Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation
Autoři:
Qing Wang aff001; Gloria J. Guzmán Pérez-Carrillo aff002; Maria Rosana Ponisio aff001; Pamela LaMontagne aff001; Sonika Dahiya aff003; Daniel S. Marcus aff001; Mikhail Milchenko aff001; Joshua Shimony aff001; Jingxia Liu aff004; Gengsheng Chen aff001; Amber Salter aff005; Parinaz Massoumzadeh aff001; Michelle M. Miller-Thomas aff001; Keith M. Rich aff006; Jonathan McConathy aff007; Tammie L. S. Benzinger aff001; Yong Wang aff001
Působiště autorů:
Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
aff001; Department of Medical Imaging, Neuroradiology Section, University of Arizona, Tucson, Arizona, United States of America
aff002; Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri, United States of America
aff003; Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
aff004; Department of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, United States of America
aff005; Department of Neurosurgery, Washington University in St. Louis, St. Louis, Missouri, United States of America
aff006; Department of Radiology, Division of Molecular Imaging and Therapeutics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
aff007; Department of Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, Missouri, United States of America
aff008
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225093
Souhrn
Objectives
Primary brain tumors are composed of tumor cells, neural/glial tissues, edema, and vasculature tissue. Conventional MRI has a limited ability to evaluate heterogeneous tumor pathologies. We developed a novel diffusion MRI-based method—Heterogeneity Diffusion Imaging (HDI)—to simultaneously detect and characterize multiple tumor pathologies and capillary blood perfusion using a single diffusion MRI scan.
Methods
Seven adult patients with primary brain tumors underwent standard-of-care MRI protocols and HDI protocol before planned surgical resection and/or stereotactic biopsy. Twelve tumor sampling sites were identified using a neuronavigational system and recorded for imaging data quantification. Metrics from both protocols were compared between World Health Organization (WHO) II and III tumor groups. Cerebral blood volume (CBV) derived from dynamic susceptibility contrast (DSC) perfusion imaging was also compared with the HDI-derived perfusion fraction.
Results
The conventional apparent diffusion coefficient did not identify differences between WHO II and III tumor groups. HDI-derived slow hindered diffusion fraction was significantly elevated in the WHO III group as compared with the WHO II group. There was a non-significantly increasing trend of HDI-derived tumor cellularity fraction in the WHO III group, and both HDI-derived perfusion fraction and DSC-derived CBV were found to be significantly higher in the WHO III group. Both HDI-derived perfusion fraction and slow hindered diffusion fraction strongly correlated with DSC-derived CBV. Neither HDI-derived cellularity fraction nor HDI-derived fast hindered diffusion fraction correlated with DSC-derived CBV.
Conclusions
Conventional apparent diffusion coefficient, which measures averaged pathology properties of brain tumors, has compromised accuracy and specificity. HDI holds great promise to accurately separate and quantify the tumor cell fraction, the tumor cell packing density, edema, and capillary blood perfusion, thereby leading to an improved microenvironment characterization of primary brain tumors. Larger studies will further establish HDI’s clinical value and use for facilitating biopsy planning, treatment evaluation, and noninvasive tumor grading.
Klíčová slova:
Adenocarcinomas – Biopsy – Cancer detection and diagnosis – Diffusion magnetic resonance imaging – Fluid dynamics – Magnetic resonance imaging – Malignant tumors – Neuroimaging
Zdroje
1. Brain Tumor Statistics—ABTA 2018. Available from: http://abta.pub30.convio.net/about-us/news/brain-tumor-statistics/.
2. Choi YP, Shim HS, Gao MQ, Kang S, Cho NH. Molecular portraits of intratumoral heterogeneity in human ovarian cancer. Cancer Lett. 2011;307(1):62–71. doi: 10.1016/j.canlet.2011.03.018 21481528
3. Sundgren PC, Fan XY, Weybright P, Welsh RC, Carlos RC, Petrou M, et al. Differentiation of recurrent brain tumor versus radiation injury using diffusion tensor imaging in patients with new contrast-enhancing lesions. Magn Reson Imaging. 2006;24(9):1131–42. doi: 10.1016/j.mri.2006.07.008 17071335
4. Xu JL, Li YL, Lian JM, Dou SW, Yan FS, Wu H, et al. Distinction between postoperative recurrent glioma and radiation injury using MR diffusion tensor imaging. Neuroradiology. 2010;52(12):1193–9. doi: 10.1007/s00234-010-0731-4 20571787
5. Bulik M, Jancalek R, Vanicek J, Skoch A, Mechl M. Potential of MR spectroscopy for assessment of glioma grading. Clin Neurol Neurosurg. 2013;115(2):146–53. doi: 10.1016/j.clineuro.2012.11.002 23237636
6. Naser RKA, Hassan AAK, Shabana AM, Omar NN. Role of magnetic resonance spectroscopy in grading of primary brain tumors. Egypt J Radiol Nuc M. 2016;47(2):577–84.
