.2 per 100,000 by 2030. Meanwhile, the ASIR in females rose from 4.5 per 100,000 in 1990 to 5.7 per 100,000 in 2019 and was predicted to rise to 6.3 per 100,000 by 2030. The age effect on the incidence of PC showed sharp increasing trends from 40 to 79 years. The period effect continuously increased with advancing periods, but the cohort effect showed substantial decreasing trends.

CONCLUSIONS

The age and period effect on the incidence of PC presented increasing trends, while the cohort effect showed decreasing trends. All indicators of the global burden of PC are increasing in both males and females, and the ASIR is predicted to rise at an alarming rate by 2030. Thus, timely screening and intervention are recommended, especially for earlier birth cohorts at high risk.

14-3-3σ downregulation sensitizes pancreatic cancer to carbon ions by suppressing the homologous recombination repair pathway

This study explored the role of 14-3-3σ in carbon ion-irradiated pancreatic adenocarcinoma (PAAD) cells and xenografts and clarified the underlying mechanism. The clinical significance of 14-3-3σ in patients with PAAD was explored using publicly available databases. 14-3-3σ was silenced or overexpressed and combined with carbon ions to measure cell proliferation, cell cycle, and DNA damage repair. Immunoblotting and immunofluorescence (IF) assays were used to determine the underlying mechanisms of 14-3-3σ toward carbon ion radioresistance. We used the BALB/c mice to evaluate the biological behavior of 14-3-3σ in combination with carbon ions. Bioinformatic analysis revealed that PAAD expressed higher 14-3-3σ than normal pancreatic tissues; its overexpression was related to invasive clinicopathological features and a worse prognosis. Knockdown or overexpression of 14-3-3σ demonstrated that 14-3-3σ promoted the survival of PAAD cells after carbon ion irradiation. And 14-3-3σ was upregulated in PAAD cells during DNA damage (carbon ion irradiation, DNA damaging agent) and promotes cell recovery. We found that 14-3-3σ resulted in carbon ion radioresistance by promoting RPA2 and RAD51 accumulation in the nucleus in PAAD cells, thereby increasing homologous recombination repair (HRR) efficiency. Blocking the HR pathway consistently reduced 14-3-3σ overexpression-induced carbon ion radioresistance in PAAD cells. The enhanced radiosensitivity of 14-3-3σ depletion on carbon ion irradiation was also demonstrated in vivo. Altogether, 14-3-3σ functions in tumor progression and can be a potential target for developing biomarkers and treatment strategies for PAAD along with incorporating carbon ion irradiation.

Identification of pancreatic cancer risk factors from clinical notes using natural language processing

OBJECTIVES

Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR.

METHODS

We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall.

RESULTS

In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations.

CONCLUSIONS

Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.

Predicting the risk of pancreatic cancer in women with new-onset diabetes mellitus

BACKGROUND AND AIM

People with new-onset diabetes mellitus (diabetes) could be a possible target population for pancreatic cancer surveillance. However, distinguishing diabetes caused by pancreatic cancer from type 2 diabetes remains challenging. We aimed to develop and validate a model to predict pancreatic cancer among women with new-onset diabetes.

METHODS

We conducted a retrospective cohort study among Australian women newly diagnosed with diabetes, using first prescription of anti-diabetic medications, sourced from administrative data, as a surrogate for the diagnosis of diabetes. The outcome was a diagnosis of pancreatic cancer within 3 years of diabetes diagnosis. We used prescription medications, severity of diabetes (i.e., change/addition of medication within 2 months after first medication), and age at diabetes diagnosis as potential predictors of pancreatic cancer.

RESULTS

Among 99 687 women aged ≥ 50 years with new-onset diabetes, 602 (0.6%) were diagnosed with pancreatic cancer within 3 years. The area under the receiver operating curve for the risk prediction model was 0.73. Age and diabetes severity were the two most influential predictors followed by beta-blockers, acid disorder drugs, and lipid-modifying agents. Using a risk threshold of 50%, sensitivity and specificity were 69% and the positive predictive value (PPV) was 1.3%.

CONCLUSIONS

Our model doubled the PPV of pancreatic cancer in women with new-onset diabetes from 0.6% to 1.3%. Age and rapid progression of diabetes were important risk factors, and pancreatic cancer occurred more commonly in women without typical risk factors for type 2 diabetes. This model could prove valuable as an initial screening tool, especially as new biomarkers emerge.

