Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
권호 표지
DOI QR코드

DOI QR Code

Percentile-Based Analysis of Non-Gaussian Diffusion Parameters for Improved Glioma Grading

  • Karaman, M. Muge (Center for MR Research, University of Illinois at Chicago) ;
  • Zhou, Christopher Y. (Trinity College, Duke University) ;
  • Zhang, Jiaxuan (Center for MR Research, University of Illinois at Chicago) ;
  • Zhong, Zheng (Center for MR Research, University of Illinois at Chicago) ;
  • Wang, Kezhou (Center for MR Research, University of Illinois at Chicago) ;
  • Zhu, Wenzhen (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology)
  • Received : 2021.10.28
  • Accepted : 2022.05.17
  • Published : 2022.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study is to systematically determine an optimal percentile cut-off in histogram analysis for calculating the mean parameters obtained from a non-Gaussian continuous-time random-walk (CTRW) diffusion model for differentiating individual glioma grades. This retrospective study included 90 patients with histopathologically proven gliomas (42 grade II, 19 grade III, and 29 grade IV). We performed diffusion-weighted imaging using 17 b-values (0-4000 s/mm2) at 3T, and analyzed the images with the CTRW model to produce an anomalous diffusion coefficient (Dm) along with temporal (𝛼) and spatial (𝛽) diffusion heterogeneity parameters. Given the tumor ROIs, we created a histogram of each parameter; computed the P-values (using a Student's t-test) for the statistical differences in the mean Dm, 𝛼, or 𝛽 for differentiating grade II vs. grade III gliomas and grade III vs. grade IV gliomas at different percentiles (1% to 100%); and selected the highest percentile with P < 0.05 as the optimal percentile. We used the mean parameter values calculated from the optimal percentile cut-offs to do a receiver operating characteristic (ROC) analysis based on individual parameters or their combinations. We compared the results with those obtained by averaging data over the entire region of interest (i.e., 100th percentile). We found the optimal percentiles for Dm, 𝛼, and 𝛽 to be 68%, 75%, and 100% for differentiating grade II vs. III and 58%, 19%, and 100% for differentiating grade III vs. IV gliomas, respectively. The optimal percentile cut-offs outperformed the entire-ROI-based analysis in sensitivity (0.761 vs. 0.690), specificity (0.578 vs. 0.526), accuracy (0.704 vs. 0.639), and AUC (0.671 vs. 0.599) for grade II vs. III differentiations and in sensitivity (0.789 vs. 0.578) and AUC (0.637 vs. 0.620) for grade III vs. IV differentiations, respectively. Percentile-based histogram analysis, coupled with the multi-parametric approach enabled by the CTRW diffusion model using high b-values, can improve glioma grading.

Keywords

Acknowledgement

We are grateful to Jingjing Jiang, Rifeng Jiang, and Changliang Su for assisting with MRI image collections, to Dr. Liping Qi, Dr. Tibor Valyi-Nagy, Dr. Kaibao Sun, and Dr. Qingfei Luo and Guangyu Dan for helpful discussions, and to Dong Kuang for guidance on pathology.

