Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Diffusion Weighted Imaging for Differentiating Benign from Malignant Orbital Tumors: Diagnostic Performance of the Apparent Diffusion Coefficient Based on Region of Interest Selection Method

  • Xu, Xiao-Quan (Department of Radiology, First Affiliated Hospital of Nanjing Medical University) ;
  • Hu, Hao (Department of Radiology, First Affiliated Hospital of Nanjing Medical University) ;
  • Su, Guo-Yi (Department of Radiology, First Affiliated Hospital of Nanjing Medical University) ;
  • Liu, Hu (Department of Ophthalmology, First Affiliated Hospital of Nanjing Medical University) ;
  • Shi, Hai-Bin (Department of Radiology, First Affiliated Hospital of Nanjing Medical University) ;
  • Wu, Fei-Yun (Department of Radiology, First Affiliated Hospital of Nanjing Medical University)
  • Received : 2016.04.03
  • Accepted : 2016.06.10
  • Published : 2016.09.01
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Abstract

Objective: To evaluate the differences in the apparent diffusion coefficient (ADC) measurements based on three different region of interest (ROI) selection methods, and compare their diagnostic performance in differentiating benign from malignant orbital tumors. Materials and Methods: Diffusion-weighted imaging data of sixty-four patients with orbital tumors (33 benign and 31 malignant) were retrospectively analyzed. Two readers independently measured the ADC values using three different ROIs selection methods including whole-tumor (WT), single-slice (SS), and reader-defined small sample (RDSS). The differences of ADC values ($ADC-ROI_{WT}$, $ADC-ROI_{SS}$, and $ADC-ROI_{RDSS}$) between benign and malignant group were compared using unpaired t test. Receiver operating characteristic curve was used to determine and compare their diagnostic ability. The ADC measurement time was compared using ANOVA analysis and the measurement reproducibility was assessed using Bland-Altman method and intra-class correlation coefficient (ICC). Results: Malignant group showed significantly lower $ADC-ROI_{WT}$, $ADC-ROI_{SS}$, and $ADC-ROI_{RDSS}$ than benign group (all p < 0.05). The areas under the curve showed no significant difference when using $ADC-ROI_{WT}$, $ADC-ROI_{SS}$, and $ADC-ROI_{RDSS}$ as differentiating index, respectively (all p > 0.05). The $ROI_{SS}$ and $ROI_{RDSS}$ required comparable measurement time (p > 0.05), while significantly shorter than $ROI_{WT}$ (p < 0.05). The $ROI_{SS}$ showed the best reproducibility (mean difference ${\pm}$ limits of agreement between two readers were $0.022[-0.080-0.123]{\times}10^{-3}mm^2/s$; ICC, 0.997) among three ROI methods. Conclusion: Apparent diffusion coefficient values based on the three different ROI selection methods can help to differentiate benign from malignant orbital tumors. The results of measurement time, reproducibility and diagnostic ability suggest that the $ROI_{SS}$ method are potentially useful for clinical practice.