Guiding post-pancreaticoduodenectomy interventions for pancreatic cancer patients utilizing decision tree models

Background

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in advanced stages, necessitating pancreaticoduodenectomy (PD) as a primary therapeutic approach. However, PD surgery can engender intricate complications. Thus, understanding the factors influencing postoperative complications documented in electronic medical records and their impact on survival rates is crucial for improving overall patient outcomes.

Methods

A total of 749 patients were divided into two groups: 598 (79.84%) chose the RPD (Robotic pancreaticoduodenectomy) procedure and 151 (20.16%) chose the LPD (Laparoscopic pancreaticoduodenectomy) procedure. We used correlation analysis, survival analysis, and decision tree models to find the similarities and differences about postoperative complications and prognostic survival.

Results

Pancreatic cancer, known for its aggressiveness, often requires pancreaticoduodenectomy as an effective treatment. In predictive models, both BMI and surgery duration weigh heavily. Lower BMI correlates with longer survival, while patients with heart disease and diabetes have lower survival rates. Complications like delayed gastric emptying, pancreatic fistula, and infection are closely linked post-surgery, prompting conjectures about their causal mechanisms. Interestingly, we found no significant correlation between nasogastric tube removal timing and delayed gastric emptying, suggesting its prompt removal post-decompression.

Conclusion

This study aimed to explore predictive factors for postoperative complications and survival in PD patients. Effective predictive models enable early identification of high-risk individuals, allowing timely interventions. Higher BMI, heart disease, or diabetes significantly reduce survival rates in pancreatic cancer patients post-PD. Additionally, there's no significant correlation between DGE incidence and postoperative extubation time, necessitating further investigation into its interaction with pancreatic fistula and infection.

Pancreatic cancer: from early detection to personalized treatment approaches

Pancreatic cancer is notorious for its persistently poor prognosis and health outcomes, so some of the questions that may be begged are "Why is it mostly diagnosed at end stage?", "What could we possibly do with the advancing technology in today's world to detect early pancreatic cancer and intervene?", and "Are there any implementation of the existing novel imaging technologies?". Well, to start with, this is in part because the majority of patients presented would already have reached a locally advanced or metastatic stage at the time of diagnosis due to its highly aggressive characteristics and lack of symptoms. Due to this striking disparity in survival, advancements in early detection and intervention are likely to significantly increase patients' survival. Presently, screening is frequently used in high-risk individuals in order to obtain an early pancreatic cancer diagnosis. Having a thorough understanding of the pathogenesis and risk factors of pancreatic cancer may enable us to identify individuals at high risk, diagnose the disease early, and begin treatment promptly. In this review, the authors outline the clinical hurdles to early pancreatic cancer detection, describe high-risk populations, and discuss current screening initiatives for high-risk individuals. The ultimate goal of this current review is to study the roles of both traditional and novel imaging modalities for early pancreatic cancer detection. A lot of the novel imaging techniques mentioned seem promising, but they need to be put to the test on a large scale and may need to be combined with other non-invasive biomarkers before they can be widely used.

Early features of pancreatic cancer on magnetic resonance imaging (MRI): a case-control study

PURPOSE

Magnetic resonance imaging has been recommended as a primary imaging modality among high-risk individuals undergoing screening for pancreatic cancer. We aimed to delineate potential precursor lesions for pancreatic cancer on MR imaging.

METHODS

We conducted a case-control study at Kaiser Permanente Southern California (2008-2018) among patients that developed pancreatic cancer who had pre-diagnostic MRI examinations obtained 2-36 months prior to cancer diagnosis (cases) matched 1:2 by age, gender, race/ethnicity, contrast status and year of scan (controls). Patients with history of acute/chronic pancreatitis or prior pancreatic surgery were excluded. Images underwent blind review with assessment of a priori defined series of parenchymal and ductal features. We performed logistic regression to assess the associations between individual factors and pancreatic cancer. We further assessed the interaction among features as well as performed a sensitivity analysis stratifying based on specific time-windows (2-3 months, 4-12 months, 13-36 months prior to cancer diagnosis).

RESULTS

We identified 141 cases (37.9% stage I-II, 2.1% III, 31.4% IV, 28.6% unknown) and 292 matched controls. A solid mass was noted in 24 (17%) of the pre-diagnostic MRI scans. Compared to controls, pre-diagnostic images from cancer cases more frequently exhibited the following ductal findings: main duct dilatation (51.4% vs 14.3%, OR [95% CI]: 7.75 [4.19-15.44], focal pancreatic duct stricture with distal (upstream) dilatation (43.6% vs 5.6%, OR 12.71 [6.02-30.89], irregularity (42.1% vs 6.0%, OR 9.73 [4.91-21.43]), focal pancreatic side branch dilation (13.6% vs1.6%, OR 11.57 [3.38-61.32]) as well as parenchymal features: atrophy (57.9% vs 27.4%, OR 46.4 [2.71-8.28], focal area of signal abnormality (39.3% vs 4.8%, OR 15.69 [6.72-44,78]), all p < 0.001).

