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Background: Pancreatic cancer (PanC) presents at late stage with high mortality. Effective early detection methods are needed. Aberrantly methylated genes are unexplored as markers for noninvasive detection by stool testing. The authors aimed to select discriminant methylated genes and to assess accuracy of these and mutant KRAS in stool to detect PanC.Methods: Nine target genes were assayed by real-time methylation-specific polymerase chain reaction (MSP) in bisulfite-treated DNA from microdissected frozen specimens of 24 PanC cases and 30 normal colon controls. Archived stools from 58 PanC cases and 65 controls matched on sex, age, and smoking were analyzed. Target genes from fecal supernatants were enriched by hybrid capture, bisulfite-treated, and assayed by MSP. KRAS mutations were assayed using the QuARTS technique.Results: Areas under the receiver operating characteristics curves (AUCs) for tissue BMP3, NDRG4, EYA4, UCHL1, MDFI, Vimentin, CNTNAP2, SFRP2, and TFPI2 were 0.90, 0.79, 0.78, 0.78, 0.77, 0.77, 0.69, 0.67, and 0.66, respectively. The top 4 markers and mutant KRAS were evaluated in stool. BMP3 was the most discriminant methylation marker in stool. At 90% specificity, methylated BMP3 alone detected 51% of PanCs, mutant KRAS detected 50%, and combination detected 67%. AUCs for methylated BMP3, mutant KRAS, and combination in stool were 0.73, 0.75, and 0.85, respectively.Conclusions: This study demonstrates that stool assay of a methylated gene marker can detect PanC. Among candidate methylated markers discriminant in tissue, BMP3 alone performed well in stool. Combining methylated BMP3 and mutant KRAS increased stool detection over either marker alone. [ABSTRACT FROM AUTHOR]

Hepatobiliary and pancreatic cancers account for 4% of all cancers in the United States. Traditionally, these cancers have had a high mortality rate and have been poorly responsive to therapy. Because of a growing number of treatment options, patients are now living longer. For hepatocellular carcinoma, a broad number of treatment options are available, including surgery, ablation, embolization, systemic therapy, and liver transplantation. Treatment options for cholangiocarcinoma include surgery, systemic therapy, and liver transplantation. For pancreatic cancer, surgery, radiation, and systemic therapy all have potential roles. This review provides an updated summary of diagnosis and assessment together with treatment options for this group of cancers. [ABSTRACT FROM AUTHOR]

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., (©2024 American Association for Cancer Research.)

Small extracellular vesicles (sEVs) are cell-derived vesicles evolving as important elements involved in all stages of cancers. sEVs bear unique protein signatures that may serve as biomarkers. Pancreatic cancer (PC) records a very poor survival rate owing to its late diagnosis and several cancer cell-derived proteins have been reported as candidate biomarkers. However, given the pivotal role played by stellate cells (PSCs, which produce the collagenous stroma in PC), it is essential to also assess PSC-sEV cargo in biomarker discovery. Thus, this study aimed to isolate and characterise sEVs from mouse PC cells and PSCs cultured alone or as co-cultures and performed proteomic profiling and pathway analysis. Proteomics confirmed the enrichment of specific markers in the sEVs compared to their cells of origin as well as the proteins that are known to express in each of the culture types. Most importantly, for the first time it was revealed that PSC-sEVs are enriched in proteins (including G6PI, PGAM1, ENO1, ENO3, and LDHA) that mediate pathways related to development of diabetes, such as glucose metabolism and gluconeogenesis revealing a potential role of PSCs in pancreatic cancer-related diabetes (PCRD). PCRD is now considered a harbinger of PC and further research will enable to identify the role of these components in PCRD and may develop as novel candidate biomarkers of PC., (© 2024 The Authors. PROTEOMICS published by Wiley‐VCH GmbH.)

Background and Objectives: Pancreatic cancer (PC) is the third cause of cancer-related deaths. Early detection and interception of premalignant pancreatic lesions represent a promising strategy to improve outcomes. We evaluated risk factors of focal pancreatic lesions (FPLs) in asymptomatic individuals at hereditary high risk for PC., Methods: This is an observational single-institution cohort study conducted over a period of 5 years. Surveillance was performed through imaging studies (EUS or magnetic resonance imaging/magnetic resonance cholangiopancreatography) and serum biomarkers. We collected demographic characteristics and used univariate and multivariate logistic regression models to evaluate associations between potential risk factors and odd ratios (ORs) for FPL development., Results: A total of 205 patients completed baseline screening. Patients were followed up to 53 months. We detected FPL in 37 patients (18%) at baseline; 2 patients had lesions progression during follow-up period, 1 of them to PC. Furthermore, 13 patients developed new FPLs during the follow-up period. Univariate and multivariate analyses revealed that new-onset diabetes (NOD) is strongly associated with the presence of FPL (OR, 10.94 [95% confidence interval, 3.01-51.79; P < 0.001]; OR, 9.98 [95% confidence interval, 2.15-46.33; P = 0.003]). Follow-up data analysis revealed that NOD is also predictive of lesions progression or development of new lesions during screening (26.7% vs. 2.6%; P = 0.005)., Conclusions: In a PC high-risk cohort, NOD is significantly associated with presence of FPL at baseline and predictive of lesions progression or new lesions during surveillance., Competing Interests: A.M. receives royalties from Cosmos Wisdom Biotechnology and Thrive Earlier Detection, an Exact Sciences Company, and is a consultant for Freenome and Tezcat. F.M. is an SAB Member at Neological Biosciences. Dr Bhutani is Walter H Wriston Distinguished Professor for Pancreatic Cancer Research. Manoop S. Bhutani is a senior associate editor of the journal. This article was subject to the journal's standard procedures, with peer review handled independently of the editor and his research group., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc on behalf of Scholar Media Publishing.)