Keywords

References

  1. Tailor TD, Gupta D, Dalley RW, Keene CD, Anzai Y. Orbital neoplasms in adults: clinical, radiologic, and pathologic review. Radiographics 2013;33:1739-1758 https://doi.org/10.1148/rg.336135502
  2. Gufler H, Preiss M, Koesling S. Visibility of sutures of the orbit and periorbital region using multidetector computed tomography. Korean J Radiol 2014;15:802-809 https://doi.org/10.3348/kjr.2014.15.6.802
  3. Xian J, Zhang Z, Wang Z, Li J, Yang B, Man F, et al. Value of MR imaging in the differentiation of benign and malignant orbital tumors in adults. Eur Radiol 2010;20:1692-1702 https://doi.org/10.1007/s00330-009-1711-0
  4. Lim HK, Lee JH, Baek HJ, Kim N, Lee H, Park JW, et al. Is diffusion-weighted MRI useful for differentiation of small non-necrotic cervical lymph nodes in patients with head and neck malignancies? Korean J Radiol 2014;15:810-816 https://doi.org/10.3348/kjr.2014.15.6.810
  5. Xu XQ, Hu H, Su GY, Zhang L, Liu H, Hong XN, et al. Orbital indeterminate lesions in adults: combined magnetic resonance morphometry and histogram analysis of apparent diffusion coefficient maps for predicting malignancy. Acad Radiol 2016;23:200-208 https://doi.org/10.1016/j.acra.2015.10.015
  6. Sepahdari AR, Kapur R, Aakalu VK, Villablanca JP, Mafee MF. Diffusion-weighted imaging of malignant ocular masses: initial results and directions for further study. AJNR Am J Neuroradiol 2012;33:314-319 https://doi.org/10.3174/ajnr.A2747
  7. 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 https://doi.org/10.5152/dir.2015.15202
  8. Razek AA, Elkhamary S, Mousa A. Differentiation between benign and malignant orbital tumors at 3-T diffusion MR-imaging. Neuroradiology 2011;53:517-522 https://doi.org/10.1007/s00234-011-0838-2
  9. Sepahdari AR, Aakalu VK, Setabutr P, Shiehmorteza M, Naheedy JH, Mafee MF. Indeterminate orbital masses: restricted diffusion at MR imaging with echo-planar diffusion-weighted imaging predicts malignancy. Radiology 2010;256:554-564 https://doi.org/10.1148/radiol.10091956
  10. Haradome K, Haradome H, Usui Y, Ueda S, Kwee TC, Saito K, et al. Orbital lymphoproliferative disorders (OLPDs): value of MR imaging for differentiating orbital lymphoma from benign OPLDs. AJNR Am J Neuroradiol 2014;35:1976-1982 https://doi.org/10.3174/ajnr.A3986
  11. Ma C, Liu L, Li J, Wang L, Chen LG, Zhang Y, et al. Apparent diffusion coefficient (ADC) measurements in pancreatic adenocarcinoma: a preliminary study of the effect of region of interest on ADC values and interobserver variability. J Magn Reson Imaging 2016;43:407-413 https://doi.org/10.1002/jmri.25007
  12. Wu CJ, Wang Q, Li H, Wang XN, Liu XS, Shi HB, et al. DWI-associated entire-tumor histogram analysis for the differentiation of low-grade prostate cancer from intermediate-high-grade prostate cancer. Abdom Imaging 2015;40:3214-3221 https://doi.org/10.1007/s00261-015-0499-4
  13. Colagrande S, Pasquinelli F, Mazzoni LN, Belli G, Virgili G. MR-diffusion weighted imaging of healthy liver parenchyma: repeatability and reproducibility of apparent diffusion coefficient measurement. J Magn Reson Imaging 2010;31:912-920 https://doi.org/10.1002/jmri.22117
  14. Mukuda N, Fujii S, Inoue C, Fukunaga T, Tanabe Y, Itamochi H, et al. Apparent diffusion coefficient (ADC) measurement in ovarian tumor: effect of region-of-interest methods on ADC values and diagnostic ability. J Magn Reson Imaging 2016;43:720-725 https://doi.org/10.1002/jmri.25011
  15. Liu L, Ma C, Li J, Wang L, Chen LG, Zhang Y, et al. Comparison of the diagnostic performances of three techniques of ROI placement for ADC measurements in pancreatic adenocarcinoma. Acad Radiol 2015;22:1385-1392 https://doi.org/10.1016/j.acra.2015.06.017
  16. Giannotti E, Waugh S, Priba L, Davis Z, Crowe E, Vinnicombe S. Assessment and quantification of sources of variability in breast apparent diffusion coefficient (ADC) measurements at diffusion weighted imaging. Eur J Radiol 2015;84:1729-1736 https://doi.org/10.1016/j.ejrad.2015.05.032
  17. 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 https://doi.org/10.2307/2531595
  18. Cohen J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull 1968;70:213-220 https://doi.org/10.1037/h0026256
  19. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-310
  20. Ahlawat S, Khandheria P, Del Grande F, Morelli J, Subhawong TK, Demehri S, et al. Interobserver variability of selective region-of-interest measurement protocols for quantitative diffusion weighted imaging in soft tissue masses: comparison with whole tumor volume measurements. J Magn Reson Imaging 2016;43:446-454 https://doi.org/10.1002/jmri.24994
  21. Wan H, Sha Y, Zhang F, Hong R, Tian G, Fan H. Diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging, and two-dimensional navigator-based reacquisition in detecting acute optic neuritis. J Magn Reson Imaging 2016;43:655-660 https://doi.org/10.1002/jmri.25026

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