Diabetes risk assessment with imaging: a radiomics study of abdominal CT
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To identify CT markers for screening of early type 2 diabetes and assessment of the risk of incident diabetes using a radiomics method.
The medical records of 26,947 inpatients were reviewed. A total of 690 patients were selected and allocated to a primary cohort, a validation cohort, and a prediction cohort and used to build prediction models for diabetes. Three radiomics signatures were constructed using CT image features extracted from three regions of interest, i.e., in the pancreas, liver, and psoas major muscle. By incorporating radiomics signatures and other markers, we built a radiomics nomogram that could be used to screen for early diabetes and predict future diabetes.
Of the three abdominal organs for which radiomics signature were constructed, that of the pancreas showed the best discriminatory power for early diabetes screening and prediction (C-statistics of 0.833, 0.846, and 0.899 for the primary cohort, validation cohort, and prediction cohort, respectively). The sensitivity and specificity of the nomogram for prediction of 3-year incident diabetes were 0.827 and 0.807, respectively.
This study presents alternative radiomics markers that have potential for use in screening for undiagnosed type 2 diabetes and prediction of 3-year incident diabetes.
• CT images may provide useful information to evaluate the risk of developing diabetes.
• Radiomics score for diabetes prediction is based on subtle changes of abdominal organs detected by CT.
• The radiomics signature of pancreas, a combination of five features of CT images, is efficient for early diabetes screening and prediction of future diabetes (AUC > 0.8).
KeywordsDiabetes mellitus Multidetector computed tomography Pancreas Adipose tissue
Abdominal adipose tissue
Least absolute shrinkage and selection operator
Magnetic resonance imaging
Region of interest
Subcutaneous adipose tissue
Visceral adipose tissue
The authors acknowledge the technical assistance of Professor Shou-Hua Luo from the Image and Signal Processing Laboratory at the Southeast University.
This study has received funding by the National Nature Science Foundation of China (NSFC, No. 81525014), the Jiangsu Provincial Special Program of Medical Science (BL2013029), and the Key Research and Development Program of Jiangsu Province (BE2016782).
Compliance with ethical standards
The scientific guarantor of this publication is Shenghong Ju.
Conflict of interest
The authors declare that they have no conflict of interest.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• Diagnostic or prognostic study
• Performed at one institution
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