Background and Aims: Immune checkpoint inhibitor therapy causes numerous immune-related adverse events, including autoimmune pancreatic injury (AIPI), which results in rapid organ atrophy. We profiled the clinico-radiological features, short-term natural history, and response to steroids of AIPI., Methods: We retrospectively reviewed medical records of 229/11,165 (2.1%) adult patients with AIPI. One hundred and ten out of 229 (48%) had abdominal computerized tomography (CT) scan at lipase elevation; data of 110 without pancreatic metastases were analyzed. We analyzed serial CT-based pancreas volumetry data in 48 patients with AIPI (32 with normal CT and 16 with pancreatitis on CT at lipase elevation). We examined impact of steroids on pain and disease course., Results: In AIPI (n = 229), median lipase elevation was 4x upper limit of normal (range: 3-40x). The injury was more often asymptomatic than painful (143/229 (62%) vs 86/229 (38%), P < .000). Majority (83/110 (75%) had normal CT, often in painless vs painful disease: 51/57 (90%) vs 32/53 (60%), P < .001) 25% had interstitial pancreatitis. On serial pancreas volumetry, marked volume (cc) loss occurred 1 year after vs 3 months before lipase elevation in both normal CT (median 81.6 vs 61.3, P  = .00) and pancreatitis on CT groups (91.8 vs 60.5, P  = .00), ≥20% volume loss occurred in 47% vs 73%, respectively ( P  = .08). Steroids, when used did not mitigate pain, biochemical relapse, pancreas volume loss or 1-year diabetes incidence (7.2%)., Conclusion: Autoimmune pancreatic injury (AIPI) is uniquely characterized by painless lipase elevation, normal pancreas on CT and rapid pancreatic volume loss on follow-up. Steroids do not appear to have a role in management., (© 2024 The Authors.)

Background & Aims: The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multi-institutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs., Methods: A 3D-CNN classification system was trained using algorithmically generated bounding boxes and pancreatic masks on a curated data set of 696 portal phase diagnostic CTs with PDA and 1080 control images with a nonneoplastic pancreas. The model was evaluated on (1) an intramural hold-out test subset (409 CTs with PDA, 829 controls); (2) a simulated cohort with a case-control distribution that matched the risk of PDA in glycemically defined new-onset diabetes, and Enriching New-Onset Diabetes for Pancreatic Cancer score ≥3; (3) multi-institutional public data sets (194 CTs with PDA, 80 controls), and (4) a cohort of 100 prediagnostic CTs (i.e., CTs incidentally acquired 3-36 months before clinical diagnosis of PDA) without a focal mass, and 134 controls., Results: Of the CTs in the intramural test subset, 798 (64%) were from other hospitals. The model correctly classified 360 CTs (88%) with PDA and 783 control CTs (94%), with a mean accuracy 0.92 (95% CI, 0.91-0.94), area under the receiver operating characteristic (AUROC) curve of 0.97 (95% CI, 0.96-0.98), sensitivity of 0.88 (95% CI, 0.85-0.91), and specificity of 0.95 (95% CI, 0.93-0.96). Activation areas on heat maps overlapped with the tumor in 350 of 360 CTs (97%). Performance was high across tumor stages (sensitivity of 0.80, 0.87, 0.95, and 1.0 on T1 through T4 stages, respectively), comparable for hypodense vs isodense tumors (sensitivity: 0.90 vs 0.82), different age, sex, CT slice thicknesses, and vendors (all P > .05), and generalizable on both the simulated cohort (accuracy, 0.95 [95% 0.94-0.95]; AUROC curve, 0.97 [95% CI, 0.94-0.99]) and public data sets (accuracy, 0.86 [95% CI, 0.82-0.90]; AUROC curve, 0.90 [95% CI, 0.86-0.95]). Despite being exclusively trained on diagnostic CTs with larger tumors, the model could detect occult PDA on prediagnostic CTs (accuracy, 0.84 [95% CI, 0.79-0.88]; AUROC curve, 0.91 [95% CI, 0.86-0.94]; sensitivity, 0.75 [95% CI, 0.67-0.84]; and specificity, 0.90 [95% CI, 0.85-0.95]) at a median 475 days (range, 93-1082 days) before clinical diagnosis., Conclusions: This automated artificial intelligence model trained on a large and diverse data set shows high accuracy and generalizable performance for detection of PDA on diagnostic CTs as well as for visually occult PDA on prediagnostic CTs. Prospective validation with blood-based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk individuals., (Copyright © 2023 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Background and Aims: While type 2 diabetes is a well-known risk factor for pancreatic ductal adenocarcinoma (PDAC), PDAC-induced new-onset diabetes (PDAC-NOD) is a manifestation of underlying PDAC. In this study, we sought to identify potential blood-based biomarkers for distinguishing PDAC-NOD from type 2 diabetes (T2DM) without PDAC., Materials and Methods: By ELISA analysis, a migration signature biomarker panel comprising tissue factor pathway inhibitor (TFPI), tenascin C (TNC-FNIII-C) and CA 19-9 was analyzed in plasma samples from 50 PDAC-NOD and 50 T2DM controls., Results: Both TFPI (area under the curve (AUC) 0.71) and TNC-FNIII-C (AUC 0.69) outperformed CA 19-9 (AUC 0.60) in distinguishing all stages of PDAC-NOD from T2DM controls. The combined panel showed an AUC of 0.82 (95% CI = 0.73-0.90) (p = 0.002). In the PDAC-NOD early stage II samples, the three biomarkers had an AUC of 0.84 (95% CI = 0.73-0.93) vs CA 19-9, AUC = 0.60, (95% CI = 0.45-0.73), which also improved significance (p = 0.0123)., Conclusion: The migration signature panel adds significantly to CA 19-9 to discriminate PDAC-NOD from T2DM controls and warrants further validation for high-risk group stratification., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. S. Balasenthil, N. Chen and A.M. Killary hold United States Patent US 11,693,007 and Japanese Patent JP 7162398 for the early detection of pancreatic cancer. A. Maitra is a consultant for Freenome and Tezcat Biosciences. A. Maitra also holds a patent that has been licensed to Thrive Earlier Detection by John Hopkins University., (Copyright © 2023. Published by Elsevier B.V.)

