European Radiology

, Volume 29, Issue 5, pp 2293–2301 | Cite as

Evaluation of renal dysfunction using texture analysis based on DWI, BOLD, and susceptibility-weighted imaging

  • Jiule Ding
  • Zhaoyu Xing
  • Zhenxing Jiang
  • Hua Zhou
  • Jia Di
  • Jie Chen
  • Jianguo Qiu
  • Shengnan Yu
  • Liqiu Zou
  • Wei XingEmail author
Magnetic Resonance



To explore the value of texture analysis based on diffusion-weighted imaging (DWI), blood oxygen level–dependent MRI (BOLD), and susceptibility-weighted imaging (SWI) in evaluating renal dysfunction.


Seventy-two patients (mean age 53.72 ± 13.46 years) underwent MRI consisting of DWI, BOLD, and SWI. According to their estimated glomerular filtration rate (eGFR), the patients were classified into either severe renal function impairment (sRI, eGFR < 30 mL/min/1.73 m2), non-severe renal function impairment (non-sRI, eGFR ≥ 30 mL/min/1.73 m2, and < 80 mL/min/1.73 m2), or control (CG, eGFR ≥ 80 mL/min/1.73 m2) groups. Thirteen texture features were extracted and then were analyzed to select the most valuable for discerning the three groups with each imaging method. A ROC curve was performed to compare the capacities of the features to differentiate non-sRI from sRI or CG.


Six features proved to be the most valuable for assessing renal dysfunction: 0.25QuantileDWI, 0.5QuantileDWI, HomogeneityDWI, EntropyBOLD, SkewnessSWI, and CorrelationSWI. Three features derived from DWI (0.25QuantileDWI, 0.5QuantileDWI, and HomogeneityDWI) were smaller in sRI than in non-sRI; EntropyBOLD and CorrelationSWI were smaller in non-sRI than in CG (p < 0.05). 0.25QuantileDWI, 0.5QuantileDWI, and HomogeneityDWI showed similar capacities for differentiating sRI from non-sRI. Similarly, EntropyBOLD and CorrelationSWI showed equal capacities for differentiating non-sRI from CG.


Texture analysis based on DWI, BOLD, and SWI can assist in assessing renal dysfunction, and texture features based on BOLD and SWI may be suitable for assessing renal dysfunction during early stages.

Key Points

• Texture analysis based on MRI techniques allowed for assessing renal dysfunction.

Texture features based on BOLD and SWI, but not DWI, may be suitable for assessing renal function impairment during early stages.

• SWI exhibited a similar capacity to BOLD for assessing renal dysfunction.


Diffusion magnetic resonance imaging Chronic kidney disease Chronic renal insufficiency Image processing, computer-assisted 



Apparent diffusion coefficient


Acute kidney injury


Area under the receiver operating characteristic curve


Blood oxygen level–dependent MRI


Concordance correlation coefficient


Control group


Confidence interval


Chronic kidney disease


Diffusion-weighted imaging


Estimated glomerular filtration rate


Gray-level co-occurrence matrix


Interquartile range


Median absolute deviation


Magnetic resonance imaging


Non-severe renal function impairment


Receiver operating characteristic curve


Severe renal function impairment


Susceptibility-weighted imaging


T2-weighted imaging



This work was supported by the National Natural Science Foundation of China (81771798, 81771805); Jiangsu Provincial Medical Youth Talent Program, China (QNRC2016299); General Program of Jiangsu Provincial Commission of Health and Family Planning, China (H2017003); Key Project of Health Commission of Changzhou, Jiangsu, China (ZD201509); Applied and Basic Research Program of Science and Technology Bureau of Changzhou, Jiangsu, China (CJ20160038); and Changzhou Municipal Medical Youth Talent Program, Jiangsu, China (QN201610).

Compliance with ethical standards


The scientific guarantor of this publication is Wei Xing.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Bin Xu kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

This study was approved by Ethics Committee of Third Affiliated Hospital of Soochow University.