CONCLUSION

In addition to potential missed lesions, we have identified a series of ductal and parenchymal features on MRI that are associated with increased odds of developing pancreatic cancer.

The Use of Glucagon-like Peptide-1 Receptor Agonists in Patients with Type 2 Diabetes Mellitus Does Not Increase the Risk of Pancreatic Cancer: A U.S.-Based Cohort Study

BACKGROUND

GLP-1 RAs are widely used for T2DM treatment due to their cardiorenal and metabolic benefits. This study examines the risk of pancreatic cancer with GLP-1 RA use in patients with T2DM.

METHODS

We analyzed TriNetX's deidentified research database using the U.S. Collaborative Network comprising 62 healthcare organizations across the U.S.A. Patients with T2DM were split into two cohorts: one receiving GLP-1 RAs, and one not receiving GLP-1 RAs. We excluded patients with known risk factors for pancreatic cancer, including pancreatic cysts, a personal or family history of BRCA1, BRCA2, CDKN2A, KRAS, MEN1, MLH1, MSH2, NOTCH1, PALB2, PMS2, and PRSS1S genes, family history of pancreatic cancer, and VHL syndrome. Using a 1:1 propensity score-matching model based on baseline characteristics and comorbidities, we created comparable cohorts. We then compared the rate of pancreatic cancer between the two cohorts at a 7-year interval.

RESULTS

Out of 7,146,015 identified patients with T2DM, 10.3% were on a GLP-1 RA and 89.7% were not. Post-PSM, 721,110 patients were in each group. Patients on GLP-1 RAs had a 0.1% risk compared to a 0.2% risk of pancreatic cancer in the 7-year timeframe.

CONCLUSION

The use of GLP-1 RAs in patients with type 2 diabetes mellitus (T2DM) does not appear to substantially elevate the risk of pancreatic cancer; in fact, it may potentially exert a protective effect.

Liquid Biopsy for Detection of Pancreaticobiliary Cancers by Functional Enrichment and Immunofluorescent Profiling of Circulating Tumor Cells and Their Clusters

Circulating tumor cells (CTCs) have historically been used for prognostication in oncology. We evaluate the performance of liquid biopsy CTC assay as a diagnostic tool in suspected pancreaticobiliary cancers (PBC). The assay utilizes functional enrichment of CTCs followed by immunofluorescent profiling of organ-specific markers. The performance of the assay was first evaluated in a multicentric case-control study of blood samples from 360 participants, including 188 PBC cases (pre-biopsy samples) and 172 healthy individuals. A subsequent prospective observational study included pre-biopsy blood samples from 88 individuals with suspicion of PBC and no prior diagnosis of cancer. CTCs were harvested using a unique functional enrichment method and used for immunofluorescent profiling for CA19.9, Maspin, EpCAM, CK, and CD45, blinded to the tissue histopathological diagnosis. TruBlood® malignant or non-malignant predictions were compared with tissue diagnoses to establish sensitivity and specificity. The test had 95.9% overall sensitivity (95% CI: 86.0-99.5%) and 92.3% specificity (95% CI: 79.13% to 98.38%) to differentiate PBC (n = 49) from benign conditions (n = 39). The high accuracy of the CTC-based TruBlood test demonstrates its potential clinical application as a diagnostic tool to assist the effective detection of PBC when tissue sampling is unviable or inconclusive.

Pancreatic Cysts Greater Than 1 cm Are Associated With an Increased Risk for Developing Pancreatic Cancer in Individuals From Pancreatic-Cancer Prone Kindreds Undergoing Surveillance

BACKGROUND

The International Cancer of the Pancreas Screening Consortium recommended annual imaging for individuals at increased risk for developing a pancreatic ductal adenocarcinoma (PDAC) who did not have concerning pancreatic findings or a cyst <3 cm without worrisome features. We aimed to determine if 3-cm cyst size accurately predicted advanced precursor lesions in high-risk individuals undergoing surveillance.