Importance: Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cysts that can give rise to pancreatic cancer (PC). Limited population data exist on their prevalence, natural history, or risk of malignant transformation (IPMN-PC)., Objective: To fill knowledge gaps in epidemiology of IPMNs and associated PC risk by estimating population prevalence of IPMNs, associated PC risk, and proportion of IPMN-PC., Design, Setting, and Participants: : This retrospective cohort study was conducted in Olmsted County, Minnesota. Using the Rochester Epidemiology Project (REP), patients aged 50 years and older with abdominal computed tomography (CT) scans between 2000 and 2015 were randomly selected (CT cohort). All patients from the REP with PC between 2000 and 2019 were also selected (PC cohort). Data were analyzed from November 2021 through August 2023., Main Outcomes and Measures: CIs for PC incidence estimates were calculated using exact methods with the Poisson distribution. Cox models were used to estimate age, sex, and stage-adjusted hazard ratios for time-to-event end points., Results: The CT cohort included 2114 patients (1140 females [53.9%]; mean [SD] age, 68.6 [12.1] years). IPMNs were identified in 231 patients (10.9%; 95% CI, 9.7%-12.3%), most of which were branch duct (210 branch-duct [90.9%], 16 main-duct [6.9%], and 5 mixed [2.2%] IPMNs). There were 5 Fukuoka high-risk (F-HR) IPMNs (2.2%), 39 worrisome (F-W) IPMNs (16.9%), and 187 negative (F-N) IPMNs (81.0%). After a median (IQR) follow-up of 12.0 (8.1-15.3) years, 4 patients developed PC (2 patients in F-HR and 2 patients in F-N groups). The PC incidence rate per 100 person years for F-HR IPMNs was 34.06 incidents (95% CI, 4.12-123.02 incidents) and not significantly different for patients with F-N IPMNs compared with patients without IPMNs (0.16 patients; 95% CI, 0.02-0.57 patients vs 0.11 patients; 95% CI, 0.06-0.17 patients; P = .62). The PC cohort included 320 patients (155 females [48.4%]; mean [SD] age, 72.0 [12.3] years), and 9.8% (95% CI, 7.0%-13.7%) had IPMN-PC. Compared with 284 patients with non-IPMN PC, 31 patients with IPMN-PC were older (mean [SD] age, 76.9 [9.2] vs 71.3 [12.5] years; P = .02) and more likely to undergo surgical resection (14 patients [45.2%] vs 60 patients [21.1%]; P = .003) and more-frequently had nonmetastatic PC at diagnosis (20 patients [64.5%] vs 130 patients [46.8%]; P = .047). Patients with IPMN-PC had better survival (adjusted hazard ratio, 0.62; 95% CI, 0.40-0.94; P = .03) than patients with non-IPMN PC., Conclusions and Relevance: In this study, CTs identified IPMNs in approximately 10% of patients aged 50 years or older. PC risk in patients with F-N IPMNs was low and not different compared with patients without IPMNs; approximately 10% of patients with PC had IPMN-PC, and they had better survival compared with patients with non-IPMN PC.

Purpose of Review: Immune checkpoint inhibitors (ICI) have revolutionized cancer care and work primarily by blocking CTLA-4 (cytotoxic T-lymphocyte-associated protein 4), and/or PD-1 (programmed cell death protein 1), and/or PD-L1 (programmed death-ligand 1), thereby providing highly efficacious anti-tumor activity. However, this unmitigated immune response can also trigger immune related adverse events (irAEs) in multiple organs, with pancreatic irAEs (now referred to as type 3 Autoimmune pancreatitis (AIP) being infrequent., Recent Findings: Type 3 AIP is a drug-induced, immune mediated progressive inflammatory disease of the pancreas that may have variable clinical presentations viz., an asymptomatic pancreatic enzyme elevation, incidental imaging evidence of pancreatitis, painful pancreatitis, or any combination of these subtypes. Management is largely supportive with intravenous fluid hydration, pain control and holding the inciting medication. Steroids have not been shown to demonstrate a clear benefit in acute management. A rapid development pancreatic atrophy is observed on imaging as early as 1 year post initial injury. Type 3 AIP is a chronic inflammatory disease of the pancreas that though predominantly asymptomatic and mild in severity can lead to rapid organ volume loss regardless of type of clinical presentation and despite steroid therapy., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: To develop a bounding-box-based 3D convolutional neural network (CNN) for user-guided volumetric pancreas ductal adenocarcinoma (PDA) segmentation., Methods: Reference segmentations were obtained on CTs (2006-2020) of treatment-naïve PDA. Images were algorithmically cropped using a tumor-centered bounding box for training a 3D nnUNet-based-CNN. Three radiologists independently segmented tumors on test subset, which were combined with reference segmentations using STAPLE to derive composite segmentations. Generalizability was evaluated on Cancer Imaging Archive (TCIA) (n = 41) and Medical Segmentation Decathlon (MSD) (n = 152) datasets., Results: Total 1151 patients [667 males; age:65.3 ± 10.2 years; T1:34, T2:477, T3:237, T4:403; mean (range) tumor diameter:4.34 (1.1-12.6)-cm] were randomly divided between training/validation (n = 921) and test subsets (n = 230; 75% from other institutions). Model had a high DSC (mean ± SD) against reference segmentations (0.84 ± 0.06), which was comparable to its DSC against composite segmentations (0.84 ± 0.11, p = 0.52). Model-predicted versus reference tumor volumes were comparable (mean ± SD) (29.1 ± 42.2-cc versus 27.1 ± 32.9-cc, p = 0.69, CCC = 0.93). Inter-reader variability was high (mean DSC 0.69 ± 0.16), especially for smaller and isodense tumors. Conversely, model's high performance was comparable between tumor stages, volumes and densities (p > 0.05). Model was resilient to different tumor locations, status of pancreatic/biliary ducts, pancreatic atrophy, CT vendors and slice thicknesses, as well as to the epicenter and dimensions of the bounding-box (p > 0.05). Performance was generalizable on MSD (DSC:0.82 ± 0.06) and TCIA datasets (DSC:0.84 ± 0.08)., Conclusion: A computationally efficient bounding box-based AI model developed on a large and diverse dataset shows high accuracy, generalizability, and robustness to clinically encountered variations for user-guided volumetric PDA segmentation including for small and isodense tumors., Clinical Relevance: AI-driven bounding box-based user-guided PDA segmentation offers a discovery tool for image-based multi-omics models for applications such as risk-stratification, treatment response assessment, and prognostication, which are urgently needed to customize treatment strategies to the unique biological profile of each patient's tumor., Competing Interests: Declaration of competing interest None of the authors have conflicts of interest., (Copyright © 2023 IAP and EPC. Published by Elsevier B.V. All rights reserved.)

