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Diabetes risk assessment with imaging: a radiomics study of abdominal CT

  • Chun-Qiang Lu
  • Yuan-Cheng Wang
  • Xiang-Pan Meng
  • Hai-Tong Zhao
  • Chu-Hui Zeng
  • Weiwei Xu
  • Ya-Ting Gao
  • Shenghong Ju
Computed Tomography
  • 13 Downloads

Abstract

Objectives

To identify CT markers for screening of early type 2 diabetes and assessment of the risk of incident diabetes using a radiomics method.

Methods

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.

Results

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.

Conclusions

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.

Key Points

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).

Keywords

Diabetes mellitus Multidetector computed tomography Pancreas Adipose tissue 

Abbreviations

AAT

Abdominal adipose tissue

CI

Confidence interval

CT

Computed tomography

LASSO

Least absolute shrinkage and selection operator

MRI

Magnetic resonance imaging

ROC

Receiver-operating characteristic

ROI

Region of interest

SAT

Subcutaneous adipose tissue

VAT

Visceral adipose tissue

Notes

Acknowledgements

The authors acknowledge the technical assistance of Professor Shou-Hua Luo from the Image and Signal Processing Laboratory at the Southeast University.

Funding

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

Guarantor

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.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Supplementary material

330_2018_5865_MOESM1_ESM.docx (1.7 mb)
ESM 1 (DOCX 1775 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of RadiologyZhongda Hospital, Medical School of Southeast UniversityNanjingChina
  2. 2.Lab of Image and Signal Processing, School of Biological Science and Medical EngineeringSoutheast UniversityNanjingChina

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