METHODS

Imaging for high-risk individuals (HRIs) undergoing PDAC surveillance from 2007 to 2021 was reviewed and pancreatic abnormalities were recorded including dominant cyst size and number of cysts. Subjects were excluded if they had the following: (1) no follow-up imaging after baseline, (2) solid lesion at baseline, or (3) development of solid lesion without having cyst on prior imaging.

RESULTS

Five of the 77 HRIs found to have a cystic lesion on surveillance developed a PDAC: 3 with cystic lesion >1 cm as compared with only 2 of 67 HRIs with cystic lesions <1 cm (P < 0.05). None of these cysts developed worrisome findings and 4/5 PDACs did not arise from visualized cystic precursor lesion.

CONCLUSIONS

Patients with a cyst ≥1 cm were at increased risk for developing PDAC compared with patients with cyst <1 cm. Pancreatic ductal adenocarcinoma usually did not arise from a recognized cystic lesion.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Identification of breath volatile organic compounds to distinguish pancreatic adenocarcinoma, pancreatic cystic neoplasm, and patients without pancreatic lesions

BACKGROUND

Volatile organic compounds (VOCs) are a promising potential biomarker that may be able to identify the presence of cancers.

AIM

To identify exhaled breath VOCs that distinguish pancreatic ductal adenocarcinoma (PDAC) from intraductal papillary mucinous neoplasm (IPMN) and healthy volunteers.

METHODS

We collected exhaled breath from histologically proven PDAC patients, radiological diagnosis IPMN, and healthy volunteers using the ReCIVA® device between 10/2021-11/2022. VOCs were identified by thermal desorption-gas chromatography/field-asymmetric ion mobility spectrometry and compared between groups.

RESULTS

A total of 156 participants (44% male, mean age 62.6 ± 10.6) were enrolled (54 PDAC, 42 IPMN, and 60 controls). Among the nine VOCs identified, two VOCs that showed differences between groups were dimethyl sulfide [0.73 vs 0.74 vs 0.94 arbitrary units (AU), respectively; P = 0.008] and acetone dimers (3.95 vs 4.49 vs 5.19 AU, respectively; P < 0.001). After adjusting for the imbalance parameters, PDAC showed higher dimethyl sulfide levels than the control and IPMN groups, with adjusted odds ratio (aOR) of 6.98 (95%CI: 1.15-42.17) and 4.56 (1.03-20.20), respectively (P < 0.05 both). Acetone dimer levels were also higher in PDAC compared to controls and IPMN (aOR: 5.12 (1.80-14.57) and aOR: 3.35 (1.47-7.63), respectively (P < 0.05 both). Acetone dimer, but not dimethyl sulfide, performed better than CA19-9 in PDAC diagnosis (AUROC 0.910 vs 0.796). The AUROC of acetone dimer increased to 0.936 when combined with CA19-9, which was better than CA19-9 alone (P < 0.05).

CONCLUSION

Dimethyl sulfide and acetone dimer are VOCs that potentially distinguish PDAC from IPMN and healthy participants. Additional prospective studies are required to validate these findings.

Enhancing Fractionated Cancer Therapy: A Triple-Anthracene Photosensitizer Unleashes Long-Persistent Photodynamic and Luminous Efficacy

Conventional photodynamic therapy (PDT) is often limited in treating solid tumors due to hypoxic conditions that impede the generation of reactive oxygen species (ROS), which are critical for therapeutic efficacy. To address this issue, a fractionated PDT protocol has been suggested, wherein light irradiation is administered in stages separated by dark intervals to permit oxygen recovery during these breaks. However, the current photosensitizers used in fractionated PDT are incapable of sustaining ROS production during the dark intervals, leading to suboptimal therapeutic outcomes (Table S1). To circumvent this drawback, we have synthesized a novel photosensitizer based on a triple-anthracene derivative that is designed for prolonged ROS generation, even after the cessation of light exposure. Our study reveals a unique photodynamic action of these derivatives, facilitating the direct and effective disruption of biomolecules and significantly improving the efficacy of fractionated PDT (Table S2). Moreover, the existing photosensitizers lack imaging capabilities for monitoring, which constraints the fine-tuning of irradiation parameters (Table S1). Our triple-anthracene derivative also serves as an afterglow imaging agent, emitting sustained luminescence postirradiation. This imaging function allows for the precise optimization of intervals between PDT sessions and aids in determining the timing for subsequent irradiation, thus enabling meticulous control over therapy parameters. Utilizing our novel triple-anthracene photosensitizer, we have formulated a fractionated PDT regimen that effectively eliminates orthotopic pancreatic tumors. This investigation highlights the promise of employing long-persistent photodynamic activity in advanced fractionated PDT approaches to overcome the current limitations of PDT in solid tumor treatment.

