RESUMO
Objective: To utilize radiomics analysis on dual-energy CT images of the pancreas to establish a quantitative imaging biomarker for type 2 diabetes mellitus. Materials and methods: In this retrospective study, 78 participants (45 with type 2 diabetes mellitus, 33 without) underwent a dual energy CT exam. Pancreas regions were segmented automatically using a deep learning algorithm. From these regions, radiomics features were extracted. Additionally, 24 clinical features were collected for each patient. Both radiomics and clinical features were then selected using the least absolute shrinkage and selection operator (LASSO) technique and then build classifies with random forest (RF), support vector machines (SVM) and Logistic. Three models were built: one using radiomics features, one using clinical features, and a combined model. Results: Seven radiomic features were selected from the segmented pancreas regions, while eight clinical features were chosen from a pool of 24 using the LASSO method. These features were used to build a combined model, and its performance was evaluated using five-fold cross-validation. The best classifier type is Logistic and the reported area under the curve (AUC) values on the test dataset were 0.887 (0.73-1), 0.881 (0.715-1), and 0.922 (0.804-1) for the respective models. Conclusion: Radiomics analysis of the pancreas on dual-energy CT images offers potential as a quantitative imaging biomarker in the detection of type 2 diabetes mellitus.
RESUMO
PURPOSE: To investigate the clinical value of measuring pancreatic fat fraction using dual-energy computed tomography (DECT) in association with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: This retrospective study included patients who underwent abdominal DECT between September 2021 and July 2022. The fat fractions in the head, body, and tail of the pancreas were calculated using fat maps generated from unenhanced DECT images, and CT values were measured at the same locations. The intraclass correlation coefficient (ICC) was used to analyze the reproducibility of measurements from two observers. Diagnostic performance was assessed using receiver operating characteristic curves. RESULTS: Seventy-eight patients, including 45 T2DM patients and 33 controls, were enrolled. The fat fractions of the pancreas were significantly higher in the T2DM group than in the control group (pancreatic head: 8.4 ± 6.3 % vs 5.1 ± 3.9 %; pancreatic body: 4.8 ± 4.0 % vs 2.7 ± 3.9 %; and pancreatic tail: 5.3 ± 3.2 % vs 2.7 ± 2.9 %, all p < 0.05). And the CT values of the pancreas were significantly lower in the T2DM group than in the control group (pancreatic head: 41.1 ± 8.5 HU vs 45.7 ± 4.6 HU; pancreatic body: 44.4 ± 5.0 HU vs 47.4 ± 3.7 HU; and pancreatic tail: 44.5 ± 5.0 HU vs 47.6 ± 3.2 HU, all p < 0.05). The fat fraction of the pancreatic tail was the best indicator for distinguishing T2DM patients from the controls (area under the curve: 0.716 (95 % CI: 0.601, 0.832), sensitivity: 64.4 % (95 % CI: 48.7 %, 77.7 %), and specificity: 78.8 % (95 % CI: 60.6 %, 90.4 %)). CONCLUSION: The DECT fat fractions of the pancreas could be a valuable additional parameter in the detection of T2DM.