Background: New-onset diabetes (NOD) has been suggested as an early indicator of pancreatic cancer. However, the definition of NOD by the American Diabetes Association requires 2 simultaneous or consecutive elevated glycemic measures. We aimed to apply a machine-learning approach using electronic health records to predict the risk in patients with recent-onset hyperglycemia., Materials and Methods: In this retrospective cohort study, health plan enrollees 50 to 84 years of age who had an elevated (6.5%+) glycated hemoglobin (HbA1c) tested in January 2010 to September 2018 with recent-onset hyperglycemia were identified. A total of 102 potential predictors were extracted. Ten imputation datasets were generated to handle missing data. The random survival forests approach was used to develop and validate risk models. Performance was evaluated by c -index, calibration plot, sensitivity, specificity, and positive predictive value., Results: The cohort consisted of 109,266 patients (mean age: 63.6 y). The 3-year incidence rate was 1.4 (95% confidence interval: 1.3-1.6)/1000 person-years of follow-up. The 3 models containing age, weight change in 1 year, HbA1c, and 1 of the 3 variables (HbA1c change in 1 y, HbA1c in the prior 6 mo, or HbA1c in the prior 18 mo) appeared most often out of the 50 training samples. The c -indexes were in the range of 0.81 to 0.82. The sensitivity, specificity, and positive predictive value in patients who had the top 20% of the predicted risks were 56% to 60%, 80%, and 2.5% to 2.6%, respectively., Conclusion: Targeting evaluation at the point of recent hyperglycemia based on elevated HbA1c could offer an opportunity to identify pancreatic cancer early and possibly impact survival in cancer patients., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)-enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability., Methods: Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected ("corrected CNN [c-CNN]" segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared., Results: Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation-derived volumes [76.6 cm 3 (20.2 cm 3 ), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%-87% [ P < 0.0001])., Conclusions: Convolutional neural network-enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Background & Aims: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study., Methods: Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale., Results: Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5)., Conclusions: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility., (Copyright © 2022 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Purpose: To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D)., Methods: Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset., Results: Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4]., Conclusion: Pancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Exocrine pancreatic insufficiency (EPI) is frequently seen in patients with pancreatic cancer (PDAC) and is thought to contribute to nutritional complications. While EPI can be pharmacologically temporized with pancreatic enzyme replacement therapy (PERT), there is lack of clear evidence informing its use in PDAC. Here we aim to survey pancreatic surgeons regarding their utilization of PERT in the management of EPI for PDAC., Methods: An online survey was distributed to the members of The Americas Hepato-Pancreato-Biliary Association (AHPBA) and The Pancreas Club., Results: 86.5% (180/208) of surgeons prescribe PERT for at least some resectable/borderline resectable PDAC cases. Only a minority of surgeons order investigations to confirm EPI before starting PERT (28.1%) or test for adequacy of therapy (28.3%). Few surgeons believe that PERT has an effect on overall survival (19.7%) or disease-free survival (6.25%) in PDAC., Conclusion: PERT is widely prescribed in patients with resectable/borderline resectable PDAC, but investigations establishing EPI and assessing PERT adequacy are underutilized. A substantial proportion of surgeons are unclear as to the effect of PERT on survival outcomes in PDAC. These data call for prospective studies to establish guidelines for optimal use of PERT and its effects on survival outcomes in PDAC., (Copyright © 2022 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.)

Purpose of Review: Type 3 auto-immune pancreatitis (AIP) is a rare immune-related adverse event (irAE) because of immune checkpoint inhibitor (ICI) therapy employed in the management of advanced malignancies. The evaluation and management of this disease entity is not well documented in the literature. We summarize the available information on the clinical profile, diagnosis, and treatment of this disorder., Recent Findings: ICI-pancreatic injury (ICI-PI) is a form of AIP, recently termed type 3 AIP, which may present as an asymptomatic lipase elevation or clinical pancreatitis, that is, abdominal pain and elevated lipase. CT findings of pancreatitis may be absent in some cases. Diagnosis is based on a temporal relationship to ICI exposure and the absence of other cause of pancreatitis. Combination ICIs increase the risk of type 3 AIP compared with ICI monotherapy. Though corticosteroids are used for ICIP, their role and benefit remain unclear to date. Holding immunotherapy carries the risk of progression of underlying cancer., Summary: ICI-PI is a unique form of AIP (type 3) with a distinct disease profile. The majority of patients with ICIPI are asymptomatic and steroid therapy has unclear benefits., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Background and Aims: Management of intraductal papillary mucinous neoplasms (IPMNs) relies on clinical and imaging features to select patients for either pancreatectomy or periodic image-based surveillance. We aimed to compare outcomes in patients with IPMNs who underwent surgery at diagnosis with those who underwent surgery after a period of surveillance and identify preoperative clinical and imaging features associated with advanced neoplasia., Methods: Patients with surgically resected IPMN (n = 450) were divided into 2 groups: "immediate surgery": resection within 6 months of IPMN detection, and "surveillance surgery": resection after surveillance >6 months. Survival was analyzed with Kaplan-Meier estimates and Cox proportional hazard models., Results: Pancreatic cancers in the surveillance surgery group (n = 135) was more frequently stage I compared with the immediate surgery group (9/13, 69.2% vs 41/110, 37.3%; P  = .027). Among Fukuoka "worrisome features," only main pancreatic duct dilation 5-9 mm (odds ratio [OR] = 3.12, 95% confidence interval [CI]: 1.72-5.68; P < .001) and serum CA 19-9≥ 35 U/mL (OR = 2.82, 95% CI: 1.31-6.06; P  = .008) were significantly associated with advanced neoplasia. In addition, smoking history was associated with increased risk of advanced neoplasia (OR = 2.05, 95% CI: 1.23-3.43). Occurrence of future cancer was 16-fold higher in IPMN with high-grade dysplasia when compared with low-grade dysplasia (hazard ratio: 16.5; 95% CI: 4.19-64.7)., Conclusion: Surveillance-detected pancreatic cancers in patients with IPMNs are more frequently stage I, and IPMN-HGD on surgical pathology is associated with significant risk of future pancreatic cancer. In addition to known "high-risk" features, main pancreatic duct dilation 5-9 mm, CA 19-9 elevation, and smoking history are significantly associated with advanced neoplasia., (© 2022 The Authors.)