Clinical development of a blood biomarker using apolipoprotein-A2 isoforms for early detection of pancreatic cancer

BACKGROUND

We have previously reported apolipoprotein A2-isoforms (apoA2-is) as candidate plasma biomarkers for early-stage pancreatic cancer. The aim of this study was the clinical development of apoA2-is.

METHODS

We established a new enzyme-linked immunosorbent sandwich assay for apoA2-is under the Japanese medical device Quality Management System requirements and performed in vitro diagnostic tests with prespecified end points using 2732 plasma samples. The clinical equivalence and significance of apoA2-is were compared with CA19-9.

RESULTS

The point estimate of the area under the curve to distinguish between pancreatic cancer (n = 106) and healthy controls (n = 106) was higher for apoA2-ATQ/AT [0.879, 95% confidence interval (CI): 0.832-0.925] than for CA19-9 (0.849, 95% CI 0.793-0.905) and achieved the primary end point. The cutoff apoA2-ATQ/AT of 59.5 μg/mL was defined based on a specificity of 95% in 2000 healthy samples, and the reliability of specificities was confirmed in two independent healthy cohorts as 95.3% (n = 106, 95% CI 89.4-98.0%) and 95.8% (n = 400, 95% CI 93.3-97.3%). The sensitivities of apoA2-ATQ/AT for detecting both stage I (47.4%) and I/II (50%) pancreatic cancers were higher than those of CA19-9 (36.8% and 46.7%, respectively). The combination of apoA2-ATQ/AT (cutoff, 59.5 μg/mL) and CA19-9 (37 U/mL) increased the sensitivity for pancreatic cancer to 87.7% compared with 69.8% for CA19-9 alone. The clinical performance of apoA2-is was blindly confirmed by the National Cancer Institute Early Detection Research Network.

CONCLUSIONS

The clinical performance of ApoA2-ATQ/AT as a blood biomarker is equivalent to or better than that of CA19-9.

A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With Acute Pancreatitis

OBJECTIVES

We aimed to develop and validate a prediction model as the first step in a sequential screening strategy to identify acute pancreatitis (AP) individuals at risk for pancreatic cancer (PC).

MATERIALS AND METHODS

We performed a population-based retrospective cohort study among individuals 40 years or older with a hospitalization for AP in the US Veterans Health Administration. For variable selection, we used least absolute shrinkage and selection operator regression with 10-fold cross-validation to identify a parsimonious logistic regression model for predicting the outcome, PC diagnosed within 2 years after AP. We evaluated model discrimination and calibration.

RESULTS

Among 51,613 eligible study patients with AP, 801 individuals were diagnosed with PC within 2 years. The final model (area under the receiver operating curve, 0.70; 95% confidence interval, 0.67-0.73) included histories of gallstones, pancreatic cyst, alcohol use, smoking, and levels of bilirubin, triglycerides, alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, and albumin. If the predicted risk threshold was set at 2% over 2 years, 20.3% of the AP population would undergo definitive screening, identifying nearly 50% of PC associated with AP.

CONCLUSIONS

We developed a prediction model using widely available clinical factors to identify high-risk patients with PC-associated AP, the first step in a sequential screening strategy.

Simulating the healthcare workforce impact and capacity for pancreatic cancer care in Victoria: a model-based analysis

BACKGROUND

The incidence of pancreatic cancer is rising. With improvements in knowledge for screening and early detection, earlier detection of pancreatic cancer will continue to be more common. To support workforce planning, our aim is to perform a model-based analysis that simulates the potential impact on the healthcare workforce, assuming an earlier diagnosis of pancreatic cancer.

METHODS

We developed a simulation model to estimate the demand (i.e. new cases of pancreatic cancer) and supply (i.e. the healthcare workforce including general surgeons, medical oncologists, radiation oncologists, pain medicine physicians, and palliative care physicians) between 2023 and 2027 in Victoria, Australia. The model compares the current scenario to one in which pancreatic cancer is diagnosed at an earlier stage. The incidence of pancreatic cancer in Victoria, five-year survival rates, and Victoria's population size were obtained from Victorian Cancer Registry, Cancer Council NSW, and Australian Bureau of Statistics respectively. The healthcare workforce data were sourced from the Australian Government Department of Health and Aged Care's Health Workforce Data. The model was constructed at the remoteness level. We analysed the new cases and the number of healthcare workforce by profession together to assess the impact on the healthcare workforce.

