MRI Features May Predict Molecular Features of Glioblastoma in Isocitrate Dehydrogenase Wild-Type Lower-Grade Gliomas

References

1. 1.↵
   1. Yan H,
   2. Parsons DW,
   3. Jin G, et al

   . IDH1 and IDH2 mutations in gliomas. N Engl J Med 2009;360:765–73 doi:10.1056/NEJMoa0808710 pmid:19228619

   CrossRefPubMedWeb of Science
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. Jiao Y,
   2. Killela PJ,
   3. Reitman ZJ, et al

   . Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the classification of malignant gliomas. Oncotarget 2012;3:709–22 doi:10.18632/oncotarget.588 pmid:22869205

   CrossRefPubMedWeb of Science
4. 4.↵
   1. Brat DJ,
   2. Verhaak RG,
   3. Aldape KD, et al

   ; Cancer Genome Atlas Research Network. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 2015;372:2481–98 doi:10.1056/NEJMoa1402121 pmid:26061751

   CrossRefPubMed
5. 5.↵
   1. Metellus P,
   2. Coulibaly B,
   3. Colin C, et al

   . Absence of IDH mutation identifies a novel radiologic and molecular subtype of WHO grade II gliomas with dismal prognosis. Acta Neuropathol 2010;120:719–29 doi:10.1007/s00401-010-0777-8 pmid:21080178

   CrossRefPubMedWeb of Science
6. 6.↵
   1. Eckel-Passow JE,
   2. Lachance DH,
   3. Molinaro AM, et al

   . Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 2015;372:2499–508 doi:10.1056/NEJMoa1407279 pmid:26061753

   CrossRefPubMed
7. 7.↵
   1. Aibaidula A,
   2. Chan AK,
   3. Shi Z, et al

   . Adult IDH wild-type lower-grade gliomas should be further stratified. Neuro Oncol 2017;19:1327–37 doi:10.1093/neuonc/nox078 pmid:28575485

   CrossRefPubMed
8. 8.↵
   1. Chan AK,
   2. Yao Y,
   3. Zhang Z, et al

   . TERT promoter mutations contribute to subset prognostication of lower-grade gliomas. Mod Pathol 2015;28:177–86 doi:10.1038/modpathol.2014.94 pmid:25081751

   CrossRefPubMed
9. 9.↵
   1. Chan AK,
   2. Yao Y,
   3. Zhang Z, et al

   . Combination genetic signature stratifies lower-grade gliomas better than histological grade. Oncotarget 2015;6:20885–901 doi:10.18632/oncotarget.4928 pmid:26369702

   CrossRefPubMed
10. 10.↵
    1. Brat DJ,
    2. Aldape K,
    3. Colman H, et al

    . cIMPACT-NOW update 3: recommended diagnostic criteria for “Diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV.” Acta Neuropathol 2018;136:805–10 doi:10.1007/s00401-018-1913-0 pmid:30259105

    CrossRefPubMed
11. 11.↵
    1. Gutman DA,
    2. Cooper LA,
    3. Hwang SN, et al

    . MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 2013;267:560–69 doi:10.1148/radiol.13120118 pmid:23392431

    CrossRefPubMedWeb of Science
12. 12.↵
    1. Gutman DA,
    2. Dunn WD,
    3. Grossmann P, et al

    . Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology 2015;57:1227–37 doi:10.1007/s00234-015-1576-7 pmid:26337765

    CrossRefPubMed
13. 13.↵
    1. Park YW,
    2. Han K,
    3. Ahn SS, et al

    . Prediction of IDH1-mutation and 1p/19q-codeletion status using preoperative MR imaging phenotypes in lower grade gliomas. AJNR Am J Neuroradiol 2018;39:37–42 doi:10.3174/ajnr.A5421 pmid:29122763

    Abstract/FREE Full Text
14. 14.↵
    1. Gillies RJ,
    2. Kinahan PE,
    3. Hricak H

    . Radiomics: images are more than pictures, they are data. Radiology 2016;278:563–77 doi:10.1148/radiol.2015151169 pmid:26579733

    CrossRefPubMed
15. 15.↵
    1. Aerts HJ,
    2. Velazquez ER,
    3. Leijenaar RT, et al

    . Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006 doi:10.1038/ncomms5006 pmid:24892406

    CrossRefPubMed
16. 16.↵
    1. Molina D,
    2. Pérez-Beteta J,
    3. Martínez-González A, et al

    . Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images. Comput Biol Med 2016;78:49–57 doi:10.1016/j.compbiomed.2016.09.011 pmid:27658261

