Korean J Radiol.  2018 Jun;19(3):443-451. 10.3348/kjr.2018.19.3.443.

Utility of Readout-Segmented Echo-Planar Imaging-Based Diffusion Kurtosis Imaging for Differentiating Malignant from Benign Masses in Head and Neck Region

• Ma G
• Xu XQ
• Hu H
• Su GY
• Shen J
• Shi HB
• Wu FY

• KMID: 2410815
• DOI: http://doi.org/10.3348/kjr.2018.19.3.443

Abstract

OBJECTIVE
To compare the diagnostic performance of readout-segmented echo-planar imaging (RS-EPI)-based diffusion kurtosis imaging (DKI) and that of diffusion-weighted imaging (DWI) for differentiating malignant from benign masses in head and neck region.
MATERIALS AND METHODS
Between December 2014 and April 2016, we retrospectively enrolled 72 consecutive patients with head and neck masses who had undergone RS-EPI-based DKI scan (b value of 0, 500, 1000, and 1500 s/mm2) for pretreatment evaluation. Imaging data were post-processed by using monoexponential and diffusion kurtosis (DK) model for quantitation of apparent diffusion coefficient (ADC), apparent diffusion for Gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). Unpaired t test and Mann-Whitney U test were used to compare differences of quantitative parameters between malignant and benign groups. Receiver operating characteristic curve analyses were performed to determine and compare the diagnostic ability of quantitative parameters in predicting malignancy.
RESULTS
Malignant group demonstrated significantly lower ADC (0.754 ± 0.167 vs. 1.222 ± 0.420, p < 0.001) and Dapp (1.029 ± 0.226 vs. 1.640 ± 0.445, p < 0.001) while higher Kapp (1.344 ± 0.309 vs. 0.715 ± 0.249, p < 0.001) than benign group. Using a combination of Dapp and Kapp as diagnostic index, significantly better differentiating performance was achieved than using ADC alone (area under curve: 0.956 vs. 0.876, p = 0.042).
CONCLUSION
Compared to DWI, DKI could provide additional data related to tumor heterogeneity with significantly better differentiating performance. Its derived quantitative metrics could serve as a promising imaging biomarker for differentiating malignant from benign masses in head and neck region.

Keyword

Head and neck; Differentiation; Magnetic resonance imaging; Diffusion-weighted imaging; Diffusion kurtosis imaging; Tumor; Neoplasm; Imaging biomarker

MeSH Terms

Diffusion*
Echo-Planar Imaging
Head*
Humans
Magnetic Resonance Imaging
Neck*
Population Characteristics
Retrospective Studies
ROC Curve

Figure

• 

  Fig. 1 Box plots showing comparison of ADC (A), Dapp (B), and Kapp (C) for benign and malignant masses in head and neck region.Line in box represents median. Height of box represents interquartile range. Wiskers are lowest and highest data points within 1.5 interquartile range. Circles indicate outliers. ADC = apparent diffusion coefficient, Dapp = apparent diffusion for Gaussian distribution, Kapp = apparent kurtosis coefficient

• 

  Fig. 2 68-year-old man with adenocarcinoma of parotid gland.A. Axial T2-weighted image showing infiltrative mass located in right parotid gland. After region of interest (red line) was dawn around mass (B), color maps for ADC (C), Dapp (D), and Kapp (E) were obtained and superimposed on DK image (b1000 map). ADC, Dapp, and Kapp values of mass were 1.037 × 10−3 mm2/s, 1.325 × 10−3 mm2/s, and 0.766, respectively. DK = diffusion kurtosis

• 

  Fig. 3 53-year-old man with pleomorphic adenoma of parotid gland.A. Axial T2-weighted image showing local mass located in right parotid gland. After region of interest (red line) was dawn around mass (B), color maps for ADC (C), Dapp (D), and Kapp (E) were obtained and superimposed on DK image (b1000 map). ADC, Dapp, and Kapp values of mass were 2.010 × 10−3 mm2/s, 2.308 × 10−3 mm2/s, and 0.420, respectively.