RESULTS

In the status quo, over the next five years, there will be 198 to 220 stages I-II, 297 to 330 stage III, and 495 to 550 stage IV pancreatic cancer cases diagnosed annually, respectively. Assuming 20-70% of the shift towards pancreatic cancer's earlier diagnosis (shifting from stage IV to stages I-II pancreatic cancer within one year), the stages I-II cases could increase to 351 to 390 or 598 to 665 per year. The shift to early diagnosis led to substantial survival gains, translating into an additional 284 or 795 out of 5246 patients with pancreatic cancer remaining alive up to year 5 post-diagnosis. Workforce supply decreases significantly by the remoteness levels, and remote areas face a shortage of key medical professionals registered in delivering pancreatic cancer care, suggesting travel necessities by patients or clinicians.

CONCLUSION

Improving the early detection and diagnosis of pancreatic cancer is expected to bring significant survival benefits, although there are workforce distribution imbalances in Victoria that may affect the ability to achieve the anticipated survival gain.

Emergency Presentations Predict Worse Outcomes Among Patients with Pancreatic Cancer

BACKGROUND

Emergency presentation (EP) of cancer, a new cancer diagnosis made following an emergency department (ED) visit, is associated with worse patient outcomes and greater organizational stress on healthcare systems. Pancreatic cancer has the highest rate of EPs among European studies but remains understudied in the U.S.

AIMS

To evaluate the association between pancreatic cancer EPs and cancer stage, treatment, and survival.

METHODS

We conducted a retrospective cohort study among patients with pancreatic adenocarcinoma diagnosed from 2007 to 2019 at a tertiary-care Veterans Affairs medical center. Electronic health records were reviewed to identify EP cases, defined as a new pancreatic cancer diagnosis made within 30 days of an ED visit where cancer was suspected. We used multivariate logistic regression models and Cox proportional hazards models to examine the associations between EPs and cancer stage, treatment, and survival.

RESULTS

Of 243 pancreatic cancer patients, 66.7% had EPs. There was no difference in stage by EP status. However, patients diagnosed through EPs were 72% less likely to receive cancer treatment compared to non-emergency presenters (adjusted OR 0.28; 95% CI 0.13-0.57). Patients with EPs also had a 73% higher mortality risk (adjusted HR 1.73; 95% CI 1.29-2.34). This difference in mortality remained statistically significant after adjusting for cancer stage and receipt of cancer treatment (adjusted HR 1.47; 95% CI 1.09-1.99).

CONCLUSIONS

Pancreatic cancer EPs are common and independently associated with lower treatment rates and survival. Enhanced understanding of process breakdowns that lead to EPs can help identify care gaps and inform future quality improvement efforts.

Risk factors for early-onset pancreatic ductal adenocarcinoma: A systematic literature review

BACKGROUND

Emerging cancer trends suggest an increase in pancreatic cancer incidence in individuals younger than its typical age of onset, potentially reflecting changes in population exposures and lifestyles.

PATIENTS AND METHODS

We conducted a PRISMA-standard systematic literature review to identify non-heritable risk factors for early-onset pancreatic ductal adenocarcinoma (PDAC) (PROSPERO number: CRD42022299397). Systematic searches of MEDLINE and Embase bibliographic databases were performed (January 2022), and publications were screened against predetermined eligibility criteria; data were extracted using standardised data fields. The STROBE checklist was used to assess the completeness of reporting as a proxy for publication quality. Data were categorised by risk factor and analysed descriptively.

RESULTS

In total, 24 publications were included. All publications reported observational study data; thresholds for age group comparisons ranged between 40 and 65 years. Lifestyle factors investigated included smoking, alcohol consumption, obesity, physical inactivity, meat intake, socioeconomic status and geographical residence. Clinical factors investigated included pancreatitis, diabetes/insulin resistance, prior cancer and cancer stage at diagnosis, hepatitis B infection, metabolic syndrome and long-term proton pump inhibitor exposure. Publication STROBE scores were 6-21 (maximum, 22). Eight studies reported results adjusted for confounders. Potential non-heritable risk factors for early-onset PDAC that warrant further investigation included smoking, alcohol consumption, pancreatitis and hepatitis B infection.

CONCLUSION

Evidence for non-heritable risk factors for early-onset PDAC is heterogeneous, but four factors were identified that might aid the identification of at-risk individuals who may benefit from screening and risk reduction strategies.