    CrossRefPubMed
17. 17.↵
    1. Li Y,
    2. Liu X,
    3. Xu K, et al

    . MRI features can predict EGFR expression in lower grade gliomas: a voxel-based radiomic analysis. Eur Radiol 2018;28:356–62 doi:10.1007/s00330-017-4964-z pmid:28755054

    CrossRefPubMed
18. 18.↵
    1. Li Y,
    2. Qian Z,
    3. Xu K, et al

    . MRI features predict p53 status in lower-grade gliomas via a machine-learning approach. Neuroimage Clin 2018;17:306–11 doi:10.1016/j.nicl.2017.10.030 pmid:29527478

    CrossRefPubMed
19. 19.↵
    1. Li Y,
    2. Qian Z,
    3. Xu K, et al

    . Radiomic features predict Ki-67 expression level and survival in lower grade gliomas. J Neurooncol 2017;135:317–24 doi:10.1007/s11060-017-2576-8 pmid:28900812

    CrossRefPubMed
20. 20.↵
    1. Pedano N,
    2. Flanders A,
    3. Scarpace L, et al

    . Radiology data from the Cancer Genome Atlas low grade glioma [TCGA-LGG] collection. The Cancer Imaging Archive 2016 doi:10.7937/K9/TCIA.2016.L4LTD3TK

    CrossRef
21. 21.↵
    Wiki for the VASARI feature set. The National Cancer Institute Website. https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project. Updated March 30, 2020. Accessed September 17, 2019
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. Sled JG,
    2. Zijdenbos AP,
    3. Evans AC

    . A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998;17:87–97 doi:10.1109/42.668698 pmid:9617910

    CrossRefPubMedWeb of Science
24. 24.↵
    1. Shinohara RT,
    2. Sweeney EM,
    3. Goldsmith J, et al

    . Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin 2014;6:9–19 doi:10.1016/j.nicl.2014.08.008 pmid:25379412

    CrossRefPubMed
25. 25.↵
    1. van Griethuysen JJ,
    2. Fedorov A,
    3. Parmar C, et al

    . Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77:e104–07 doi:10.1158/0008-5472.CAN-17-0339 pmid:29092951

    Abstract/FREE Full Text
26. 26.↵
    1. Takano S,
    2. Tian W,
    3. Matsuda M, et al

    . Detection of IDH1 mutation in human gliomas: comparison of immunohistochemistry and sequencing. Brain Tumor Pathol 2011;28:115–23 doi:10.1007/s10014-011-0023-7 pmid:21344322

    CrossRefPubMedWeb of Science
27. 27.↵
    1. Choi J,
    2. Lee EY,
    3. Shin KJ, et al

    . IDH1 mutation analysis in low cellularity specimen: a limitation of diagnostic accuracy and a proposal for the diagnostic procedure. Pathol Res Pract 2013;209:284–90 doi:10.1016/j.prp.2013.02.010 pmid:23561624

    CrossRefPubMed
28. 28.↵
    1. Sahm F,
    2. Schrimpf D,
    3. Jones DTW, et al

    . Next-generation sequencing in routine brain tumor diagnostics enables an integrated diagnosis and identifies actionable targets. Acta Neuropathol 2016;131:903–10 doi:10.1007/s00401-015-1519-8 pmid:26671409

    CrossRefPubMed
29. 29.↵
    1. Na K,
    2. Kim HS,
    3. Shim HS, et al

    . Targeted next-generation sequencing panel (TruSight Tumor 170) in diffuse glioma: a single institutional experience of 135 cases. J Neurooncol 2019;142:445–54 doi:10.1007/s11060-019-03114-1 pmid:30710203

    CrossRefPubMed
30. 30.↵
    1. Friedman J,
    2. Hastie T,
    3. Tibshirani R

    . Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010;33:1–22 pmid:20808728

    CrossRefPubMedWeb of Science
31. 31.↵
    1. Dormann CF,
    2. Elith J,
    3. Bacher S, et al

    . Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 2013;36:27–46 doi:10.1111/j.1600-0587.2012.07348.x

    CrossRefPubMedWeb of Science
32. 32.↵
    1. Kuhn M

    . Building predictive models in R using the caret package. J Stat Soft 2008;28 doi:10.18637/jss.v028.i05

    CrossRef
33. 33.↵
    1. Landis JR,
    2. Koch GG

    . The measurement of observer agreement for categorical data. Biometrics 1977;33:159–74 pmid:843571

    CrossRefPubMedWeb of Science
34. 34.↵
    1. DeLong ER,
    2. DeLong DM,
    3. Clarke-Pearson DL

    . Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–45 pmid:3203132

    CrossRefPubMedWeb of Science
35. 35.↵
    1. Bale TA,
    2. Jordan JT,
    3. Rapalino O, et al