References

  1. Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of tumors of the central nervous system: a summary. Acta Neuropathol 2016;131:803-820 https://doi.org/10.1007/s00401-016-1545-1
  2. Ostrom QT, Gittleman H, Stetson L, Virk SM, Barnholtz-Sloan JS. Epidemiology of gliomas. Cancer Treat Res 2015;163:1-14 https://doi.org/10.1007/978-3-319-12048-5_1
  3. Ludwig K, Kornblum HI. Molecular markers in glioma. J Neurooncol 2017;134:505-512 https://doi.org/10.1007/s11060-017-2379-y
  4. Ginsberg LE, Fuller GN, Hashmi M, Leeds NE, Schomer DF. The significance of lack of MR contrast enhancement of supratentorial brain tumors in adults: histopathological evaluation of a series. Surg Neurol 1998;49:436-440 https://doi.org/10.1016/S0090-3019(97)00360-1
  5. Al-Okaili RN, Krejza J, Woo JH, et al. Intraaxial brain masses: MR imaging-based diagnostic strategy--initial experience. Radiology 2007;243:539-550 https://doi.org/10.1148/radiol.2432060493
  6. van Dijken BRJ, van Laar PJ, Holtman GA, van der Hoorn A. Diagnostic accuracy of magnetic resonance imaging techniques for treatment response evaluation in patients with high-grade glioma, a systematic review and meta-analysis. Eur Radiol 2017;27:4129-4144 https://doi.org/10.1007/s00330-017-4789-9
  7. Zonari P, Baraldi P, Crisi G. Multimodal MRI in the characterization of glial neoplasms: the combined role of single-voxel MR spectroscopy, diffusion imaging and echoplanar perfusion imaging. Neuroradiology 2007;49:795-803 https://doi.org/10.1007/s00234-007-0253-x
  8. Kickingereder P, Wiestler B, Sahm F, et al. Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging. Radiology 2014;272:843-850 https://doi.org/10.1148/radiol.14132740
  9. Zhang L, Min Z, Tang M, Chen S, Lei X, Zhang X. The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas: evidence from a meta-analysis. J Neurol Sci 2017;373:9-15 https://doi.org/10.1016/j.jns.2016.12.008
  10. Le Bihan D, Iima M. Diffusion magnetic resonance imaging: what water tells us about biological tissues. PLoS Biol 2015;13:e1002203 https://doi.org/10.1371/journal.pbio.1002203
  11. Le Bihan D. Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. Radiology 2013;268:318-322 https://doi.org/10.1148/radiol.13130420
  12. Tang L, Zhou XJ. Diffusion MRI of cancer: from low to high b-values. J Magn Reson Imaging 2019;49:23-40 https://doi.org/10.1002/jmri.26293
  13. Niendorf T, Dijkhuizen RM, Norris DG, van Lookeren Campagne M, Nicolay K. Biexponential diffusion attenuation in various states of brain tissue: implications for diffusion-weighted imaging. Magn Reson Med 1996;36:847-857 https://doi.org/10.1002/mrm.1910360607
  14. Assaf Y, Mayk A, Cohen Y. Displacement imaging of spinal cord using q-space diffusion-weighted MRI. Magn Reson Med 2000;44:713-722 https://doi.org/10.1002/1522-2594(200011)44:5<713::AID-MRM9>3.0.CO;2-6
  15. Yablonskiy DA, Bretthorst GL, Ackerman JJ. Statistical model for diffusion attenuated MR signal. Magn Reson Med 2003;50:664-669 https://doi.org/10.1002/mrm.10578
  16. Bennett KM, Schmainda KM, Bennett RT, Rowe DB, Lu H, Hyde JS. Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magn Reson Med 2003;50:727-734 https://doi.org/10.1002/mrm.10581
  17. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988;168:497-505 https://doi.org/10.1148/radiology.168.2.3393671
  18. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005;53:1432-1440 https://doi.org/10.1002/mrm.20508
  19. Ozarslan E, Basser PJ, Shepherd TM, Thelwall PE, Vemuri BC, Blackband SJ. Observation of anomalous diffusion in excised tissue by characterizing the diffusion-time dependence of the MR signal. J Magn Reson 2006;183:315-323 https://doi.org/10.1016/j.jmr.2006.08.009
  20. Westin CF, Knutsson H, Pasternak O, et al. Q-space trajectory imaging for multidimensional diffusion MRI of the human brain. Neuroimage 2016;135:345-362 https://doi.org/10.1016/j.neuroimage.2016.02.039
  21. Panagiotaki E, Chan RW, Dikaios N, et al. Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging. Invest Radiol 2015;50:218-227 https://doi.org/10.1097/RLI.0000000000000115
  22. White NS, Leergaard TB, D'Arceuil H, Bjaalie JG, Dale AM. Probing tissue microstructure with restriction spectrum imaging: histological and theoretical validation. Hum Brain Mapp 2013;34:327-346 https://doi.org/10.1002/hbm.21454
  23. Zhou XJ, Gao Q, Abdullah O, Magin RL. Studies of anomalous diffusion in the human brain using fractional order calculus. Magn Reson Med 2010;63:562-569 https://doi.org/10.1002/mrm.22285
  24. Magin RL, Abdullah O, Baleanu D, Zhou XJ. Anomalous diffusion expressed through fractional order differential operators in the Bloch-Torrey equation. J Magn Reson 2008;190:255-270 https://doi.org/10.1016/j.jmr.2007.11.007
  25. Ingo C, Magin RL, Colon-Perez L, Triplett W, Mareci TH. On random walks and entropy in diffusion-weighted magnetic resonance imaging studies of neural tissue. Magn Reson Med 2014;71:617-627 https://doi.org/10.1002/mrm.24706
