Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We introduced a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (±7.2%, at case level) with CT, 85.0% (±7.9%) with T1 W MRI, and 86.3% (±6.4%) with T2 W MRI. There was a high correlation for pancreas volume prediction with R² of 0.91, 0.84, and 0.85 for CT, T1 W, and T2 W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1 W and T2 W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet.

Objective: Severe acute pancreatitis (SAP), pancreatic inflammation leading to multiorgan failure, is associated with high morbidity and mortality. There is a critical need to identify novel therapeutic strategies to improve clinical outcomes for SAP patients. Materials and Methods: A comprehensive literature review was performed to identify current clinical strategies, known molecular pathophysiology, and potential therapeutic targets for SAP. Results: Current clinical approaches focus on determining which patients will likely develop SAP. However, therapeutic options are limited to supportive care and fluid resuscitation. The application of a novel 5-cytokine panel accurately predicting disease outcomes in SAP suggests that molecular approaches will improve impact of future clinical trials in AP. Conclusions: Inflammatory outcomes in acute pancreatitis are driven by several unique molecular signals, which compound to promote both local and systemic inflammation. The identification of master cytokine regulators is critical to developing therapeutics, which reduce inflammation through several mechanisms.

Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention.

Colorectal cancer (CRC) is the third leading cause of cancer and second leading cause of cancer death in the United States. Recent evidence has linked a high fat and animal protein diet and microbial metabolism of host bile acids as environmental risk factors for CRC development. We hypothesize that the primary bile salt taurocholic acid (TCA) is a key, diet-controlled metabolite whose use by bacteria yields a carcinogen and tumor-promoter, respectively. The work is motivated by our published data indicating hydrogen sulfide (H2S) and secondary bile acid production by colonic bacteria, serve as environmental insults contributing to CRC risk. The central aim of this study is to test whether a diet high in animal protein and saturated fat increases abundance of bacteria that generate H2S and pro-inflammatory secondary bile acids in African Americans (AAs) at high risk for CRC. Our prospective, randomized, crossover feeding trial will examine two microbial mechanisms by which an animal-based diet may support the growth of TCA metabolizing bacteria. Each subject will receive two diets in a crossover design― an animal-based diet, rich in taurine and saturated fat, and a plant-based diet, low in taurine and saturated fat. A mediation model will be used to determine the extent to which diet (independent variable) and mucosal markers of CRC risk and DNA damage (dependent variables) are explained by colonic bacteria and their functions (mediator variables). This research will generate novel information targeted to develop effective dietary interventions that may reduce the unequal CRC burden in AAs.