IDH Status in Brain Gliomas Can Be Predicted by the Spherical Mean MRI Technique

References

1. 1.↵
   1. Di Carlo DT,
   2. Duffau H,
   3. Cagnazzo F, et al

   . IDH wild-type WHO grade II diffuse low-grade gliomas; a heterogeneous family with different outcomes: systematic review and meta-analysis. Neurosurg Rev 2020;43:383–95 doi:10.1007/s10143-018-0996-3 pmid:29943141

   CrossRefPubMed
2. 2.↵
   1. Louis DN,
   2. Perry A,
   3. Reifenberger G, et al

   . The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 2016;131:803–20 doi:10.1007/s00401-016-1545-1 pmid:27157931

   CrossRefPubMed
3. 3.↵
   1. Uetani H,
   2. Azuma M,
   3. Khant ZA, et al

   . Importance of age and noncontrast-enhancing tumor as biomarkers for isocitrate dehydrogenase-mutant glioblastoma: a multicenter study. J Comput Assist Tomogr 2023;47:659–65 doi:10.1097/RCT.0000000000001456 pmid:36877775

   CrossRefPubMed
4. 4.↵
   1. Jensen JH,
   2. Helpern JA,
   3. Ramani A, et al

   . Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005;53:1432–40 doi:10.1002/mrm.20508 pmid:15906300

   CrossRefPubMedWeb of Science
5. 5.↵
   1. Le Bihan D

   . Molecular diffusion nuclear magnetic resonance imaging. Magn Reson Q 1991;7:1–30 pmid:2043461

   PubMed
6. 6.↵
   1. Darbar A,
   2. Waqas M,
   3. Enam SF, et al

   . Use of preoperative apparent diffusion coefficients to predict brain tumor grade. Cureus 2018;10:e2284 doi:10.7759/cureus.2284 pmid:29740523

   CrossRefPubMed
7. 7.↵
   1. Zulfiqar M,
   2. Yousem DM,
   3. Lai H

   . ADC values and prognosis of malignant astrocytomas: does lower ADC predict a worse prognosis independent of grade of tumor? A meta-analysis. AJR Am J Roentgenol 2013;200:624–29 doi:10.2214/AJR.12.8679 pmid:23436853

   CrossRefPubMed
8. 8.↵
   1. Eidel O,
   2. Neumann JO,
   3. Burth S, et al

   . Automatic analysis of cellularity in glioblastoma and correlation with ADC using trajectory analysis and automatic nuclei counting. PLoS One 2016;11:e0160250 doi:10.1371/journal.pone.0160250 pmid:27467557

   CrossRefPubMed
9. 9.↵
   1. Gauvain KM,
   2. McKinstry RC,
   3. Mukherjee P, et al

   . Evaluating pediatric brain tumor cellularity with diffusion-tensor imaging. AJR Am J Roentgenol 2001;177:449–54 doi:10.2214/ajr.177.2.1770449 pmid:11461881

   CrossRefPubMedWeb of Science
10. 10.↵
    1. Abdalla G,
    2. Dixon L,
    3. Sanverdi E, et al

    . The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis. Neuroradiology 2020;62:791–802 doi:10.1007/s00234-020-02425-9 pmid:32367349

    CrossRefPubMed
11. 11.↵
    1. Szczepankiewicz F,
    2. Lasič S,
    3. van Westen D, et al

    . Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors. Neuroimage 2015;104:241–52 doi:10.1016/j.neuroimage.2014.09.057 pmid:25284306

    CrossRefPubMed
12. 12.↵
    1. Smith SM,
    2. Nichols TE,
    3. Vidaurre D, et al

    . A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci 2015;18:1565–67 doi:10.1038/nn.4125 pmid:26414616

    CrossRefPubMed
13. 13.↵
    1. Hagmann P,
    2. Grant PE,
    3. Fair DA

    . MR connectomics: a conceptual framework for studying the developing brain. Front Syst Neurosci 2012;6:43 doi:10.3389/fnsys.2012.00043 pmid:22707934

    CrossRefPubMed
14. 14.↵
    1. Kaden E,
    2. Kelm ND,
    3. Carson RP, et al

    . Multi-compartment microscopic diffusion imaging. Neuroimage 2016;139:346–59 doi:10.1016/j.neuroimage.2016.06.002 pmid:27282476

    CrossRefPubMed
15. 15.↵
    1. Kaden E,
    2. Kruggel F,
    3. Alexander DC

    . Quantitative mapping of the per-axon diffusion coefficients in brain white matter. Magn Reson Med 2016;75:1752–63 doi:10.1002/mrm.25734 pmid:25974332

    CrossRefPubMed
16. 16.↵
    1. By S,
    2. Xu J,
    3. Box BA, et al

    . Multi‐compartmental diffusion characterization of the human cervical spinal cord in vivo using the spherical mean technique. NMR Biomed 2018;31:e3894 doi:10.1002/nbm.3894 pmid:29388719