  26. Karaman MM, Sui Y, Wang H, Magin RL, Li Y, Zhou XJ. Differentiating low- and high-grade pediatric brain tumors using a continuous-time random-walk diffusion model at high b-values. Magn Reson Med 2016;76:1149-1157 https://doi.org/10.1002/mrm.26012
  27. Karaman MM, Wang H, Sui Y, Engelhard HH, Li Y, Zhou XJ. A fractional motion diffusion model for grading pediatric brain tumors. Neuroimage Clin 2016;12:707-714 https://doi.org/10.1016/j.nicl.2016.10.003
  28. Barrick TR, Spilling CA, Ingo C, et al. Quasi-diffusion magnetic resonance imaging (QDI): a fast, high b-value diffusion imaging technique. Neuroimage 2020;211:116606 https://doi.org/10.1016/j.neuroimage.2020.116606
  29. Ingo C, Sui Y, Chen Y, Parrish TB, Webb AG, Ronen I. Parsimonious continuous time random walk models and kurtosis for diffusion in magnetic resonance of biological tissue. Front Phys 2015;3
  30. Sui Y, Wang H, Liu G, et al. Differentiation of low- and high-grade pediatric brain tumors with high b-value diffusion-weighted MR imaging and a fractional order calculus model. Radiology 2015;277:489-496 https://doi.org/10.1148/radiol.2015142156
  31. Sui Y, Xiong Y, Jiang J, et al. Differentiation of low- and high-grade gliomas using high b-value diffusion imaging with a non-Gaussian diffusion model. AJNR Am J Neuroradiol 2016;37:1643-1649 https://doi.org/10.3174/ajnr.A4836
  32. Tang L, Sui Y, Zhong Z, et al. Non-Gaussian diffusion imaging with a fractional order calculus model to predict response of gastrointestinal stromal tumor to second-line sunitinib therapy. Magn Reson Med 2018;79:1399-1406 https://doi.org/10.1002/mrm.26798
  33. Karaman MM, Tang L, Li Z, Sun Y, Li JZ, Zhou XJ. In vivo assessment of Lauren classification for gastric adenocarcinoma using diffusion MRI with a fractional order calculus model. Eur Radiol 2021;31:5659-5668 https://doi.org/10.1007/s00330-021-07694-3
  34. Cha S. Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol 2006;27:475-487
  35. Just N. Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 2014;111:2205-2213 https://doi.org/10.1038/bjc.2014.512
  36. Padhani AR, Liu G, Koh DM, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 2009;11:102-125 https://doi.org/10.1593/neo.81328
  37. Kang Y, Choi SH, Kim YJ, et al. Gliomas: histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging--correlation with tumor grade. Radiology 2011;261:882-890 https://doi.org/10.1148/radiol.11110686
  38. Huang H, Zhang Y, Cheng J, Wen M. Whole-tumor histogram analysis of apparent diffusion coefficient maps in grading diagnosis of ependymoma. Chinese J Acad Radiol 2020;2:41-46 https://doi.org/10.1007/s42058-019-00019-w
  39. Chenevert TL, Malyarenko DI, Galban CJ, et al. Comparison of voxel-wise and histogram analyses of glioma ADC maps for prediction of early therapeutic change. Tomography 2019;5:7-14 https://doi.org/10.18383/j.tom.2018.00049
  40. Sorensen AG, Buonanno FS, Gonzalez RG, et al. Hyperacute stroke: evaluation with combined multisection diffusion-weighted and hemodynamically weighted echo-planar MR imaging. Radiology 1996;199:391-401 https://doi.org/10.1148/radiology.199.2.8668784
  41. Fagerland MW, Lydersen S, Laake P. The McNemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional. BMC Med Res Methodol 2013;13:91 https://doi.org/10.1186/1471-2288-13-91
  42. Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983;148:839-843 https://doi.org/10.1148/radiology.148.3.6878708
  43. Catalaa I, Henry R, Dillon WP, et al. Perfusion, diffusion and spectroscopy values in newly diagnosed cerebral gliomas. NMR Biomed 2006;19:463-475 https://doi.org/10.1002/nbm.1059
  44. Murakami R, Hirai T, Sugahara T, et al. Grading astrocytic tumors by using apparent diffusion coefficient parameters: superiority of a one- versus two-parameter pilot method. Radiology 2009;251:838-845 https://doi.org/10.1148/radiol.2513080899
  45. Tozer DJ, Jager HR, Danchaivijitr N, et al. Apparent diffusion coefficient histograms may predict low-grade glioma subtype. NMR Biomed 2007;20:49-57 https://doi.org/10.1002/nbm.1091
  46. Karaman MM, Zhang J, Xie KL, Zhu W, Zhou XJ. Quartile histogram assessment of glioma malignancy using high b-value diffusion MRI with a continuous-time random-walk model. NMR Biomed 2021;34:e4485
  47. Zhong Z, Merkitch D, Karaman MM, et al. High-spatial-resolution diffusion MRI in Parkinson disease: lateral asymmetry of the substantia nigra. Radiology 2019;291:149-157 https://doi.org/10.1148/radiol.2019181042
  48. Yu Q, Reutens D, Vegh V. Can anomalous diffusion models in magnetic resonance imaging be used to characterise white matter tissue microstructure? Neuroimage 2018;175:122-137 https://doi.org/10.1016/j.neuroimage.2018.03.052
  49. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986;161:401-407 https://doi.org/10.1148/radiology.161.2.3763909