• 

  Fig. 4 Comparison of diagnostic ability for discriminating malignant from benign masses in head and neck region among different parameters.Combination of Dapp and Kapp showed significantly (p = 0.042) higher AUC than ADC alone. AUC = area under ROC curve, ROC = receiver operating characteristic

• 

  Fig. 5 Bland-Altman plots showing reproducibility of measurement for ADC (A), Dapp (B), and Kapp (C).Blue line = mean absolute difference. Green lines = confidence interval of mean difference. Red lines = 95% confidence interval of mean difference

Reference

1. Lewis-Jones H, Colley S, Gibson D. Imaging in head and neck cancer: United Kingdom national multidisciplinary guidelines. J Laryngol Otol. 2016; 130:S28–S31. PMID: 27841111.

2. Wippold FJ 2nd. Head and neck imaging: the role of CT and MRI. J Magn Reson Imaging. 2007; 25:453–465. PMID: 17279529.

3. Serifoğlu Ì, Oz ÌÌ, Damar M, Tokgöz Ö, Yazgan Ö, Erdem Z. Diffusion-weighted imaging in the head and neck region: usefulness of apparent diffusion coefficient values for characterization of lesions. Diagn Interv Radiol. 2015; 21:208–214. PMID: 25910284.

4. Schafer J, Srinivasan A, Mukherji S. Diffusion magnetic resonance imaging in the head and neck. Magn Reson Imaging Clin N Am. 2011; 19:55–67. PMID: 21129635.

5. Srinivasan A, Dvorak R, Perni K, Rohrer S, Mukherji SK. Differentiation of benign and malignant pathology in the head and neck using 3T apparent diffusion coefficient values: early experience. AJNR Am J Neuroradiol. 2008; 29:40–44. PMID: 17921228.

6. Ginat DT, Mangla R, Yeaney G, Johnson M, Ekholm S. Diffusion-weighted imaging for differentiating benign from malignant skull lesions and correlation with cell density. AJR Am J Roentgenol. 2012; 198:W597–W601. PMID: 22623576.

7. Thoeny HC, De Keyzer F, King AD. Diffusion-weighted MR imaging in the head and neck. Radiology. 2012; 263:19–32. PMID: 22438440.

8. Xu XQ, Hu H, Su GY, Liu H, Hong XN, Shi HB, et al. Utility of histogram analysis of ADC maps for differentiating orbital tumors. Diagn Interv Radiol. 2016; 22:161–167. PMID: 26829400.

9. Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed. 2010; 23:698–710. PMID: 20632416.

10. Sun K, Chen X, Chai W, Fei X, Fu C, Yan X, et al. Breast cancer: diffusion kurtosis MR imaging-diagnostic accuracy and correlation with clinical-pathologic factors. Radiology. 2015; 277:46–55. PMID: 25938679.

11. Nogueira L, Brandão S, Matos E, Nunes RG, Loureiro J, Ramos I, et al. Application of the diffusion kurtosis model for the study of breast lesions. Eur Radiol. 2014; 24:1197–1203. PMID: 24658871.

12. Rosenkrantz AB, Sigmund EE, Winnick A, Niver BE, Spieler B, Morgan GR, et al. Assessment of hepatocellular carcinoma using apparent diffusion coefficient and diffusion kurtosis indices: preliminary experience in fresh liver explants. Magn Reson Imaging. 2012; 30:1534–1540. PMID: 22819175.

13. Rosenkrantz AB, Sigmund EE, Johnson G, Babb JS, Mussi TC, Melamed J, et al. Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer. Radiology. 2012; 264:126–135. PMID: 22550312.

