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.

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.

Management of Patients With Pancreatic Cancer Using the "Right Track" Model.

Pancreatic cancer is one of the few cancer types in the US with incidence and death rates continuing to rise. As the disease threatens to become the second leading cause of cancer-related deaths in the country, it is imperative to review the best practices currently available to extend and improve patient lives. To provide a roadmap for healthcare professionals detecting, diagnosing, and caring for patients with pancreatic cancer as a supplement to national guidelines focused on recommended treatment regimens, the Pancreatic Cancer Action Network (PanCAN)'s Scientific and Medical Affairs staff and expert Scientific and Medical Advisory Board have created a series of position statements. The statements are based upon scientific evidence and clinical observations published in the literature and research conducted through PanCAN's internal programs and initiatives. This review summarizes the rationale and sources for these position statements related to diagnosis, treatment, and care for pancreatic cancer and provides information about resources to make these recommendations accessible to patients and their medical teams. Pancreatic cancer is a complex and extremely challenging disease. Beyond treatment recommendations outlined in national guidelines, steps can be taken to help patients feel better and live longer. Under the framework of the "Right Track" model-right team, right tests, right treatments, data sharing-PanCAN's position statements can provide supplementary guidance to healthcare professionals for the short- and long-term management of patients with the disease.

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.

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.

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.

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.

Staging exocrine pancreatic dysfunction.

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.

Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists.

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.

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.

Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis.

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.

Understanding the Contribution of Insulin Resistance to the Risk of Pancreatic Cancer.

Type 2 diabetes is a known risk factor for the development of pancreatic adenocarcinoma. However, the mechanisms behind this epidemiological association remain unclear. Whether it is hyperglycemia or insulin resistance that increases the risk of pancreatic adenocarcinoma is a question that has yet to be settled. A new study by Kim et al published in The American Journal of Gastroenterology shows that the presence of insulin resistance independently increases pancreatic cancer mortality even in individuals without diabetes and hyperglycemia. The study's findings and implications to our understanding of pancreatic cancer risk are discussed.

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.

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.

Pancreatic cancer in patients with autoimmune pancreatitis: A scoping review.

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.

Impact of disconnected pancreatic duct syndrome on endoscopic ultrasound-guided drainage of pancreatic fluid collections.

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.

Potential Cost-Effectiveness of Risk-Based Pancreatic Cancer Screening in Patients With New-Onset Diabetes.

BACKGROUND: There are no established methods for pancreatic cancer (PAC) screening, but the NCI and the Pancreatic Cancer Action Network (PanCAN) are investigating risk-based screening strategies in patients with new-onset diabetes (NOD), a group with elevated PAC risk. Preliminary estimates of the cost-effectiveness of these strategies can provide insights about potential value and inform supplemental data collection. Using data from the Enriching New-Onset Diabetes for Pancreatic Cancer (END-PAC) risk model validation study, we assessed the potential value of CT screening for PAC in those determined to be at elevated risk, as is being done in a planned PanCAN Early Detection Initiative trial. METHODS: We created an integrated decision tree and Markov state-transition model to assess the cost-effectiveness of PAC screening in patients aged ≥50 years with NOD using CT imaging versus no screening. PAC prevalence, sensitivity, and specificity were derived from the END-PAC validation study. PAC stage distribution in the no-screening strategy and PAC survival were derived from the SEER program. Background mortality for patients with diabetes, screening and cancer care expenditure, and health state utilities were derived from the literature. Life-years (LYs), quality-adjusted LYs (QALYs), and costs were tracked over a lifetime horizon and discounted at 3% per year. Results are presented in 2020 US dollars, and we took a limited US healthcare perspective. RESULTS: In the base case, screening resulted in 0.0055 more LYs, 0.0045 more QALYs, and $293 in additional expenditures for a cost per QALY gained of $65,076. In probabilistic analyses, screening resulted in a cost per QALY gained of <$50,000 and <$100,000 in 34% and 99% of simulations, respectively. In the threshold analysis, >25% of screen-detected PAC cases needed to be resectable for the cost per QALY gained with screening to be <$100,000. CONCLUSIONS: We found that risk-based PAC screening in patients with NOD is likely to be cost-effective in the United States if even a modest fraction (>25%) of screen-detected patients with PAC are resectable. Future studies should reassess the value of this intervention once clinical trial data become available.

Validation of the Enriching New-Onset Diabetes for Pancreatic Cancer Model in a Diverse and Integrated Healthcare Setting.

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.

A pilot study of the implementation and evaluation of a leadership program for medical undergraduate students: Lessons learned.

BACKGROUND: Most Indian medical schools lack formal leadership training though students are expected to evolve into leaders. The Student Leadership Program (SLP) was designed and evaluated with an objective to incorporate and strengthen leadership skills in undergraduates and to initiate change in organizational practice through the development of a Student Leadership Society. METHODS: The SLP was designed using best evidence guidelines in medical education. Competencies and learning outcomes were identified in four domains: reflective writing, self-management, team management, and experiential learning. A stepwise program was implemented over 6 months in which participants wrote reflections at the end of each program session. So as to gain objective evidence of behavioral change in participants in relation to the leadership training, their reflections were qualitatively analyzed and corresponding codes and themes were derived. RESULTS: We describe the content and stepwise process of implementation of our pilot leadership program, which included 24 final-year students. Results of qualitative analysis are presented in relation to the domains of self-management, team management, and evaluation of experimental learning. Among the findings were: students viewed assertive skills training as the most powerful learning experience within self-management, and in team management, the session on "Myers-Briggs Type Indicator for understanding one's own leadership style" was seen as the most powerful learning tool, while the session on conflict management was the most difficult in this domain. A Student Leadership Society was instituted. DISCUSSION: In this study, students' reflections helped us better understand factors (the "how" and "why") that make leadership training more effective. The SLP, with a strong evidence base, achieved the intended learning outcomes. A Student Leadership Society was constituted as a networking platform to explore the long-term effects of leadership training on organizational practice. The content and process of our pilot leadership program and lessons learned through understanding of students' perspectives should be applicable to subsequent iterations of student leadership development programs here and in other settings.