Introduction: The aim of this study was to assess the feasibility of cross-sectional imaging for detection of pancreatic cancer (PDAC) in patients with new-onset hyperglycemia and diabetes (NOD)., Methods: We conducted a prospective pilot study from November 2018 to March 2020 within an integrated health system. Patients aged 50-85 years with newly elevated glycemic parameters without a history of diabetes were invited to complete a 3-phase contrast-enhanced computed tomography pancreas protocol scan while participating in the Prospective Study to Establish a NOD Cohort. Abnormal pancreatic findings, incidental extrapancreatic findings, and subsequent clinical evaluation were identified. Variability in clinical reporting between medical centers based on descriptors of pancreatic duct and parenchyma was assessed., Results: A total of 130 of 147 participants (88.4%) consented to imaging; 93 scans were completed (before COVID-19 stay-at-home order). The median age was 62.4 years (interquartile range 56.3-68.8), 37.6% women; Hispanic (39.8%), White (29.0%), Black (14.0%), and Asian (13.3%). One (1.1%) case of PDAC (stage IV) was diagnosed, 12 of 93 participants (12.9%) had additional pancreatic findings: 5 fatty infiltration, 3 cysts, 2 atrophy, 1 divisum, and 1 calcification. There were 57 extrapancreatic findings among 52 of 93 (56%) unique patients; 12 of 57 (21.1%) prompted clinical evaluation with 2 additional malignancies diagnosed (nonsmall cell lung and renal oncocytoma). Reports from 1 participating medical center more frequently provided description of pancreatic parenchyma and ducts (92.9% vs 18.4%), P < 0.0001., Discussion: High proportion of incidental findings and variability in clinical reports are challenges to be addressed for a successful NOD-based early detection strategy for PDAC., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Pancreatic ductal adenocarcinoma (PDAC) is the only leading cause of cancer death without an early detection strategy. In retrospective studies, 0.5-1% of subjects >50 years of age who newly develop biochemically-defined diabetes have been diagnosed with PDAC within 3 years of meeting new onset hyperglycemia and diabetes (NOD) criteria. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) algorithm further risk stratifies NOD subjects based on age and changes in weight and diabetes parameters. We present the methodology for the Early Detection Initiative (EDI), a randomized controlled trial of algorithm-based screening in patients with NOD for early detection of PDAC. We hypothesize that study interventions (risk stratification with ENDPAC and imaging with Computerized Tomography (CT) scan) in NOD will identify earlier stage PDAC. EDI uses a modified Zelen's design with post-randomization consent. Eligible subjects will be identified through passive surveillance of electronic medical records and eligible study participants randomized 1:1 to the Intervention or Observation arm. The sample size is 12,500 subjects. The ENDPAC score will be calculated only in those randomized to the Intervention arm, with 50% (n = 3125) expected to have a high ENDPAC score. Consenting subjects in the high ENDPAC group will undergo CT imaging for PDAC detection and an estimate of potential harm. The effectiveness and efficacy evaluation will compare proportions of late stage PDAC between Intervention and Observation arm per randomization assignment or per protocol, respectively, with a planned interim analysis. The study is designed to improve the detection of sporadic PDAC when surgical intervention is possible., (Copyright © 2021. Published by Elsevier Inc.)

Digestive capacity of the gastrointestinal tract, largely but not wholly, depends on exocrine pancreatic function to achieve near complete digestion and absorption of ingested food. Coefficient of fat absorption (CFA), the proportion of ingested fat absorbed (normal >93%), reflects digestive capacity. Exocrine pancreatic insufficiency (EPI) is the state of insufficient digestive capacity (CFA <93%) caused by severe loss of pancreatic exocrine function despite variable compensation by upregulation of extra-pancreatic lipolysis. Fecal elastase 1 (FE1) level is the most widely used, though imperfect, non-invasive test of pancreatic enzyme output. Decline in pancreas enzyme output, or pancreatic exocrine dysfunction (EPD), has a variable correlation with measurable decline in CFA. EPI results in steatorrhea, weight loss and nutrient deficiency, which are mitigated by pancreatic enzyme replacement therapy (PERT). We propose a staging system for EPD, based on measurement of fecal elastase (FE1) and, if necessary, CFA and serum fat-soluble vitamin levels. In Stage I (Mild) EPD, FE1 is 100-200 mcg/gm; if steatorrhea is present, non-pancreatic causes are likely. In Stage II (Moderate) EPD), FE1 is < 100 mcg/gm without clinical and/or laboratory evidence of steatorrhea. In Stage III, there are marked reductions in FE1 and CFA, but vitamin levels remain normal (Severe EPD or EPI without nutritional deficiency). In Stage IV all parameters are abnormal (Severe EPD or EPI with nutritional deficiency). EPD stages I and II are pancreas sufficient and PERT may not be the best or first approach in management of early-stage disease; it needs further study to determine clinical utility. The term EPI refers strictly to EPD Stages III and IV which should be treated with PERT, with Stage IV requiring micronutrient supplementation as well., (Copyright © 2021. Published by Elsevier B.V.)

Background & Aims: Increased intrapancreatic fat is associated with pancreatic diseases; however, there are no established objective diagnostic criteria for fatty pancreas. On non-contrast computed tomography (CT), adipose tissue shows negative Hounsfield Unit (HU) attenuations (-150 to -30 HU). Using whole organ segmentation on non-contrast CT, we aimed to describe whole gland pancreatic attenuation and establish 5th and 10th percentile thresholds across a spectrum of age and sex. Subsequently, we aimed to evaluate the association between low pancreatic HU and risk of pancreatic ductal adenocarcinoma (PDAC)., Methods: The whole pancreas was segmented in 19,456 images from 469 non-contrast CT scans. A convolutional neural network was trained to assist pancreas segmentation. Mean pancreatic HU, volume, and body composition metrics were calculated. The lower 5th and 10th percentile for mean pancreatic HU were identified, examining the association with age and sex. Pre-diagnostic CT scans from patients who later developed PDAC were compared to cancer-free controls., Results: Less than 5th percentile mean pancreatic HU was significantly associated with increase in BMI (OR 1.07; 1.03-1.11), visceral fat (OR 1.37; 1.15-1.64), total abdominal fat (OR 1.12; 1.03-1.22), and diabetes mellitus type 1 (OR 6.76; 1.68-27.28). Compared to controls, pre-diagnostic scans in PDAC cases had lower mean whole gland pancreatic HU (-0.2 vs 7.8, p = 0.026)., Conclusion: In this study, we report age and sex-specific distribution of pancreatic whole-gland CT attenuation. Compared to controls, mean whole gland pancreatic HU is significantly lower in the pre-diagnostic phase of PDAC., Competing Interests: Declaration of competing interest statement and disclosures As co-inventor on licensed technologies, S.M. could share potential future royalties to Mayo Clinic from Exact Sciences in accordance with institutional policy and oversight. V.C. provides consulting for Interpace Diagnostics and is a shareholder of Nevakar Corporation. S.V. contributes to UpToDate chapters. The other authors of this manuscript have no conflict of interest to declare., (Copyright © 2021. Published by Elsevier B.V.)

Pancreatic ductal adenocarcinoma (PDAC) is a devastating condition characterised by vague symptomatology and delayed diagnosis. About 30% of PDAC patients report a history of new onset diabetes, usually diagnosed within 3 years prior to the diagnosis of cancer. Thus, new onset diabetes, which is also known as pancreatic cancer-related diabetes (PCRD), could be a harbinger of PDAC. Diabetes is driven by progressive β cell loss/dysfunction and insulin resistance, two key features that are also found in PCRD. Experimental studies suggest that PDAC cell-derived exosomes carry factors that are detrimental to β cell function and insulin sensitivity. However, the role of stromal cells, particularly pancreatic stellate cells (PSCs), in the pathogenesis of PCRD is not known. PSCs are present around the earliest neoplastic lesions and around islets. Given that PSCs interact closely with cancer cells to drive cancer progression, it is possible that exosomal cargo from both cancer cells and PSCs plays a role in modulating β cell function and peripheral insulin resistance. Identification of such mediators may help elucidate the mechanisms of PCRD and aid early detection of PDAC. This paper discusses the concept of a novel role of PSCs in the pathogenesis of PCRD.

