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Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma

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Abstract

Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann–Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors (\(p=0.005\)), with mean ADC of \(1.07\pm 0.16 \times 10^{-3}\) and \(1.23\pm 0.16\times 10^{-3}\; \mathrm{{mm^2/s}}\) for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation (\(\rho =-0.51\), \(p=0.001\)). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.

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References

  1. Barajas RF Jr, Hodgson JG, Chang JS, Vandenberg SR, Yeh RF, Parsa AT, McDermott MW, Berger MS, Dillon WP, Cha S (2010) Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic mr imaging 1. Radiology 254(2):564–576

    Article  PubMed  PubMed Central  Google Scholar 

  2. Bedard PL, Hansen AR, Ratain MJ, Siu LL (2013) Tumour heterogeneity in the clinic. Nature 501(7467):355–364

    Article  CAS  PubMed  Google Scholar 

  3. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300

    Google Scholar 

  4. Bleeker FE, Molenaar RJ, Leenstra S (2012) Recent advances in the molecular understanding of glioblastoma. J Neuro-oncol 108(1):11–27

    Article  CAS  Google Scholar 

  5. Carrillo J, Lai A, Nghiemphu P, Kim H, Phillips H, Kharbanda S, Moftakhar P, Lalaezari S, Yong W, Ellingson B et al (2012) Relationship between tumor enhancement, edema, idh1 mutational status, mgmt promoter methylation, and survival in glioblastoma. Am J Neuroradiol 33(7):1349–1355

    Article  CAS  PubMed  Google Scholar 

  6. Chang H, Han J, Borowsky A, Loss L, Gray JW, Spellman PT, Parvin B (2013) Invariant delineation of nuclear architecture in glioblastoma multiforme for clinical and molecular association. IEEE Trans Med Imaging 32(4):670–682

    Article  PubMed  Google Scholar 

  7. Chenevert TL, Ross BD (2009) Diffusion imaging for therapy response assessment of brain tumor. Neuroimaging Clin N Am 19(4):559–571

    Article  PubMed  PubMed Central  Google Scholar 

  8. Chenevert TL, McKeever PE, Ross BD (1997) Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging. Clin Cancer Res 3(9):1457–1466

    CAS  PubMed  Google Scholar 

  9. Chenevert TL, Stegman LD, Taylor JM, Robertson PL, Greenberg HS, Rehemtulla A, Ross BD (2000) Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst 92(24):2029–2036

    Article  CAS  PubMed  Google Scholar 

  10. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M et al (2013) The cancer imaging archive (tcia): maintaining and operating a public information repository. J Digital Imaging 26(6):1045–1057

    Article  Google Scholar 

  11. Colen RR, Vangel M, Wang J, Gutman DA, Hwang SN, Wintermark M, Jain R, Jilwan-Nicolas M, Chen JY, Raghavan P et al (2014) Imaging genomic mapping of an invasive mri phenotype predicts patient outcome and metabolic dysfunction: a tcga glioma phenotype research group project. BMC Med Genomics 7(1):30

    Article  PubMed  PubMed Central  Google Scholar 

  12. Crawford FW, Khayal IS, McGue C, Saraswathy S, Pirzkall A, Cha S, Lamborn KR, Chang SM, Berger MS, Nelson SJ (2009) Relationship of pre-surgery metabolic and physiological mr imaging parameters to survival for patients with untreated gbm. J Neuro-oncol 91(3):337–351

    Article  Google Scholar 

  13. Dabney AR (2005) Classification of microarrays to nearest centroids. Bioinformatics 21(22):4148–4154

    Article  CAS  PubMed  Google Scholar 

  14. Dabney AR, Storey JD (2007) Optimality driven nearest centroid classification from genomic data. PLoS One 2(10):e1002

    Article  PubMed  PubMed Central  Google Scholar 

  15. Di Costanzo A, Trojsi F, Giannatempo G, Vuolo L, Popolizio T, Catapano D, Bonavita S, d’Angelo V, Tedeschi G, Scarabino T (2006) Spectroscopic, diffusion and perfusion magnetic resonance imaging at 3.0 tesla in the delineation of glioblastomas: preliminary results. J Exp Clin Cancer Res 25(3):383–390

    CAS  PubMed  Google Scholar 

  16. Doucette T, Rao G, Rao A, Shen L, Aldape K, Wei J, Dziurzynski K, Gilbert M, