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Distribution and Characteristics of Pancreatic Volume Using Computed Tomography VolumetryDistribution and Characteristics of Pancreatic Volume Using Computed Tomography Volumetry

Other Titles
Distribution and Characteristics of Pancreatic Volume Using Computed Tomography Volumetry
Authors
윤지현김광기김영재임상헌박연호김두진강희택이두호
Issue Date
Oct-2020
Publisher
대한의료정보학회
Keywords
Pancreas; Deep Learning; Body Mass Index (BMI); Sarcopenia; Computed Tomography (CT)
Citation
Healthcare Informatics Research, v.26, no.4, pp.321 - 327
Journal Title
Healthcare Informatics Research
Volume
26
Number
4
Start Page
321
End Page
327
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/79264
DOI
10.4258/hir.2020.26.4.321
ISSN
2093-3681
Abstract
Objectives: Changes in the pancreatic volume (PV) are useful as potential clinical markers for some pancreatic-related diseases. The objective of this study was to measure the volume of the pancreas using computed tomography (CT) volumetryand to evaluate the relationships between sex, age, body mass index (BMI), and sarcopenia. Methods: We retrospectivelyanalyzed the abdominal CT scans of 1,003 subjects whose ages ranged between 10 and 90 years. The pancreas was segmentedmanually to define the region of interest (ROI) based on CT images, and then the PVs were measured by counting the voxelsin all ROIs within the pancreas boundary. Sarcopenia was identified by examination of CT images that determined the crosssectionalarea of the skeletal muscle around the third lumbar vertebra. Results: The mean volume of the pancreas was 62.648± 19.094 cm3. The results indicated a negative correlation between the PV and age. There was a positive correlation betweenthe PV and BMI for both sexes, females, and males (r = 0.343, p < 0.001; r = 0.461, p < 0.001; and r = 0.244, p < 0.001, respectively). Additionally, there was a positive correlation between the PV and sarcopenia for females (r = 0.253, p < 0.001) andmales (r = 0.200, p < 0.001). Conclusions: CT pancreas volumetry results may help physicians follow up or predict conditionsof the pancreas after interventions for pancreatic-related disease in the future.
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