Contributions of the Microbiome-Derived Metabolome for Risk Assessment and Prognostication of Pancreatic Cancer

BACKGROUND

Increasing evidence implicates microbiome involvement in the development and progression of pancreatic ductal adenocarcinoma (PDAC). Studies suggest that reflux of gut or oral microbiota can lead to colonization in the pancreas, resulting in dysbiosis that culminates in release of microbial toxins and metabolites that potentiate an inflammatory response and increase susceptibility to PDAC. Moreover, microbe-derived metabolites can exert direct effector functions on precursors and cancer cells, as well as other cell types, to either promote or attenuate tumor development and modulate treatment response.

CONTENT

The occurrence of microbial metabolites in biofluids thereby enables risk assessment and prognostication of PDAC, as well as having potential for design of interception strategies. In this review, we first highlight the relevance of the microbiome for progression of precancerous lesions in the pancreas and, using liquid chromatography-mass spectrometry, provide supporting evidence that microbe-derived metabolites manifest in pancreatic cystic fluid and are associated with malignant progression of intraductal papillary mucinous neoplasm(s). We secondly summarize the biomarker potential of microbe-derived metabolite signatures for (a) identifying individuals at high risk of developing or harboring PDAC and (b) predicting response to treatment and disease outcomes.

SUMMARY

The microbiome-derived metabolome holds considerable promise for risk assessment and prognostication of PDAC.

Next-generation therapies for pancreatic cancer

INTRODUCTION

Pancreas ductal adenocarcinoma (PDAC) is a frequently lethal malignancy that poses unique therapeutic challenges. The current mainstay of therapy for metastatic PDAC (mPDAC) is cytotoxic chemotherapy. NALIRIFOX (liposomal irinotecan, fluorouracil, leucovorin, oxaliplatin) is an emerging standard of care in the metastatic setting. An evolving understanding of PDAC pathogenesis is driving a shift toward targeted therapy. Olaparib, a poly-ADP-ribose polymerase (PARP) inhibitor, has regulatory approval for maintenance therapy in BRCA-mutated mPDAC along with other targeted agents receiving disease-agnostic approvals including for PDAC with rare fusions and mismatch repair deficiency. Ongoing research continues to identify and evaluate an expanding array of targeted therapies for PDAC.

AREAS COVERED

This review provides a brief overview of standard therapies for PDAC and an emphasis on current and emerging targeted therapies.

EXPERT OPINION

There is notable potential for targeted therapies for KRAS-mutated PDAC with opportunity for meaningful benefit for a sizable portion of patients with this disease. Further, emerging approaches are focused on novel immune, tumor microenvironment, and synthetic lethality strategies.

Cytotoxic Impact of Naringenin-Loaded Solid Lipid Nanoparticles on RIN5F Pancreatic β Cells via Autophagy Blockage

BACKGROUND

Autophagy plays a crucial role in modulating the proliferation of cancer diseases. However, the application of Naringenin (Nar), a compound with potential benefits against these diseases, has been limited due to its poor solubility and bioavailability.

OBJECTIVE

This study aimed to develop solid lipid nanoparticles (Nar-SLNs) loaded with Nar to enhance their therapeutic impact.

METHODS

In vitro experiments using Rin-5F cells exposed to Nar and Nar-SLNs were carried out to investigate the protective effects of Nar and its nanoformulation against the pancreatic cancer cell line of Rin-5F.

RESULTS

Treatment with Nar and Nar-SLN led to an increase in autophagic markers (Akt, LC3, Beclin1, and ATG genes) and a decrease in the level of miR-21. Both Nar and Nar-SLN treatments inhibited cell proliferation and reduced the expression of autophagic markers. Notably, Nar-SLNs exhibited greater efficacy compared to free Nar.

CONCLUSION

These findings suggest that SLNs effectively enhance the cytotoxic impact of Nar, making Nar-SLNs a promising candidate for suppressing or preventing Rin-5F cell growth.

Association of Glycated Hemoglobin With a Risk of Pancreatic Cancer in High-Risk Individuals Based on Genetic and Family History

INTRODUCTION

Screening for pancreatic cancer (PC) is suggested for high-risk individuals. Additional risk factors may enhance early detection in this population.

METHODS

Retrospective cohort study among patients with germline variants and/or familial pancreatic cancer in an integrated healthcare system between 2003 and 2019. We calculated the incidence rate (IR) by risk category and performed a nested case-control study to evaluate the relationship between HbA1C and PC within 3 years before diagnosis (cases) or match date (controls). Cases were matched 1:4 by age, sex, and timing of HbA1c. Logistic regression was performed to assess an independent association with PC.

