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

  • Computed Tomography
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European Radiology Aims and scope Submit manuscript

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).

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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

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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).

Author information

Authors and Affiliations

  1. Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China

    Chun-Qiang Lu, Yuan-Cheng Wang, Xiang-Pan Meng, Chu-Hui Zeng, Weiwei Xu, Ya-Ting Gao & Shenghong Ju

  2. Lab of Image and Signal Processing, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China

    Hai-Tong Zhao

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  1. Chun-Qiang Lu
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Correspondence to Shenghong Ju.

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The scientific guarantor of this publication is Shenghong Ju.

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The authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Lu, CQ., Wang, YC., Meng, XP. et al. Diabetes risk assessment with imaging: a radiomics study of abdominal CT. Eur Radiol 29, 2233–2242 (2019). https://doi.org/10.1007/s00330-018-5865-5

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  • DOI: https://doi.org/10.1007/s00330-018-5865-5

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