Comparative effectiveness of immune-cell depletion and a targeted therapy against LTβR-signaling in the treatment of autoimmune pancreatitis

Abstract

s / Pancreatology 14 (2014) S1eS129 S18 Aims: In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. Materials&methods: HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the “ground truth”). After the expert defined the ground truth, the computer was able to develop a prediction rule which was then applied to a unique set of high resolution images in order to validate the process. Results: For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue areawith only a difference of 1% ± 0.05% (p1⁄40.21). Within regions of injured tissue, the software reported a difference of 2.5% ± 0.04% in acinar area compared with the pathologist (p1⁄40.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. Conclusion: The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas.