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Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status

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Abstract

Objectives

To explore the correlations of parameters derived from standard diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) with the Ki-67 proliferation status.

Methods

Seventy-five patients with histologically proven sinonasal malignancies who underwent standard DWI, DKI and IVIM were retrospectively reviewed. The mean, minimum, maximum and whole standard DWI [apparent diffusion coefficient (ADC)], DKI [diffusion kurtosis (K) and diffusion coefficient (Dk)] and IVIM [pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f)] parameters were measured and correlated with the Ki-67 labelling index (LI). The Ki-67 LI was categorised as high (> 50%) or low (≤ 50%).

Results

The K and f values were positively correlated with the Ki-67 LI (rho = 0.295~0.532), whereas the ADC, Dk and D values were negatively correlated with the Ki-67 LI (rho = -0.443~-0.277). The ADC, Dk and D values were lower, whereas the K value was higher in sinonasal malignancies with a high Ki-67 LI than in those in a low Ki-67 LI (all p < 0.05). A higher maximum K value (Kmax > 0.977) independently predicted a high Ki-67 status [odds ratio (OR) = 7.614; 95% confidence interval (CI) = 2.197-38.674; p = 0.017].

Conclusion

ADC, Dk, K, D and f are correlated with Ki-67 LI. Kmax is the strongest independent factor for predicting Ki-67 status.

Key Points

DWI-derived parameters from different models are capable of providing different pathophysiological information.

DWI, DKI and IVIM parameters are associated with Ki-67 proliferation status.

K max derived from DKI is the strongest independent factor for the prediction of Ki-67 proliferation status.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

CI:

Confidence interval

DKI:

Diffusion kurtosis imaging

DWI:

Diffusion-weighted imaging

EPI:

Echo planar imaging

FOV:

Field of view

ICC:

Intraclass correlation coefficient

IVIM:

Intravoxel incoherent motion

LI:

Labelling index

NPV:

Negative predictive value

OR:

Odds ratio

PPV:

Positive predictive value

ROC:

Receiver-operating characteristic

ROIs:

Regions of interest

References

  1. Slootweg PJ, Ferlito A, Cardesa A et al (2013) Sinonasal tumors: a clinicopathologic update of selected tumors. Eur Arch Otorhinolaryngol 270:5–20

    Article  PubMed  Google Scholar 

  2. Su SY, Kupferman ME, DeMonte F et al (2014) Endoscopic resection of sinonasal cancers. Curr Oncol Rep 16:369

    Article  PubMed  Google Scholar 

  3. Eggesbo HB (2012) Imaging of sinonasal tumours. Cancer Imaging 12:136–152

    Article  PubMed  Google Scholar 

  4. Koeller KK (2016) Radiologic features of sinonasal tumors. Head Neck Pathol 10:1–12

    Article  PubMed  PubMed Central  Google Scholar 

  5. Dulguerov P, Jacobsen MS, Allal AS, Lehmann W, Calcaterra T (2001) Nasal and paranasal sinus carcinoma: are we making progress? A series of 220 patients and a systematic review. Cancer 92:3012–3029

    Article  PubMed  CAS  Google Scholar 

  6. Bhattacharyya N (2002) Cancer of the nasal cavity: survival and factors influencing prognosis. Arch Otolaryngol Head Neck Surg 128:1079–1083

    Article  PubMed  Google Scholar 

  7. Valente G, Mamo C, Bena A et al (2006) Prognostic significance of microvessel density and vascular endothelial growth factor expression in sinonasal carcinomas. Hum Pathol 37:391–400

    Article  PubMed  CAS  Google Scholar 

  8. Airoldi M, Garzaro M, Valente G et al (2009) Clinical and biological prognostic factors in 179 cases with sinonasal carcinoma treated in the Italian Piedmont region. Oncology 76:262–269

    Article  PubMed  Google Scholar 

  9. Chen WJ, He DS, Tang RX, Ren FH, Chen G (2015) Ki-67 is a valuable prognostic factor in gliomas: evidence from a systematic review and meta-analysis. Asian Pac J Cancer Prev 16:411–420

    Article  PubMed  Google Scholar 

  10. Stathopoulos GP, Malamos NA, Markopoulos C et al (2014) The role of Ki-67 in the proliferation and prognosis of breast cancer molecular classification subtypes. Anticancer Drugs 25:950–957

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Fukushima S, Sugita Y, Niino D, Mihashi H, Ohshima K (2012) Clincopathological analysis of olfactory neuroblastoma. Brain Tumor Pathol 29:207–215

    Article  PubMed  CAS  Google Scholar 

  12. Le Bihan D (1995) Molecular diffusion, tissue microdynamics and microstructure. NMR Biomed 8:375–386

    Article  PubMed  Google Scholar 

  13. Yan R, Haopeng P, Xiaoyuan F et al (2016) Non-Gaussian diffusion MR imaging of glioma: comparisons of multiple diffusion parameters and correlation with histologic grade and MIB-1 (Ki-67 labeling) index. Neuroradiology 58:121–132

    Article  PubMed  Google Scholar 

  14. Shin JK, Kim JY (2017) Dynamic contrast-enhanced and diffusion-weighted MRI of estrogen receptor-positive invasive breast cancers: associations between quantitative MR parameters and Ki-67 proliferation status. J Magn Reson Imaging 45:94–102

    Article  PubMed  Google Scholar 

  15. Li HM, Zhao SH, Qiang JW et al (2017) Diffusion kurtosis imaging for differentiating borderline from malignant epithelial ovarian tumors: a correlation with Ki-67 expression. J Magn Reson Imaging. https://doi.org/10.1002/jmri.25696

  16. Driessen JP, Caldas-Magalhaes J, Janssen LM et al (2014) Diffusion-weighted MR imaging in laryngeal and hypopharyngeal carcinoma: association between apparent diffusion coefficient and histologic findings. Radiology 272:456–463

    Article  PubMed  Google Scholar 

  17. Yuan J, Yeung DK, Mok GS et al (2014) Non-Gaussian analysis of diffusion weighted imaging in head and neck at 3T: a pilot study in patients with nasopharyngeal carcinoma. PLoS One 9:e87024

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K (2005) Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53:1432–1440

    Article  PubMed  Google Scholar 

  19. Le Bihan D (1988) Intravoxel incoherent motion imaging using steady-state free precession. Magn Reson Med 7:346–351

    Article  PubMed  Google Scholar 

  20. Le Bihan D, Breton E, Lallemand D et al (1986) MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161:401–407

    Article  PubMed  Google Scholar 

  21. Jiang JX, Tang ZH, Zhong YF, Qiang JW (2016) Diffusion kurtosis imaging for differentiating between the benign and malignant sinonasal lesions. J Magn Reson Imaging. https://doi.org/10.1002/jmri.25500

  22. Sumi M, Nakamura T (2013) Head and neck tumors: assessment of perfusion-related parameters and diffusion coefficients based on the intravoxel incoherent motion model. AJNR Am J Neuroradiol 34:410–416

    Article  PubMed  CAS  Google Scholar 

  23. Sumi M, Nakamura T (2014) Head and neck tumours: combined MRI assessment based on IVIM and TIC analyses for the differentiation of tumors of different histological types. Eur Radiol 24:223–231

    Article  PubMed  Google Scholar 

  24. Sumi M, Van Cauteren M, Sumi T et al (2012) Salivary gland tumors: use of intravoxel incoherent motion MR imaging for assessment of diffusion and perfusion for the differentiation of benign from malignant tumors. Radiology 263:770–777

    Article  PubMed  Google Scholar 

  25. Lu Y, Jansen JF, Mazaheri Y et al (2012) Extension of the intravoxel incoherent motion model to non-Gaussian diffusion in head and neck cancer. J Magn Reson Imaging 36:1088–1096

    Article  PubMed  PubMed Central  Google Scholar 

  26. Jansen JF, Stambuk HE, Koutcher JA, Shukla-Dave A (2010) Non-Gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: a feasibility study. AJNR Am J Neuroradiol 31:741–748

    Article  PubMed  CAS  Google Scholar 

  27. Sun K, Chen X, Chai W et al (2015) Breast cancer: diffusion kurtosis MR imaging-diagnostic accuracy and correlation with clinical-pathologic factors. Radiology 277:46–55

    Article  PubMed  Google Scholar 

  28. Le Bihan D, Turner R, MacFall JR (1989) Effects of intravoxel incoherent motions (IVIM) in steady-state free precession (SSFP) imaging: application to molecular diffusion imaging. Magn Reson Med 10:324–337

    Article  PubMed  Google Scholar 

  29. Marzi S, Piludu F, Vidiri A (2013) Assessment of diffusion parameters by intravoxel incoherent motion MRI in head and neck squamous cell carcinoma. NMR Biomed 26:1806–1814

    Article  PubMed  Google Scholar 

  30. Fujima N, Yoshida D, Sakashita T et al (2017) Prediction of the treatment outcome using intravoxel incoherent motion and diffusional kurtosis imaging in nasal or sinonasal squamous cell carcinoma patients. Eur Radiol 27:956–965

    Article  PubMed  Google Scholar 

  31. Fudaba H, Shimomura T, Abe T et al (2014) Comparison of multiple parameters obtained on 3T pulsed arterial spin-labeling, diffusion tensor imaging, and MRS and the Ki-67 labeling index in evaluating glioma grading. AJNR Am J Neuroradiol 35:2091–2098

    Article  PubMed  CAS  Google Scholar 

  32. Iima M, Le Bihan D (2016) Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. Radiology 278:13–32

    Article  PubMed  Google Scholar 

  33. Le Bihan D, Turner R (1992) The capillary network: a link between IVIM and classical perfusion. Magn Reson Med 27:171–178

    Article  PubMed  Google Scholar 

  34. Lewin M, Fartoux L, Vignaud A et al (2011) The diffusion-weighted imaging perfusion fraction f is a potential marker of sorafenib treatment in advanced hepatocellular carcinoma: a pilot study. Eur Radiol 21:281–290

    Article  PubMed  Google Scholar 

  35. Liu C, Wang K, Chan Q et al (2016) Intravoxel incoherent motion MR imaging for breast lesions: comparison and correlation with pharmacokinetic evaluation from dynamic contrast-enhanced MR imaging. Eur Radiol 26:3888–3898

    Article  PubMed  Google Scholar 

  36. Lai V, Lee VH, Lam KO et al (2015) Intravoxel water diffusion heterogeneity MR imaging of nasopharyngeal carcinoma using stretched exponential diffusion model. Eur Radiol 25:1708–1713

    Article  PubMed  Google Scholar 

  37. Parikh J, Selmi M, Charles-Edwards G et al (2014) Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy. Radiology 272:100–112

    Article  PubMed  Google Scholar 

  38. Yoon SH, Park CM, Park SJ et al (2016) Tumor heterogeneity in lung cancer: assessment with dynamic contrast-enhanced MR imaging. Radiology 280:940–948

    Article  PubMed  Google Scholar 

Download references

Funding

This study has received funding from the Grant of Science and Technology Commission of Shanghai Municipality (no. 17411962100; 14411962000) and Shanghai Municipal Commission of Health and Family Planning (grant no. ZK2015A05).

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Corresponding authors

Correspondence to Zuohua Tang or Jinwei Qiang.

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Guarantor

The scientific guarantor of this publication is Prof. Zuohua Tang, MD, PhD, Eye and ENT Hospital of Shanghai Medical School, Fudan University, and Prof. Jinwei Qiang, MD, PhD, Jinshan Hospital of Shanghai Medical School, Fudan University.

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

No complex statistical methods were necessary for this paper.

Ethical approval

Institutional Review Board approval was obtained.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Cite this article

Xiao, Z., Zhong, Y., Tang, Z. et al. Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status. Eur Radiol 28, 2923–2933 (2018). https://doi.org/10.1007/s00330-017-5286-x

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  • DOI: https://doi.org/10.1007/s00330-017-5286-x

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