RESULTS

We identified 5,931 high-risk individuals: 1,175(19.8%) familial PC, 45(0.8%) high-risk germline variants ( STK11, CDKN2A ), 4,097(69.1%) had other germline variants ( ATM, BRCA 1, BRCA 2, CASR, CDKN2A, CFTR, EPCAM, MLH1, MSH2, MSH6, PALB2, PRSS1, STK11, and TP53 ), and 614(10.4%) had both germline variants and family history. Sixty-eight patients (1.1%) developed PC; 50% were metastatic at diagnosis. High-risk variant was associated with greatest risk of PC, IR = 85.1(95% confidence interval: 36.7-197.6)/10,000 person-years; other germline variants and first-degree relative had IR = 33 (18.4, 59.3), whereas IR among ≥2 first-degree relative alone was 10.7 (6.1, 18.8). HbA1c was significantly higher among cases vs controls (median = 7.0% vs 6.4%, P = 0.02). In multivariable analysis, every 1% increase in HbA1c was associated with 36% increase in odds of PC (odds ratio 1.36, 95% confidence interval: 1.08-1.72). Pancreatitis was independently associated with a risk of PC (odds ratio 3.93, 95% confidence limit 1.19, 12.91).

DISCUSSION

Risk of PC varies among high-risk individuals. HbA1c and history of pancreatitis may be useful additional markers for early detection in this patient population.

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.

A pancreatic cancer risk prediction model (Prism) developed and validated on large-scale US clinical data

BACKGROUND

Pancreatic Duct Adenocarcinoma (PDAC) screening can enable early-stage disease detection and long-term survival. Current guidelines use inherited predisposition, with about 10% of PDAC cases eligible for screening. Using Electronic Health Record (EHR) data from a multi-institutional federated network, we developed and validated a PDAC RISk Model (Prism) for the general US population to extend early PDAC detection.

METHODS

Neural Network (PrismNN) and Logistic Regression (PrismLR) were developed using EHR data from 55 US Health Care Organisations (HCOs) to predict PDAC risk 6-18 months before diagnosis for patients 40 years or older. Model performance was assessed using Area Under the Curve (AUC) and calibration plots. Models were internal-externally validated by geographic location, race, and time. Simulated model deployment evaluated Standardised Incidence Ratio (SIR) and other metrics.

FINDINGS

With 35,387 PDAC cases, 1,500,081 controls, and 87 features per patient, PrismNN obtained a test AUC of 0.826 (95% CI: 0.824-0.828) (PrismLR: 0.800 (95% CI: 0.798-0.802)). PrismNN's average internal-external validation AUCs were 0.740 for locations, 0.828 for races, and 0.789 (95% CI: 0.762-0.816) for time. At SIR = 5.10 (exceeding the current screening inclusion threshold) in simulated model deployment, PrismNN sensitivity was 35.9% (specificity 95.3%).

INTERPRETATION

Prism models demonstrated good accuracy and generalizability across diverse populations. PrismNN could find 3.5 times more cases at comparable risk than current screening guidelines. The small number of features provided a basis for model interpretation. Integration with the federated network provided data from a large, heterogeneous patient population and a pathway to future clinical deployment.

FUNDING

Prevent Cancer Foundation, TriNetX, Boeing, DARPA, NSF, and Aarno Labs.

Are Tumor Marker Tests Applied Appropriately in Clinical Practice? A Healthcare Claims Data Analysis

Tumor markers (TM) are crucial in the monitoring of cancer treatment. However, inappropriate requests for screening reasons have a high risk of false positive and negative findings, which can lead to patient anxiety and unnecessary follow-up examinations. We aimed to assess the appropriateness of TM testing in outpatient practice in Switzerland. We conducted a retrospective cohort study based on healthcare claims data. Patients who had received at least one out of seven TM tests (CEA, CA19-9, CA125, CA15-3, CA72-4, Calcitonin, or NSE) between 2018 and 2021 were analyzed. Appropriate determinations were defined as a request with a corresponding cancer-related diagnosis or intervention. Appropriateness of TM determination by patient characteristics and prescriber specialty was estimated by using multivariate analyses. A total of 51,395 TM determinations in 36,537 patients were included. An amount of 41.6% of all TM were determined appropriately. General practitioners most often determined TM (44.3%) and had the lowest number of appropriate requests (27.8%). A strong predictor for appropriate determinations were requests by medical oncologists. A remarkable proportion of TM testing was performed inappropriately, particularly in the primary care setting. Our results suggest that a considerable proportion of the population is at risk for various harms associated with misinterpretations of TM test results.