TY  - JOUR
AB  - RATIONALE AND OBJECTIVES: Type 2 diabetes is a known risk factor for vascular disease with an impact on the aorta. The aim of this study was to develop a deep learning framework for quantification of aortic phenotypes from magnetic resonance imaging (MRI) and to investigate the association between aortic features and impaired glucose metabolism beyond traditional cardiovascular (CV) risk factors. MATERIALS AND METHODS: This study used data from the prospective Cooperative Health Research in the Region of Augsburg (KORA) study to develop a deep learning framework for automatic quantification of aortic features (maximum aortic diameter, total volume, length, and width of the aortic arch) derived from MRI. Aortic features were compared between different states of glucose metabolism and tested for associations with impaired glucose metabolism adjusted for traditional CV risk factors (age, sex, height, weight, hypertension, smoking, and lipid panel). RESULTS: The deep learning framework yielded a high performance for aortic feature quantification with a Dice coefficient of 91.1±0.02. Of 381 participants (58% male, mean age 56 years), 231 (60.6%) had normal blood glucose, 97 (25.5%) had prediabetes, and 53 (13.9%) had diabetes. All aortic features showed a significant increase between different groups of glucose metabolism (p≤0.04). Total aortic length and total aortic volume were associated with impaired glucose metabolism (OR 0.85, 95%CI 0.74-0.96; p=0.01, and OR 0.99, 95%CI 0.98-0.99; p=0.02) independent of CV risk factors. CONCLUSION: Aortic features showed a glucose level dependent increase from normoglycemic individuals to those with prediabetes and diabetes. Total aortic length and volume were independently and inversely associated with impaired glucose metabolism beyond traditional CV risk factors.
AU  - Rau, A.*
AU  - Michel, L.J.*
AU  - Reisert, M.*
AU  - Rospleszcz, S.*
AU  - Russe, M.F.*
AU  - Schlesinger, S.*
AU  - Peters, A.
AU  - Bamberg, F.*
AU  - Schlett, C.L.*
AU  - Weiss, J.*
AU  - Taron, J.*
C1  - 73374
C2  - 57029
CY  - Ste 800, 230 Park Ave, New York, Ny 10169 Usa
SP  - 2509-2516
TI  - Association between aortic imaging features and impaired glucose metabolism: A deep learning population phenotyping approach.
JO  - Acad. Radiol.
VL  - 32
IS  - 5
PB  - Elsevier Science Inc
PY  - 2025
SN  - 1076-6332
ER  - 

TY  - JOUR
AB  - Rationale and Objectives: To investigate radiomics features of hepatic fat as potential biomarkers of type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) in individuals without overt cardiovascular disease, and benchmarking against hepatic proton density fat fraction (PDFF) and the body mass index (BMI). Materials and Methods: This study collected liver radiomics features of 310 individuals that were part of a case-controlled imaging substudy embedded in a prospective cohort. Individuals had known T2DM (n = 39; 12.6 %) and MetS (n = 107; 34.5 %) status, and were divided into stratified training (n = 232; 75 %) and validation (n = 78; 25 %) sets. Six hundred eighty-four MRI radiomics features were extracted for each liver volume of interest (VOI) on T1-weighted dual-echo Dixon relative fat water content (rfwc) maps. Test-retest and inter-rater variance was simulated by additionally extracting radiomics features using noise augmented rfwc maps and deformed volume of interests. One hundred and seventy-one features with test-retest reliability (ICC(1,1)) and inter-rater agreement (ICC(3,k)) of ≥0.85 on the training set were considered stable. To construct predictive random forest (RF) models, stable features were filtered using univariate RF analysis followed by sequential forward aggregation. The predictive performance was evaluated on the independent validation set with area under the curve of the receiver operating characteristic (AUROC) and balanced accuracy (AccuracyB). Results: On the validation set, the radiomics RF models predicted T2DM with AUROC of 0.835 and AccuracyB of 0.822 and MetS with AUROC of 0.838 and AccuracyB of 0.787, outperforming the RF models trained on the benchmark parameters PDFF and BMI. Conclusion: Hepatic radiomics features may serve as potential imaging biomarkers for T2DM and MetS.
AU  - Gutmann, D.A.P.*
AU  - Rospleszcz, S.
AU  - Rathmann, W.*
AU  - Schlett, C.L.*
AU  - Peters, A.
AU  - Wachinger, C.*
AU  - Gatidis, S.*
AU  - Bamberg, F.*
C1  - 59924
C2  - 49123
CY  - Ste 800, 230 Park Ave, New York, Ny 10169 Usa
SP  - S1-S10
TI  - MRI-derived radiomics features of hepatic fat predict metabolic states in individuals without cardiovascular disease.
JO  - Acad. Radiol.
VL  - 28
PB  - Elsevier Science Inc
PY  - 2020
SN  - 1076-6332
ER  -