14. Yuan J, Yeung DK, Mok GS, Bhatia KS, Wang YX, Ahuja AT, et al. Non-gaussian analysis of diffusion weighted imaging in head and neck at 3T: a pilot study in patients with nasopharyngeal carcinoma. PLoS One. 2014; 9:e87024. PMID: 24466318.

15. Jansen JF, Stambuk HE, Koutcher JA, Shukla-Dave A. Non-gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: a feasibility study. AJNR Am J Neuroradiol. 2010; 31:741–748. PMID: 20037133.

16. Chen Y, Ren W, Zheng D, Zhong J, Liu X, Yue Q, et al. Diffusion kurtosis imaging predicts neoadjuvant chemotherapy responses within 4 days in advanced nasopharyngeal carcinoma patients. J Magn Reson Imaging. 2015; 42:1354–1361. PMID: 25873208.

17. Sakamoto J, Kuribayashi A, Kotaki S, Fujikura M, Nakamura S, Kurabayashi T. Application of diffusion kurtosis imaging to odontogenic lesions: analysis of the cystic component. J Magn Reson Imaging. 2016; 44:1565–1571. PMID: 27185307.

18. Xu XQ, Liu J, Hu H, Su GY, Zhang YD, Shi HB, et al. Improve the image quality of orbital 3 T diffusion-weighted magnetic resonance imaging with readout-segmented echo-planar imaging. Clin Imaging. 2016; 40:793–796. PMID: 27317226.

19. Koyasu S, Iima M, Umeoka S, Morisawa N, Porter DA, Ito J, et al. The clinical utility of reduced-distortion readout-segmented echo-planar imaging in the head and neck region: initial experience. Eur Radiol. 2014; 24:3088–3096. PMID: 25117744.

20. Yuan M, Zhang YD, Zhu C, Yu TF, Shi HB, Shi ZF, et al. Comparison of intravoxel incoherent motion diffusion-weighted MR imaging with dynamic contrast-enhanced MRI for differentiating lung cancer from benign solitary pulmonary lesions. J Magn Reson Imaging. 2016; 43:669–679. PMID: 26340144.

21. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988; 44:837–845. PMID: 3203132.

22. Jiang JX, Tang ZH, Zhong YF, Qiang JW. Diffusion kurtosis imaging for differentiating between the benign and malignant sinonasal lesions. J Magn Reson Imaging. 2017; 45:1446–1454. PMID: 27758016.

23. Roethke MC, Kuder TA, Kuru TH, Fenchel M, Hadaschik BA, Laun FB, et al. Evaluation of diffusion kurtosis imaging versus standard diffusion imaging for detection and grading of peripheral zone prostate cancer. Invest Radiol. 2015; 50:483–489. PMID: 25867657.

24. Jiang R, Jiang J, Zhao L, Zhang J, Zhang S, Yao Y, et al. Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget. 2015; 6:42380–42393. PMID: 26544514.

25. Iima M, Yano K, Kataoka M, Umehana M, Murata K, Kanao S, et al. Quantitative non-gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions. Invest Radiol. 2015; 50:205–211. PMID: 25260092.

26. Yu J, Huang DY, Li Y, Dai X, Shi HB. Correlation of standard diffusion-weighted imaging and diffusion kurtosis imaging with distant metastases of rectal carcinoma. J Magn Reson Imaging. 2016; 44:221–229. PMID: 26715111.

27. Zhao M, Liu Z, Sha Y, Wang S, Ye X, Pan Y, et al. Readout-segmented echo-planar imaging in the evaluation of sinonasal lesions: a comprehensive comparison of image quality in single-shot echo-planar imaging. Magn Reson Imaging. 2016; 34:166–172. PMID: 26541548.

28. Bogner W, Pinker-Domenig K, Bickel H, Chmelik M, Weber M, Helbich TH, et al. Readout-segmented echo-planar imaging improves the diagnostic performance of diffusion-weighted MR breast examinations at 3.0 T. Radiology. 2012; 263:64–76. PMID: 22438442.