ArticlePloS one2020
Application of computerized 3D-CT texture analysis of pancreas for the assessment of patients with diabetes.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pancreas image mining: a systematic review of radiomics.European radiology · 2021Pooled it
- Quantitative CT-Derived Imaging Features Improve Preoperative Risk Stratification of Intraductal Papillary Mucinous Neoplasms.Annals of surgical oncology · 2026Article
- Article
- Effects of diabetic ketoacidosis, islet cell function, and age of onset on pancreas size, morphology, and exocrine function in children with type 1 diabetes: an abdominal MRI study.Journal of endocrinological investigation · 2025Article
- Multiparametric Magnetic Resonance Imaging Findings of the Pancreas: A Comparison in Patients with Type 1 and 2 Diabetes.Tomography (Ann Arbor, Mich.) · 2025Article
- Accurate screening of patients with diabetes based on physical examination: pancreas imaging features from chest CT and laboratory data.Frontiers in endocrinology · 2025Article
- Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography.Diagnostics (Basel, Switzerland) · 2024Article
- CT-based pancreatic radiomics predicts secondary loss of response to infliximab in biologically naïve patients with Crohn's disease.Insights into imaging · 2024Article
- Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach.PLOS digital health · 2024Article
- Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study.Abdominal radiology (New York) · 2022Article
- Imaging evaluation of the pancreas in diabetic patients.Abdominal radiology (New York) · 2022Review
- Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization.Frontiers in oncology · 2022Article
- Quantification of Pancreas Surface Lobularity on CT: A Feasibility Study in the Normal Pancreas.Korean journal of radiology · 2021Article
- Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists.Journal of computer assisted tomographyArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the role of computerized 3D CT texture analysis of the pancreas as quantitative parameters for assessing diabetes.
methodsAmong 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.
resultsIn 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, 1.649×104; P = .048), GLCM entropy (adjusted OR, 1.057×105; 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.
conclusionsComputerized 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.
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