Year: 2025
Type: article
Study design: Observational Study
Abstract: ABSTRACT Pancreas shape features, derived from 3D segmentation of the pancreas, might be relevant to identifying subtle disease‐related changes in the pancreas. This work utilized a retrospective sample of clinical contrast‐enhanced CT examinations of 140 children < 18 years of age. Manual segmentations of the pancreas were used to extract radiomic shape features and perform statistical shape modeling using a partial least squares regression approach. Age, weight, height, body surface area, and body mass index each showed significant associations with volume, surface area, surface area to volume ratio, compactness, major axis length, minor axis length, least axis length, and area density. Elastic net regression models showed that demographic variables had strong predictive power (cross‐validated r 2 0.71–0.89) for pancreas volume, surface area, surface area to volume ratio, major axis length, minor axis length, and least axis length. In the analysis of shape modes, only the first partial least squares regression component accounted for a statistically significant amount of the demographic variance among participants (24.8% variance explained, p < 0.001), with significant loadings for age ( p < 0.001), weight ( p = 0.001), and height ( p < 0.001). Our results are the first to characterize pancreas shape features in children with healthy pancreatic function and set the stage for future studies to understand how disease impacts pancreas shape, beyond global changes in volume, and how changes in shape might be used to diagnose and monitor disease.
Source: Clinical Anatomy
DOI: 10.1002/ca.70003
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Award: G953322400
OpenAlex ID: W4412040912