    . Financially effective test algorithm to identify an aggressive, EGFR-amplified variant of IDH-wildtype, lower-grade diffuse glioma. Neuro Oncol 2019;21:596–605 doi:10.1093/neuonc/noy201 pmid:30496526

    CrossRefPubMed
36. 36.↵
    1. Weller M,
    2. Weber RG,
    3. Willscher E, et al

    . Molecular classification of diffuse cerebral WHO grade II/III gliomas using genome- and transcriptome-wide profiling improves stratification of prognostically distinct patient groups. Acta Neuropathol 2015;129:679–93 doi:10.1007/s00401-015-1409-0 pmid:25783747

    CrossRefPubMed
37. 37.↵
    1. Wijnenga MM,
    2. Dubbink HJ,
    3. French PJ, et al

    . Molecular and clinical heterogeneity of adult diffuse low-grade IDH wild-type gliomas: assessment of TERT promoter mutation and chromosome 7 and 10 copy number status allows superior prognostic stratification. Acta Neuropathol 2017;134:957–59 doi:10.1007/s00401-017-1781-z pmid:29052002

    CrossRefPubMed
38. 38.↵
    1. Aoki K,
    2. Nakamura H,
    3. Suzuki H, et al

    . Prognostic relevance of genetic alterations in diffuse lower-grade gliomas. Neuro Oncol 2018;20:66–77 doi:10.1093/neuonc/nox132 pmid:29016839

    CrossRefPubMed
39. 39.↵
    1. Stichel D,
    2. Ebrahimi A,
    3. Reuss D, et al

    . Distribution of EGFR amplification, combined chromosome 7 gain and chromosome 10 loss, and TERT promoter mutation in brain tumors and their potential for the reclassification of IDH wild type astrocytoma to glioblastoma. Acta Neuropathol 2018;136:793–803 doi:10.1007/s00401-018-1905-0 pmid:30187121

    CrossRefPubMed
40. 40.↵
    1. Gupta A,
    2. Young RJ,
    3. Shah AD, et al

    . Pretreatment dynamic susceptibility contrast MRI perfusion in glioblastoma: prediction of EGFR gene amplification. Clin Neuroradiol 2015;25:143–50 doi:10.1007/s00062-014-0289-3 pmid:24474262

    CrossRefPubMed
41. 41.↵
    1. Ivanidze J,
    2. Lum M,
    3. Pisapia D, et al

    . MRI features associated with TERT promoter mutation status in glioblastoma. J Neuroimaging 2019;29:357–63 doi:10.1111/jon.12596 pmid:30644143

    CrossRefPubMed
42. 42.↵
    1. Akbari H,
    2. Bakas S,
    3. Pisapia JM, et al

    . In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro Oncol 2018;20:1068–79 doi:10.1093/neuonc/noy033 pmid:29617843

    CrossRefPubMed
43. 43.↵
    1. Bakas S,
    2. Akbari H,
    3. Pisapia J, et al

    . In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the φ-Index. Clin Cancer Res 2017;23:4724–34 doi:10.1158/1078-0432.CCR-16-1871 pmid:28428190

    Abstract/FREE Full Text
44. 44.↵
    1. Liu X,
    2. Li Y,
    3. Qian Z, et al

    . A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. Neuroimage Clin 2018;20:1070–77 doi:10.1016/j.nicl.2018.10.014 pmid:30366279

    CrossRefPubMed
45. 45.↵
    1. Jiang C,
    2. Kong Z,
    3. Zhang Y, et al

    . Conventional magnetic resonance imaging-based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas. Neuroradiology 2020;62:803–13 doi:10.1007/s00234-020-02392-1 pmid:32239241

    CrossRefPubMed
46. 46.↵
    1. Kros JM

    . Grading of gliomas: the road from eminence to evidence. J Neuropathol Exp Neurol 2011;70:101–09 doi:10.1097/NEN.0b013e31820681aa pmid:21343878

    CrossRefPubMed
47. 47.↵
    1. Prayson RA,
    2. Agamanolis DP,
    3. Cohen ML, et al

    . Interobserver reproducibility among neuropathologists and surgical pathologists in fibrillary astrocytoma grading. J Neurol Sci 2000;175:33–39 doi:10.1016/s0022-510x(00)00274-4 pmid:10785254

    CrossRefPubMed
48. 48.↵
    1. Vigneswaran K,
    2. Neill S,
    3. Hadjipanayis CG

    . Beyond the World Health Organization grading of infiltrating gliomas: advances in the molecular genetics of glioma classification. Ann Transl Med 2015;3:95 doi:10.3978/j.issn.2305-5839.2015.03.57 pmid:26015937

    CrossRefPubMed