7. Gupta PK, Saini J, Sahoo P, Patir R, Ahlawat S, Beniwal M, et al. Role of Dynamic Contrast-Enhanced Perfusion Magnetic Resonance Imaging in Grading of Pediatric Brain Tumors on 3T. Pediatr Neurosurg. 2017;52(5):298–305. doi: 10.1159/000479283 28848203
8. Yamasaki F, Kurisu K, Satoh K, Arita K, Sugiyama K, Ohtaki M, et al. Apparent diffusion coefficient of human brain tumors at MR imaging. Radiology. 2005;235(3):985–91. doi: 10.1148/radiol.2353031338 15833979
9. Maier SE, Sun Y, Mulkern RV. Diffusion imaging of brain tumors. NMR Biomed. 2010;23(7):849–64. doi: 10.1002/nbm.1544 20886568
10. Guo AC, Cummings TJ, Dash RC, Provenzale JM. Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology. 2002;224(1):177–83. doi: 10.1148/radiol.2241010637 12091680
11. Tomura N, Narita K, Izumi J, Suzuki A, Anbai A, Otani T, et al. Diffusion changes in a tumor and peritumoral tissue-after stereotactic irradiation for brain tumors: Possible prediction of treatment response. J Comput Assist Tomo. 2006;30(3):496–500.
12. Schmainda KM. Diffusion-weighted MRI as a biomarker for treatment response in glioma. CNS Oncol. 2012;1(2):169–80. doi: 10.2217/cns.12.25 23936625
13. Zhang H, Rodiger LA, Shen T, Miao J, Oudkerk M. Perfusion MR imaging for differentiation of benign and malignant meningiomas. Neuroradiology. 2008;50(6):525–30. doi: 10.1007/s00234-008-0373-y 18379768
14. Yoon RG, Kim HS, Hong GS, Park JE, Jung SC, Kim SJ, et al. Joint approach of diffusion- and perfusion-weighted MRI in intra-axial mass like lesions in clinical practice simulation. PLoS One. 2018;13(9):e0202891. doi: 10.1371/journal.pone.0202891 30192785
15. Groenendaal G, van den Berg CA, Korporaal JG, Philippens ME, Luijten PR, van Vulpen M, et al. Simultaneous MRI diffusion and perfusion imaging for tumor delineation in prostate cancer patients. Radiother Oncol. 2010;95(2):185–90. doi: 10.1016/j.radonc.2010.02.014 20231041
16. Wang Y, Sun P, Wang Q, Trinkaus K, Schmidt RE, Naismith RT, et al. Differentiation and quantification of inflammation, demyelination and axon injury or loss in multiple sclerosis. Brain. 2015;138(Pt 5):1223–38. doi: 10.1093/brain/awv046 25724201
17. Wang Y, Wang Q, Haldar JP, Yeh FC, Xie M, Sun P, et al. Quantification of increased cellularity during inflammatory demyelination. Brain. 2011;134(Pt 12):3590–601. doi: 10.1093/brain/awr307 22171354
18. Chiang CW, Wang Y, Sun P, Lin TH, Trinkaus K, Cross AH, et al. Quantifying white matter tract diffusion parameters in the presence of increased extra-fiber cellularity and vasogenic edema. Neuroimage. 2014;101:310–9. doi: 10.1016/j.neuroimage.2014.06.064 25017446
19. Murphy RK, Sun P, Xu J, Wang Y, Sullivan S, Gamble P, et al. Magnetic Resonance Imaging Biomarker of Axon Loss Reflects Cervical Spondylotic Myelopathy Severity. Spine (Phila Pa 1976). 2015.
20. Wang X, Cusick MF, Wang Y, Sun P, Libbey JE, Trinkaus K, et al. Diffusion basis spectrum imaging detects and distinguishes coexisting subclinical inflammation, demyelination and axonal injury in experimental autoimmune encephalomyelitis mice. NMR in biomedicine. 2014;27(7):843–52. doi: 10.1002/nbm.3129 24816651
21. Fouke SJ, Benzinger TL, Milchenko M, LaMontagne P, Shimony JS, Chicoine MR, et al. The comprehensive neuro-oncology data repository (CONDR): a research infrastructure to develop and validate imaging biomarkers. Neurosurgery. 2014;74(1):88–98. doi: 10.1227/NEU.0000000000000201 24089052
22. Milchenko M, Snyder AZ, LaMontagne P, Shimony JS, Benzinger TL, Fouke SJ, et al. Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research. Neuroinformatics. 2016;14(3):305–17. doi: 10.1007/s12021-016-9296-7 26910516
23. Hajnal JV, Saeed N, Soar EJ, Oatridge A, Young IR, Bydder GM. A Registration and Interpolation Procedure for Subvoxel Matching of Serially Acquired Mr-Images. J Comput Assist Tomo. 1995;19(2):289–96.