• retrospective

• cross-sectional study

• performed at one institution

Supplementary material

330_2018_5911_MOESM1_ESM.docx (18 kb)
Table S1 (DOCX 17 kb)


  1. 1.
    Webster AC, Nagler EV, Morton RL, Masson P (2017) Chronic kidney disease. Lancet 389:1238–1252CrossRefGoogle Scholar
  2. 2.
    Saran R, Li Y, Robinson B et al (2015) US renal data system 2014 annual data report: epidemiology of kidney disease in the United States. Am J Kidney Dis 66(Svii):S1–S305Google Scholar
  3. 3.
    Fine LG, Norman JT (2008) Chronic hypoxia as a mechanism of progression of chronic kidney diseases: from hypothesis to novel therapeutics. Kidney Int 74:867–872CrossRefGoogle Scholar
  4. 4.
    Hirakawa Y, Tanaka T, Nangaku M (2017) Renal hypoxia in CKD; pathophysiology and detecting methods. Front Physiol 8:99CrossRefGoogle Scholar
  5. 5.
    Takahashi T, Wang F, Quarles CC (2015) Current MRI techniques for the assessment of renal disease. Curr Opin Nephrol Hypertens 24:217–223CrossRefGoogle Scholar
  6. 6.
    Zhang JG, Xing ZY, Zha TT et al (2017) Longitudinal assessment of rabbit renal fibrosis induced by unilateral ureteral obstruction using two-dimensional susceptibility weighted imaging. J Magn Reson Imaging 47:1572–1577CrossRefGoogle Scholar
  7. 7.
    Abou-El-Ghar ME, El-Diasty TA, El-Assmy AM, Refaie HF, Refaie AF, Ghoneim MA (2012) Role of diffusion-weighted MRI in diagnosis of acute renal allograft dysfunction: a prospective preliminary study. Br J Radiol 85:e206–e211CrossRefGoogle Scholar
  8. 8.
    Chang K, Barnes S, Haacke EM, Grossman RI, Ge Y (2014) Imaging the effects of oxygen saturation changes in voluntary apnea and hyperventilation on susceptibility-weighted imaging. AJNR Am J Neuroradiol 35:1091–1095CrossRefGoogle Scholar
  9. 9.
    Pan L, Chen J, Xing W et al (2017) Magnetic resonance imaging evaluation of renal ischaemia-reperfusion injury in a rabbit model. Exp Physiol 102:1000–1006CrossRefGoogle Scholar
  10. 10.
    Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069CrossRefGoogle Scholar
  11. 11.
    Ortiz-Ramón R, Larroza A, Ruiz-España S, Arana E, Moratal D (2018) Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol.
  12. 12.
    Hocquelet A, Auriac T, Perier C et al (2018) Pre-treatment magnetic resonance-based texture features as potential imaging biomarkers for predicting event free survival in anal cancer treated by chemoradiotherapy. Eur Radiol.
  13. 13.
    Naganawa S, Enooku K, Tateishi R et al (2018) Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis. Eur Radiol.
  14. 14.
    American Diabetes Association (2013) Diagnosis and classification of diabetes mellitus. Diabetes Care 36(Suppl 1):S67–S74Google Scholar
  15. 15.
    Zhou HY, Chen TW, Zhang XM (2016) Functional magnetic resonance imaging in acute kidney injury: present status. Biomed Res Int 2016:2027370Google Scholar
  16. 16.
    Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE (2015) IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys 42:1341–1353CrossRefGoogle Scholar
  17. 17.
    Peng W, Liu C, Xia S et al (2017) Thyroid nodule recognition in computed tomography using first order statistics. Biomed Eng Online 16:67CrossRefGoogle Scholar
  18. 18.
    Alobaidli S, McQuaid S, South C, Prakash V, Evans P, Nisbet A (2014) The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol 87:20140369CrossRefGoogle Scholar
  19. 19.
    Woo S, Cho JY, Kim SY, Kim SH (2014) Histogram analysis of apparent diffusion coefficient map of diffusion-weighted MRI in endometrial cancer: a preliminary correlation study with histological grade. Acta Radiol 55:1270–1277CrossRefGoogle Scholar
  20. 20.
    Xu X, Fang W, Ling H, Chai W, Chen K (2010) Diffusion-weighted MR imaging of kidneys in patients with chronic kidney disease: initial study. Eur Radiol 20:978–983CrossRefGoogle Scholar
  21. 21.
    Ichikawa S, Motosugi U, Ichikawa T, Sano K, Morisaka H, Araki T (2013) Intravoxel incoherent motion imaging of the kidney: alterations in diffusion and perfusion in patients with renal dysfunction. Magn Reson Imaging 31:414–417CrossRefGoogle Scholar
  22. 22.
    Hueper K, Rong S, Gutberlet M et al (2013) T2 relaxation time and apparent diffusion coefficient for noninvasive assessment of renal pathology after acute kidney injury in mice: comparison with histopathology. Invest Radiol 48:834–842CrossRefGoogle Scholar