    CrossRefPubMed
17. 17.↵
    1. Tournier JD,
    2. Smith R,
    3. Raffelt D, et al

    . MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 2019;202:116137 doi:10.1016/j.neuroimage.2019.116137 pmid:31473352

    CrossRefPubMed
18. 18.↵
    1. Veraart J,
    2. Novikov DS,
    3. Christiaens D, et al

    . Denoising of diffusion MRI using random matrix theory. Neuroimage 2016;142:394–406 doi:10.1016/j.neuroimage.2016.08.016 pmid:27523449

    CrossRefPubMed
19. 19.↵
    1. Kellner E,
    2. Dhital B,
    3. Kiselev VG, et al

    . Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med 2016;76:1574–81 doi:10.1002/mrm.26054 pmid:26745823

    CrossRefPubMed
20. 20.↵
    1. Andersson JL,
    2. Sotiropoulos SN

    . An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 2016;125:1063–78 doi:10.1016/j.neuroimage.2015.10.019 pmid:26481672

    CrossRefPubMed
21. 21.↵
    1. Andersson JL,
    2. Skare S,
    3. Ashburner J

    . How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 2003;20:870–88 doi:10.1016/S1053-8119(03)00336-7 pmid:14568458

    CrossRefPubMedWeb of Science
22. 22.↵
    1. Tustison NJ,
    2. Avants BB,
    3. Cook PA, et al

    . N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 2010;29:1310–20 doi:10.1109/TMI.2010.2046908 pmid:20378467

    CrossRefPubMedWeb of Science
23. 23.↵
    1. Wang X,
    2. Zhou C,
    3. Wang L, et al

    . Motor cortex gliomas induces microstructural changes of large fiber tracts revealed by TBSS. Sci Rep 2020;10:16900–08 doi:10.1038/s41598-020-73746-1 pmid:33037275

    CrossRefPubMed
24. 24.↵
    1. Marino S,
    2. Menna G,
    3. Di Bonaventura R, et al

    . The extracellular matrix in glioblastomas: a glance at its structural modifications in shaping the tumoral microenvironment: a systematic review. Cancers (Basel) 2023;15:1879 doi:10.3390/cancers15061879 pmid:36980765

    CrossRefPubMed
25. 25.↵
    1. Henriques RN,
    2. Jespersen SN,
    3. Shemesh N

    . Microscopic anisotropy misestimation in spherical-mean single diffusion encoding MRI. Magn Reson Med 2019;81:3245–61 doi:10.1002/mrm.27606 pmid:30648753

    CrossRefPubMed
26. 26.↵
    1. Wang QP,
    2. Lei DQ,
    3. Yuan Y, et al

    . Accuracy of ADC derived from DWI for differentiating high-grade from low-grade gliomas: systematic review and meta-analysis. Medicine (Baltimore) 2020;99:e19254 doi:10.1097/MD.0000000000019254 pmid:32080132

    CrossRefPubMed
27. 27.↵
    1. Larochelle H,
    2. Ranzato M,
    3. Hadsell R,
    4. Balcan MF,
    5. Lin H

    1. Fadnavis S,
    2. Batson J,
    3. Garyfallidis E

    . 2020 Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds, Advances in Neural Information Processing Systems; vol. 33: 16293–303. Curran Associates, Inc
28. 28.↵
    1. Michailovich O,
    2. Rathi Y,
    3. Dolui S

    . Spatially regularized compressed sensing for high angular resolution diffusion imaging. IEEE Trans Med Imaging 2011;30:1100–15 doi:10.1109/TMI.2011.2142189 pmid:21536524

    CrossRefPubMed
29. 29.↵
    1. Mancini L,
    2. Casagranda S,
    3. Gautier G, et al

    . CEST MRI provides amide/amine surrogate biomarkers for treatment-naïve glioma sub-typing. Eur J Nucl Med Mol Imaging 2022;49:2377–91 doi:10.1007/s00259-022-05676-1 pmid:35029738

    CrossRefPubMed
30. 30.↵
    1. Eichinger P,
    2. Alberts E,
    3. Delbridge C, et al

    . Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas. Sci Rep 2017;7:13396 doi:10.1038/s41598-017-13679-4 pmid:29042619

    CrossRefPubMed
31. 31.↵
    1. Sakai Y,
    2. Yang C,
    3. Kihira S, et al

    . MRI radiomic features to predict IDH1 mutation status in gliomas: a machine learning approach using gradient tree boosting. Int J Mol Sci 2020;21:8004 doi:10.3390/ijms21218004 pmid:33121211

    CrossRefPubMed
32. 32.↵
    1. Zhang H,
    2. Schneider T,
    3. Wheeler-Kingshott CA, et al

    . NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 2012;61:1000–16 doi:10.1016/j.neuroimage.2012.03.072 pmid:22484410

    CrossRefPubMed