Objective: Quality gaps in medical imaging datasets lead to profound errors in experiments. Our objective was to characterize such quality gaps in public pancreas imaging datasets (PPIDs), to evaluate their impact on previously published studies, and to provide post-hoc labels and segmentations as a value-add for these PPIDs., Methods: We scored the available PPIDs on the medical imaging data readiness (MIDaR) scale, and evaluated for associated metadata, image quality, acquisition phase, etiology of pancreas lesion, sources of confounders, and biases. Studies utilizing these PPIDs were evaluated for awareness of and any impact of quality gaps on their results. Volumetric pancreatic adenocarcinoma (PDA) segmentations were performed for non-annotated CTs by a junior radiologist (R1) and reviewed by a senior radiologist (R3)., Results: We found three PPIDs with 560 CTs and six MRIs. NIH dataset of normal pancreas CTs (PCT) (n = 80 CTs) had optimal image quality and met MIDaR A criteria but parts of pancreas have been excluded in the provided segmentations. TCIA-PDA (n = 60 CTs; 6 MRIs) and MSD(n = 420 CTs) datasets categorized to MIDaR B due to incomplete annotations, limited metadata, and insufficient documentation. Substantial proportion of CTs from TCIA-PDA and MSD datasets were found unsuitable for AI due to biliary stents [TCIA-PDA:10 (17%); MSD:112 (27%)] or other factors (non-portal venous phase, suboptimal image quality, non-PDA etiology, or post-treatment status) [TCIA-PDA:5 (8.5%); MSD:156 (37.1%)]. These quality gaps were not accounted for in any of the 25 studies that have used these PPIDs (NIH-PCT:20; MSD:1; both: 4). PDA segmentations were done by R1 in 91 eligible CTs (TCIA-PDA:42; MSD:49). Of these, corrections were made by R3 in 16 CTs (18%) (TCIA-PDA:4; MSD:12) [mean (standard deviation) Dice: 0.72(0.21) and 0.63(0.23) respectively]., Conclusion: Substantial quality gaps, sources of bias, and high proportion of CTs unsuitable for AI characterize the available limited PPIDs. Published studies on these PPIDs do not account for these quality gaps. We complement these PPIDs through post-hoc labels and segmentations for public release on the TCIA portal. Collaborative efforts leading to large, well-curated PPIDs supported by adequate documentation are critically needed to translate the promise of AI to clinical practice., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 IAP and EPC. Published by Elsevier B.V. All rights reserved.)

Background: Chronic pancreatitis is a known risk factor of pancreatic cancer (PDAC). A similar association has been suggested but not demonstrated for autoimmune pancreatitis (AIP)., Objective: The aim of our study was to identify and analyse all published cases of AIP and PDAC co-occurrence, focusing on the interval between the diagnoses and the cancer site within the pancreas., Methods: Relevant studies were identified through automatic searches of the MEDLINE, EMBASE, Scopus, and Web of Science databases, and supplemented by manual checks of reference lists in all retrieved articles. Missing/unpublished data were obtained from the authors of relevant publications in the form of pre-prepared questionnaires., Results: A total of 45 cases of PDAC in AIP patients were identified, of which 12 were excluded from the analysis due to suspicions of duplicity or lack of sufficient data. Thirty-one patients (94%) had type 1 AIP. Synchronous occurrence of PDAC and AIP was reported in 11 patients (33%), metachronous in 22 patients (67%). In the metachronous group, the median period between diagnoses was 66.5 months (2-186) and a majority of cancers (86%) occurred more than two years after AIP diagnosis. In most patients (70%), the cancer originated in the part of the pancreas affected by AIP., Conclusions: In the literature, there are reports on numerous cases of PDAC in AIP patients. PDAC is more frequent in AIP type 1 patients, typically metachronous in character, and generally found in the part of the pancreas affected by AIP., (Copyright © 2021 IAP and EPC. All rights reserved.)

Abstract: The potential of artificial intelligence (AI) applied to clinical data from electronic health records (EHRs) to improve early detection for pancreatic and other cancers remains underexplored. The Kenner Family Research Fund, in collaboration with the Cancer Biomarker Research Group at the National Cancer Institute, organized the workshop entitled: "Early Detection of Pancreatic Cancer: Opportunities and Challenges in Utilizing Electronic Health Records (EHR)" in March 2021. The workshop included a select group of panelists with expertise in pancreatic cancer, EHR data mining, and AI-based modeling. This review article reflects the findings from the workshop and assesses the feasibility of AI-based data extraction and modeling applied to EHRs. It highlights the increasing role of data sharing networks and common data models in improving the secondary use of EHR data. Current efforts using EHR data for AI-based modeling to enhance early detection of pancreatic cancer show promise. Specific challenges (biology, limited data, standards, compatibility, legal, quality, AI chasm, incentives) are identified, with mitigation strategies summarized and next steps identified., Competing Interests: The authors declare no conflict of interest. S.T.C., B.F.F., V.L.W.G., W.A.H., and D.S.K. are Kenner Family Research Fund scientific advisors. A.E.G., B.J.K., and L.J.R. are Kenner Family Research Fund board members., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These limitations often result in delayed or failed diagnosis, which negatively impact patient management and outcomes. This study aimed to create an endoscopic ultrasound (EUS)-based convolutional neural network (CNN) model trained to differentiate AIP from PDAC, chronic pancreatitis (CP) and normal pancreas (NP), with sufficient performance to analyse EUS video in real time., Design: A database of still image and video data obtained from EUS examinations of cases of AIP, PDAC, CP and NP was used to develop a CNN. Occlusion heatmap analysis was used to identify sonographic features the CNN valued when differentiating AIP from PDAC., Results: From 583 patients (146 AIP, 292 PDAC, 72 CP and 73 NP), a total of 1 174 461 unique EUS images were extracted. For video data, the CNN processed 955 EUS frames per second and was: 99% sensitive, 98% specific for distinguishing AIP from NP; 94% sensitive, 71% specific for distinguishing AIP from CP; 90% sensitive, 93% specific for distinguishing AIP from PDAC; and 90% sensitive, 85% specific for distinguishing AIP from all studied conditions (ie, PDAC, CP and NP)., Conclusion: The developed EUS-CNN model accurately differentiated AIP from PDAC and benign pancreatic conditions, thereby offering the capability of earlier and more accurate diagnosis. Use of this model offers the potential for more timely and appropriate patient care and improved outcome., Competing Interests: Competing interests: PDP: is an independent researcher who was compensated by Mayo Clinic grant funds to participate in AI model research., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Biliary strictures caused by inflammation or fibrosis lead to jaundice and cholangitis which often make it difficult to distinguish malignant strictures. In cases when malignancy cannot be excluded, surgery is often performed. The concept of immunoglobulin G4 (IgG4)-related sclerosing cholangitis (SC) as a benign biliary stricture was recently proposed. The high prevalence of the disease in Asian countries has resulted in multiple diagnostic and treatment guidelines; however, there is need to formulate a standardized diagnostic strategy among various countries considering the utility, invasiveness, and cost-effectiveness. We evaluated accuracies of various diagnostic modalities for biliary strictures comparing pathology in the Delphi meetings which were held in Rochester, MN. The diagnostic utility for each modality was graded according to the experts, including gastroenterologists, endoscopists, radiologists, and pathologists from the United States and Japan. Diagnostic utility of 10 modalities, including serum IgG4 level, noninvasive imaging, endoscopic ultrasound, endoscopic retrograde cholangiopancreatography-related diagnostic procedures were advocated and the reasons were specified. Serum IgG4 level, noninvasive imaging, diagnostic endoscopic ultrasound and intraductal ultrasonography under endoscopic retrograde cholangiopancreatography were recognized as useful modalities for the diagnosis. The information in this article will aid in the diagnosis of biliary strictures particularly for distinguishing IgG4-SC from cholangiocarcinoma and/or primary SC., (© 2021 [Author/Employing Institution].)

