ArticleAbdominal radiology (New York)2022
Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study.
Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Quantitative pancreatic MRI in diabetes mellitus: current advances and future directions.Frontiers in endocrinology · 2026Review
- Pancreas segmentation using AI developed on the largest CT dataset with multi-institutional validation and implications for early cancer detection.Scientific reports · 2025Article
- Can AI Be Useful in the Early Detection of Pancreatic Cancer in Patients with New-Onset Diabetes?Biomedicines · 2025Review
- Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods.Abdominal radiology (New York) · 2024Article
- Radiomics analysis of pancreas based on dual-energy computed tomography for the detection of type 2 diabetes mellitus.Frontiers in medicine · 2024Article
- The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.Journal of Korean medical science · 2023Review
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeTo determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D).
methodsTotal 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset.
resultsModel correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4].
conclusionPancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually.
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