24. Rowland DJ, Garbow JR, Laforest R, Snyder AZ. Registration of [18F]FDG microPET and small-animal MRI. Nucl Med Biol. 2005;32(6):567–72. doi: 10.1016/j.nucmedbio.2005.05.002 16026703
25. Lee JJ, Bretthorst GL, Derdeyn CP, Powers WJ, Videen TO, Snyder AZ, et al. Dynamic susceptibility contrast MRI with localized arterial input functions. Magn Reson Med. 2010;63(5):1305–14. doi: 10.1002/mrm.22338 20432301
26. Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B. 1994;103(3):247–54. doi: 10.1006/jmrb.1994.1037 8019776
27. Anderson AW. Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magn Reson Med. 2005;54(5):1194–206. doi: 10.1002/mrm.20667 16161109
28. 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(2):401–7. doi: 10.1148/radiology.161.2.3763909 3763909
29. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30(9):1323–41. doi: 10.1016/j.mri.2012.05.001 22770690
30. Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature. 2013;501(7467):328–37. doi: 10.1038/nature12624 24048065
31. Nicholson C. Factors governing diffusing molecular signals in brain extracellular space. J Neural Transm (Vienna). 2005;112(1):29–44.
32. 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(6):1432–40. doi: 10.1002/mrm.20508 15906300
33. Van Cauter S, Veraart J, Sijbers J, Peeters RR, Himmelreich U, De Keyzer F, et al. Gliomas: diffusion kurtosis MR imaging in grading. Radiology. 2012;263(2):492–501. doi: 10.1148/radiol.12110927 22403168
34. Falk Delgado A, Nilsson M, van Westen D, Falk Delgado A. Glioma Grade Discrimination with MR Diffusion Kurtosis Imaging: A Meta-Analysis of Diagnostic Accuracy. Radiology. 2018;287(1):119–27. doi: 10.1148/radiol.2017171315 29206593
35. Bennett KM, Schmainda KM, Bennett R, Rowe DB, Lu HB, Hyde JS. Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magnet Reson Med. 2003;50(4):727–34.
36. Bai Y, Lin Y, Tian J, Shi D, Cheng J, Haacke EM, et al. Grading of Gliomas by Using Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MR Imaging and Diffusion Kurtosis MR Imaging. Radiology. 2016;278(2):496–504. doi: 10.1148/radiol.2015142173 26230975
37. Yeh FC, Wedeen VJ, Tseng WY. Generalized q-sampling imaging. IEEE Trans Med Imaging. 2010;29(9):1626–35. doi: 10.1109/TMI.2010.2045126 20304721
38. Taylor EN, Ding Y, Zhu S, Cheah E, Alexander P, Lin L, et al. Association between tumor architecture derived from generalized Q-space MRI and survival in glioblastoma. Oncotarget. 2017;8(26):41815–26. doi: 10.18632/oncotarget.16296 28404971
39. White NS, McDonald CR, Farid N, Kuperman JM, Kesari S, Dale AM. Improved conspicuity and delineation of high-grade primary and metastatic brain tumors using "restriction spectrum imaging": quantitative comparison with high B-value DWI and ADC. AJNR Am J Neuroradiol. 2013;34(5):958–64, S1. doi: 10.3174/ajnr.A3327 23139079
40. McDonald RJ, McDonald JS, Kallmes DF, Jentoft ME, Murray DL, Thielen KR, et al. Intracranial Gadolinium Deposition after Contrast-enhanced MR Imaging. Radiology. 2015;275(3):772–82. doi: 10.1148/radiol.15150025 25742194
41. Shen N, Zhao L, Jiang J, Jiang R, Su C, Zhang S, et al. Intravoxel incoherent motion diffusion-weighted imaging analysis of diffusion and microperfusion in grading gliomas and comparison with arterial spin labeling for evaluation of tumor perfusion. J Magn Reson Imaging. 2016;44(3):620–32. doi: 10.1002/jmri.25191 26880230
42. Silverman SG, Collick BD, Figueira MR, Khorasani R, Adams DF, Newman RW, et al. Interactive MR-guided biopsy in an open-configuration MR imaging system. Radiology. 1995;197(1):175–81. doi: 10.1148/radiology.197.1.7568819 7568819
43. Deng J, Virmani S, Yang GY, Tang R, Woloschak G, Omary RA, et al. Intraprocedural diffusion-weighted PROPELLER MRI to guide percutaneous biopsy needle placement within rabbit VX2 liver tumors. J Magn Reson Imaging. 2009;30(2):366–73. doi: 10.1002/jmri.21840 19629976
44. Yuan Y. Spatial Heterogeneity in the Tumor Microenvironment. Cold Spring Harb Perspect Med. 2016;6(8).
45. Xu JZ, Li H, Harkins KD, Jiang XY, Xie JP, Kang H, et al. Mapping mean axon diameter and axonal volume fraction by MRI using temporal diffusion spectroscopy. Neuroimage. 2014;103:10–9. doi: 10.1016/j.neuroimage.2014.09.006 25225002
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