Background: Endoscopic intervention for pancreatic fluid collections (PFCs) with disconnected pancreatic duct syndrome (DPDS) has been associated with failures and increased need for additional endoscopic and non-endoscopic interventions. The primary aim of this study was to determine the outcomes of endoscopic ultrasound (EUS)-guided transmural drainage of PFCs in patients with DPDS., Methods: In patients undergoing EUS-guided drainage of PFCs from January 2013 to January 2018, demographic profiles, procedural indications and details, adverse events, outcomes, and subsequent interventions were retrospectively collected. Overall treatment success was determined by PFC resolution on follow-up imaging or stent removal without recurrence., Results: EUS-guided drainage of PFCs was performed in 141 patients. DPDS was present in 57 of them (40 %) and walled-off necrosis was the most frequent type of PFC (55 %). DPDS was not associated with lower clinical success, increased number of repeat interventions, or increased time to PFC resolution. Patients with DPDS were more likely to be treated with permanent transmural plastic double-pigtail stents (odds ratio [OR] 6.4; 95 % confidence interval [CI] 2.5 - 16.5; P  < 0.001). However, when stents were removed, DPDS was associated with increased PFC recurrence after stent removal (OR 8.0; 95 %CI 1.2 - 381.8; P  = 0.04)., Conclusions: DPDS frequently occurs in patients with PFCs but does not negatively impact successful resolution. DPDS is associated with increased PFC recurrence after stent removal., Competing Interests: Dr. Abu Dayyeh is consultant for Metamodix, BFKW, DyaMx, Boston Scientific, USGI medical, and Endo-TAGSS. He received research support from Apollo Endosurgery, USGI, Spatz Medical, Boston Scientific, GI Dynamics, Cairn Diagnostics, Aspire Bariatrics, and Medtronic. He served as a speaker for Johnson and Johnson, Endogastric Solutions, and Olympus. Bret T Petersen: 3M: Stock Shareholder; Abbvie: Stock Shareholder; Boston Scientific: Grant/Research Support, Consulting; Exact Sciences: Stock Shareholder; GIE Medical: Consulting; Johnson & Johnson: Stock Shareholder; Medtronic: Stock Shareholder; Olympus America: Consulting. Andrew C Storm: Apollo Endosurgery: Consulting; Boston Scientific: Grant/Research Support; Endo-TAGSS: Grant/Research Support; Enterasense: Grant/Research Support; GI Dynamics: Consulting. Santhi S Vege: Uptodate: Royalties; vical: Consulting. Vinay Chandrasekhara – Consultant, Interpace Diagnostics. Shareholder, Nevakar Corporation. The other authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Purpose: We have previously identified tissue methylated DNA markers (MDMs) associated with pancreatic ductal adenocarcinoma (PDAC). In this case-control study, we aimed to assess the diagnostic performance of plasma MDMs for PDAC., Experimental Design: Thirteen MDMs ( GRIN2D, CD1D, ZNF781, FER1L4, RYR2, CLEC11A, AK055957, LRRC4, GH05J042948, HOXA1, PRKCB, SHISA9 , and NTRK3 ) were identified on the basis of selection criteria applied to results of prior tissue experiments and assays were optimized in plasma. Next, 340 plasma samples (170 PDAC cases and 170 controls) were assayed using target enrichment long-probe quantitative amplified signal method. Initially, 120 advanced-stage PDAC cases and 120 healthy controls were used to train a prediction algorithm at 97.5% specificity using random forest modeling. Subsequently, the locked algorithm derived from the training set was applied to an independent blinded test set of 50 early-stage PDAC cases and 50 controls. Finally, data from all 340 patients were combined, and cross-validated., Results: The cross-validated area under the receiver operating characteristic curve (AUC) for the training set was 0.93 (0.89-0.96) for the MDM panel alone, 0.91 (95% confidence interval, 0.87-0.96) for carbohydrate antigen 19-9 (CA19-9) alone, and 0.99 (0.98-1) for the combined MDM-CA19-9 panel. In the test set of early-stage PDAC, the AUC for MDMs alone was 0.84 (0.76-0.92), CA19-9 alone was 0.87 (0.79-0.94), and combined MDM-CA19-9 panel was 0.90 (0.84-0.97) significantly better compared with either MDMs alone or CA19-9 alone ( P = 0.0382 and 0.0490, respectively). At a preset specificity of 97.5%, the sensitivity for the combined panel in the test set was 80% (28%-99%) for stage I disease and 82% (68%-92%) for stage II disease. Using the combined datasets, the cross-validated AUC was 0.9 (0.86-0.94) for the MDM panel alone and 0.89 for CA19-9 alone (0.84-0.93) versus 0.97 (0.94-0.99) for the combined MDM-CA19-9 panel ( P ≤ 0.0001). Overall, cross-validated sensitivity of MDM-CA19-9 panel was 92% (83%-98%), with an observed specificity of 92% at the preset specificity of 97.5%., Conclusions: Plasma MDMs in combination with CA19-9 detect PDAC with significantly higher accuracy compared with either biomarker individually., (©2021 American Association for Cancer Research.)

