Proteomic profiling of small extracellular vesicles derived from mouse pancreatic cancer and stellate cells: Role in pancreatic cancer.

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.

Long-Term Outcomes and Risk of Pancreatic Cancer in Intraductal Papillary Mucinous Neoplasms.

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.

Immune Checkpoint Inhibitor-Induced (Type 3) Autoimmune Pancreatitis.

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.

Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans.

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.

Blood-based Migration Signature Biomarker Panel Discriminates Early Stage New Onset Diabetes related Pancreatic Ductal Adenocarcinoma from Type 2 Diabetes.

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.

Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs.

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.

Risk Prediction of Pancreatic Cancer in Patients With Recent-onset Hyperglycemia: A Machine-learning Approach.

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.

Type 3 autoimmune pancreatitis (immune checkpoint inhibitor-induced pancreatitis).

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.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.

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.

Prescription patterns of pancreatic enzyme replacement therapy for patients with pancreatic cancer in the United States.

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.

Imaging of the Pancreas in New-Onset Diabetes: A Prospective Pilot Study.

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.

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

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.

Early Detection of Pancreatic Cancer: Applying Artificial Intelligence to Electronic Health Records.

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.

Role of Pancreatic Stellate Cell-Derived Exosomes in Pancreatic Cancer-Related Diabetes: A Novel Hypothesis.

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 ß 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 ß 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 ß 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.

Determining age and sex-specific distribution of pancreatic whole-gland CT attenuation using artificial intelligence aided image segmentation: Associations with body composition and pancreatic cancer risk.

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.

Diagnosing Biliary Strictures: Distinguishing IgG4-Related Sclerosing Cholangitis From Cholangiocarcinoma and Primary Sclerosing Cholangitis.

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.

Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications.

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.

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review.

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.