Immune Checkpoint Inhibitor-Induced Pancreatic Injury: Clinical and Radiological Profile and Response to Steroids.

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.

New-onset diabetes is a predictive risk factor for pancreatic lesions in high-risk individuals: An observational cohort study.

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.

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.

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.

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.

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.

Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study.

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).

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.

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.

Pancreatic Cancer is More Frequently Early Stage at Diagnosis in Surgically Resected Intraductal Papillary Mucinous Neoplasms With Preoperative Surveillance.

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.

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.

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.

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.

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.

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.

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.

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.