Purpose: To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra-reader and inter-reader reliability at full dose and reduced radiation dose CTs on a public dataset., Methods: A dataset of 1994 abdomen CT scans (portal venous phase, slice thickness ≤ 3.75-mm, multiple CT vendors) was curated by two radiologists (R1 and R2) to exclude cases with pancreatic pathology, suboptimal image quality, and image artifacts (n = 77). Remaining 1917 CTs were equally allocated between R1 and R2 for volumetric pancreas segmentation [ground truth (GT)]. This internal dataset was randomly divided into training (n = 1380), validation (n = 248), and test (n = 289) sets for the development of a two-stage 3D CNN model based on a modified U-net architecture for automated volumetric pancreas segmentation. Model's performance for pancreas segmentation and the differences in model-predicted pancreatic volumes vs GT volumes were compared on the test set. Subsequently, an external dataset from The Cancer Imaging Archive (TCIA) that had CT scans acquired at standard radiation dose and same scans reconstructed at a simulated 25% radiation dose was curated (n = 41). Volumetric pancreas segmentation was done on this TCIA dataset by R1 and R2 independently on the full dose and then at the reduced radiation dose CT images. Intra-reader and inter-reader reliability, model's segmentation performance, and reliability between model-predicted pancreatic volumes at full vs reduced dose were measured. Finally, model's performance was tested on the benchmarking National Institute of Health (NIH)-Pancreas CT (PCT) dataset., Results: Three-dimensional CNN had mean (SD) Dice similarity coefficient (DSC): 0.91 (0.03) and average Hausdorff distance of 0.15 (0.09) mm on the test set. Model's performance was equivalent between males and females (P = 0.08) and across different CT slice thicknesses (P > 0.05) based on noninferiority statistical testing. There was no difference in model-predicted and GT pancreatic volumes [mean predicted volume 99 cc (31cc); GT volume 101 cc (33 cc), P = 0.33]. Mean pancreatic volume difference was -2.7 cc (percent difference: -2.4% of GT volume) with excellent correlation between model-predicted and GT volumes [concordance correlation coefficient (CCC)=0.97]. In the external TCIA dataset, the model had higher reliability than R1 and R2 on full vs reduced dose CT scans [model mean (SD) DSC: 0.96 (0.02), CCC = 0.995 vs R1 DSC: 0.83 (0.07), CCC = 0.89, and R2 DSC:0.87 (0.04), CCC = 0.97]. The DSC and volume concordance correlations for R1 vs R2 (inter-reader reliability) were 0.85 (0.07), CCC = 0.90 at full dose and 0.83 (0.07), CCC = 0.96 at reduced dose datasets. There was good reliability between model and R1 at both full and reduced dose CT [full dose: DSC: 0.81 (0.07), CCC = 0.83 and reduced dose DSC:0.81 (0.08), CCC = 0.87]. Likewise, there was good reliability between model and R2 at both full and reduced dose CT [full dose: DSC: 0.84 (0.05), CCC = 0.89 and reduced dose DSC:0.83(0.06), CCC = 0.89]. There was no difference in model-predicted and GT pancreatic volume in TCIA dataset (mean predicted volume 96 cc (33); GT pancreatic volume 89 cc (30), p = 0.31). Model had mean (SD) DSC: 0.89 (0.04) (minimum-maximum DSC: 0.79 -0.96) on the NIH-PCT dataset., Conclusion: A 3D CNN developed on the largest dataset of CTs is accurate for fully automated volumetric pancreas segmentation and is generalizable across a wide range of CT slice thicknesses, radiation dose, and patient gender. This 3D CNN offers a scalable tool to leverage biomarkers from pancreas morphometrics and radiomics for pancreatic diseases including for early pancreatic cancer detection., (© 2021 American Association of Physicists in Medicine.)

Abstract: Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders., Competing Interests: The authors declare no conflict of interest. S.T.C., B. F., V.L.W.G., W.H., and D.S.K. are Kenner Family Research Fund scientific advisors. A.G., B.K., and L.R. are Kenner Family Research Fund board members., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: The risk of pancreatic cancer is elevated among people with new-onset diabetes (NOD). Based on Rochester Epidemiology Project Data, the Enriching New-Onset Diabetes for Pancreatic Cancer (END-PAC) model was developed and validated., Aims: We validated the END-PAC model in a cohort of patients with NOD using retrospectively collected data from a large integrated health maintenance organization., Methods: A retrospective cohort of patients between 50 and 84 years of age meeting the criteria for NOD in 2010-2014 was identified. Each patient was assigned a risk score (< 1: low risk; 1-2: intermediate risk; ≥ 3: high risk) based on the values of the predictors specified in the END-PAC model. Patients who developed pancreatic ductal adenocarcinoma (PDAC) within 3 years were identified using the Cancer Registry and California State Death files. Area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were estimated., Results: Out of the 13,947 NOD patients who were assigned a risk score, 99 developed PDAC in 3 years (0.7%). Of the 3038 patients who had a high risk, 62 (2.0%) developed PDAC in 3 years. The risk increased to 3.0% in white patients with a high risk. The AUC was 0.75. At the 3+ threshold, the sensitivity, specificity, PPV, and NPV were 62.6%, 78.5%, 2.0%, and 99.7%, respectively., Conclusions: It is critical that prediction models are validated before they are implemented in various populations and clinical settings. More efforts are needed to develop screening strategies most appropriate for patients with NOD in real-world settings.

Background and Objectives: No single optimal test reliably determines the pancreatic cyst subtype. Following EUS-FNA, the "string sign" test can differentiate mucinous from nonmucinous cysts. However, the interobserver variability of string sign results has not been studied., Methods: An experienced endosonographer performed EUS-FNA of pancreatic cysts on different patients and was recorded on video performing the string sign test for each. The videos were shared internationally with 14 experienced endosonographers, with a survey for each video: "Is the string sign positive?" and "If the string sign is positive, what is the length of the formed string?" Also asked "What is the cutoff length for string sign to be considered positive?" Interobserver variability was assessed using the kappa statistic (κ)., Results: A total of 112 observations were collected from 14 endosonographers. Regarding string sign test positivity, κ was 0.6 among 14 observers indicating good interrater agreement (P < 0.001) while κ was 0.38 when observers were compared to the index endosonographer demonstrating marginal agreement (P < 0.001). Among observations of the length of the string in positive samples, 89.8% showed >5 mm of variability (P < 0.001), indicating marked variability. There was poor agreement on the cutoff length for a string to be considered positive., Conclusion: String sign of pancreatic cysts has a good interobserver agreement regarding its positivity that can help in differentiating mucinous from nonmucinous pancreatic cysts. However, the agreement is poor on the measured length of the string and the cutoff length of the formed string to be considered a positive string sign., Competing Interests: None