  23. 23.
    Mao W, Zhou J, Zeng M et al (2018) Intravoxel incoherent motion diffusion-weighted imaging for the assessment of renal fibrosis of chronic kidney disease: a preliminary study. Magn Reson Imaging 47:118–124CrossRefGoogle Scholar
  24. 24.
    Müller MF, Prasad PV, Bimmler D, Kaiser A, Edelman RR (1994) Functional imaging of the kidney by means of measurement of the apparent diffusion coefficient. Radiology 193:711–715CrossRefGoogle Scholar
  25. 25.
    Sigmund EE, Vivier PH, Sui D et al (2012) Intravoxel incoherent motion and diffusion-tensor imaging in renal tissue under hydration and furosemide flow challenges. Radiology 263:758–769CrossRefGoogle Scholar
  26. 26.
    Li LP, Tan H, Thacker JM et al (2017) Evaluation of renal blood flow in chronic kidney disease using arterial spin labeling perfusion magnetic resonance imaging. Kidney Int Rep 2:36–43CrossRefGoogle Scholar
  27. 27.
    Gillis KA, McComb C, Patel RK et al (2016) Non-contrast renal magnetic resonance imaging to assess perfusion and corticomedullary differentiation in health and chronic kidney disease. Nephron 133:183–192CrossRefGoogle Scholar
  28. 28.
    Chen WB, Liang L, Zhang B et al (2015) To evaluate the damage of renal function in CIAKI rats at 3T: using ASL and BOLD MRI. Biomed Res Int 2015:593060Google Scholar
  29. 29.
    Odudu A, Francis ST, McIntyre CW (2012) MRI for the assessment of organ perfusion in patients with chronic kidney disease. Curr Opin Nephrol Hypertens 21:647–654CrossRefGoogle Scholar
  30. 30.
    Neugarten J (2012) Renal BOLD-MRI and assessment for renal hypoxia. Kidney Int 81:613–614CrossRefGoogle Scholar
  31. 31.
    Rapacchi S, Smith RX, Wang Y et al (2015) Towards the identification of multi-parametric quantitative MRI biomarkers in lupus nephritis. Magn Reson Imaging 33:1066–1074CrossRefGoogle Scholar
  32. 32.
    Inoue T, Kozawa E, Okada H et al (2011) Noninvasive evaluation of kidney hypoxia and fibrosis using magnetic resonance imaging. J Am Soc Nephrol 22:1429–1434CrossRefGoogle Scholar
  33. 33.
    Milani B, Ansaloni A, Sousa-Guimaraes S et al (2017) Reduction of cortical oxygenation in chronic kidney disease: evidence obtained with a new analysis method of blood oxygenation level-dependent magnetic resonance imaging. Nephrol Dial Transplant 32:2097–2105CrossRefGoogle Scholar
  34. 34.
    Michaely HJ, Metzger L, Haneder S, Hansmann J, Schoenberg SO, Attenberger UI (2012) Renal BOLD-MRI does not reflect renal function in chronic kidney disease. Kidney Int 81:684–689CrossRefGoogle Scholar
  35. 35.
    Mie MB, Nissen JC, Zöllner FG et al (2010) Susceptibility weighted imaging (SWI) of the kidney at 3T--initial results. Z Med Phys 20:143–150CrossRefGoogle Scholar
  36. 36.
    Park SY, Kim CK, Park BK, Kim SJ, Lee S, Huh W (2014) Assessment of early renal allograft dysfunction with blood oxygenation level-dependent MRI and diffusion-weighted imaging. Eur J Radiol 83:2114–2121CrossRefGoogle Scholar
  37. 37.
    Li X, Xu X, Zhang Q et al (2014) Diffusion weighted imaging and blood oxygen level-dependent MR imaging of kidneys in patients with lupus nephritis. J Transl Med 12:295CrossRefGoogle Scholar
  38. 38.
    Daginawala N, Li B, Buch K et al (2016) Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Eur J Radiol 85:511–517CrossRefGoogle Scholar
  39. 39.
    Yu H, Buch K, Li B et al (2015) Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI. J Magn Reson Imaging 42:1259–1265CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Jiule Ding
    • 1
  • Zhaoyu Xing
    • 2
  • Zhenxing Jiang
    • 1
  • Hua Zhou
    • 3
  • Jia Di
    • 3
  • Jie Chen
    • 1
  • Jianguo Qiu
    • 1
  • Shengnan Yu
    • 1
  • Liqiu Zou
    • 4
  • Wei Xing
    • 1
    Email author
  1. 1.Department of RadiologyThird Affiliated Hospital of Soochow UniversityChangzhouChina
  2. 2.Department of UrologyThird Affiliated Hospital of Soochow UniversityChangzhouChina
  3. 3.Department of NephrologyThird Affiliated Hospital of Soochow UniversityChangzhouChina
  4. 4.Department of Radiology, Shenzhen nanshan People’s HospitalShenzhen University Health Science CenterShenzhenChina

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