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

  • Magnetic Resonance
  • Published:
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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.

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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


  1. Webster AC, Nagler EV, Morton RL, Masson P (2017) Chronic kidney disease. Lancet 389:1238–1252

    Article  PubMed  Google Scholar 

  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–S305

  3. Fine LG, Norman JT (2008) Chronic hypoxia as a mechanism of progression of chronic kidney diseases: from hypothesis to novel therapeutics. Kidney Int 74:867–872

    Article  CAS  PubMed  Google Scholar 

  4. Hirakawa Y, Tanaka T, Nangaku M (2017) Renal hypoxia in CKD; pathophysiology and detecting methods. Front Physiol 8:99

    Article  PubMed  PubMed Central  Google Scholar 

  5. Takahashi T, Wang F, Quarles CC (2015) Current MRI techniques for the assessment of renal disease. Curr Opin Nephrol Hypertens 24:217–223

    Article  PubMed  PubMed Central  Google Scholar 

  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 47:1572–1577

    Article  PubMed  Google Scholar 

  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–e211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 35:1091–1095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 102:1000–1006

    Article  PubMed  Google Scholar 

  10. Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069

    Article  CAS  PubMed  Google Scholar 

  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. 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. Naganawa S, Enooku K, Tateishi R et al (2018) Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis. Eur Radiol.

  14. American Diabetes Association (2013) Diagnosis and classification of diabetes mellitus. Diabetes Care 36(Suppl 1):S67–S74

  15. Zhou HY, Chen TW, Zhang XM (2016) Functional magnetic resonance imaging in acute kidney injury: present status. Biomed Res Int 2016:2027370

    PubMed  PubMed Central  Google Scholar 

  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 42:1341–1353

    Article  PubMed  PubMed Central  Google Scholar 

  17. Peng W, Liu C, Xia S et al (2017) Thyroid nodule recognition in computed tomography using first order statistics. Biomed Eng Online 16:67

    Article  PubMed  PubMed Central  Google Scholar 

  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 87:20140369

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 55:1270–1277

    Article  PubMed  Google Scholar 

  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 20:978–983

    Article  PubMed  Google Scholar 

  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 31:414–417

    Article  PubMed  Google Scholar 

  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 48:834–842

    Article  CAS  PubMed  Google Scholar 

  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 47:118–124

    Article  PubMed  Google Scholar 

  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 193:711–715

    Article  PubMed  Google Scholar 

  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 263:758–769

    Article  PubMed  Google Scholar 

  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 2:36–43

    Article  PubMed  Google Scholar 

  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 133:183–192

    Article  PubMed  Google Scholar 

  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 2015:593060

    PubMed  PubMed Central  Google Scholar 

  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 21:647–654

    Article  PubMed  Google Scholar 

  30. Neugarten J (2012) Renal BOLD-MRI and assessment for renal hypoxia. Kidney Int 81:613–614

    Article  PubMed  Google Scholar 

  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 33:1066–1074

    Article  PubMed  Google Scholar 

  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 22:1429–1434

    Article  PubMed  PubMed Central  Google Scholar 

  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 32:2097–2105

    Article  CAS  PubMed  Google Scholar 

  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 81:684–689

    Article  CAS  PubMed  Google Scholar 

  35. Mie MB, Nissen JC, Zöllner FG et al (2010) Susceptibility weighted imaging (SWI) of the kidney at 3T--initial results. Z Med Phys 20:143–150

    Article  PubMed  Google Scholar 

  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 83:2114–2121

    Article  PubMed  Google Scholar 

  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 12:295

    Article  PubMed  PubMed Central  Google Scholar 

  38. Daginawala N, Li B, Buch K et al (2016) Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Eur J Radiol 85:511–517

    Article  PubMed  Google Scholar 

  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 42:1259–1265

    Article  PubMed  Google Scholar 

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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).

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Correspondence to Wei Xing.

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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

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Ding, J., Xing, Z., Jiang, Z. et al. Evaluation of renal dysfunction using texture analysis based on DWI, BOLD, and susceptibility-weighted imaging. Eur Radiol 29, 2293–2301 (2019).

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