Open AccessApplication of computerized 3D-CT texture analysis of pancreas for the assessment of patients with diabetes
Author(s) -
Siwon Jang,
Jung Hoon Kim,
Seo Youn Choi,
Sang Joon Park,
Joon Koo Han
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0227492
Subject(s) - sphericity , medicine , diabetes mellitus , receiver operating characteristic , type 2 diabetes , logistic regression , analysis of variance , multivariate statistics , mathematics , nuclear medicine , statistics , endocrinology , geometry
Objective To evaluate the role of computerized 3D CT texture analysis of the pancreas as quantitative parameters for assessing diabetes. Methods Among 2,493 patients with diabetes, 39 with type 2 diabetes (T2D) and 12 with type 1 diabetes (T1D) who underwent CT using two selected CT scanners, were enrolled. We compared these patients with age-, body mass index- (BMI), and CT scanner-matched normal subjects. Computerized texture analysis for entire pancreas was performed by extracting 17 variable features. A multivariate logistic regression analysis was performed to identify the predictive factors for diabetes. A receiver operator characteristic (ROC) curve was constructed to determine the optimal cut off values for statistically significant variables. Results In diabetes, mean attenuation, standard deviation, variance, entropy, homogeneity, surface area, sphericity, discrete compactness, gray-level co-occurrence matrix (GLCM) contrast, and GLCM entropy showed significant differences ( P < .05). Multivariate analysis revealed that a higher variance (adjusted OR, 1.002; P = .005), sphericity (adjusted OR, 1.649×10 4 ; P = .048), GLCM entropy (adjusted OR, 1.057×10 5 ; P = .032), and lower GLCM contrast (adjusted OR, 0.997; P < .001) were significant variables. The mean AUCs for each feature were 0.654, 0.689, 0.620, and 0.613, respectively ( P < .05). In subgroup analysis, only larger surface area (adjusted OR, 1.000; P = .025) was a significant predictor for T2D. Conclusions Computerized 3D CT texture analysis of the pancreas could be helpful for predicting diabetes. A higher variance, sphericity, GLCM entropy, and a lower GLCM contrast were the significant predictors for diabetes.