Magnetic Resonance Imaging–Based Assessment of Pancreatic Fat Strongly Correlates With Histology-Based Assessment of Pancreas Composition

Abstract

Objective The aim of the study is to assess the relationship between magnetic resonance imaging (MRI)-based estimation of pancreatic fat and histology-based measurement of pancreatic composition. Materials and Methods In this retrospective study, MRI was used to noninvasively estimate pancreatic fat content in preoperative images from high-risk individuals and disease controls having normal pancreata. A deep learning algorithm was used to label 11 tissue components at micron resolution in subsequent pancreatectomy histology. A linear model was used to determine correlation between histologic tissue composition and MRI fat estimation. Results Twenty-seven patients (mean age 64.0 ± 12.0 years [standard deviation], 15 women) were evaluated. The fat content measured by MRI ranged from 0% to 36.9%. Intrapancreatic histologic tissue fat content ranged from 0.8% to 38.3%. MRI pancreatic fat estimation positively correlated with microanatomical composition of fat (r = 0.90, 0.83 to 0.95], P < 0.001); as well as with pancreatic cancer precursor (r = 0.65, P < 0.001); and collagen (r = 0.46, P < 0.001) content, and negatively correlated with pancreatic acinar (r = −0.85, P < 0.001) content. Conclusions Pancreatic fat content, measurable by MRI, correlates to acinar content, stromal content (fibrosis), and presence of neoplastic precursors of cancer.

3D histology reveals that immune response to pancreatic precancers is heterogeneous and depends on global pancreas structure

“…This data provides strong evidence derived from human tissue samples to support proposed links between inflammation and neoplastic initiation in the pancreas, specifically the existence of a positive feedback loop between precancer abundance and pancreatic inflammation. [41][42][43][44][45] The noted correlation between microscopic structures such as inflammation and PanINs to macroscopic structures such as pancreatic acinar lobules and stroma may explain the successes of works to non-invasively detect features such as pancreatic fibrosis, pancreatitis, lobulocentric atrophy, and PanINs using techniques such as MRI, CT, and EUS, [45][46][47][48][49][50][51][52] and suggests that further development of imaging-based screening for pancreatic inflammation and PanINs is warranted. Extension of this 3D analysis to detailed assessment of samples from individuals diagnosed with chronic pancreatitis remains an important area for future study.…”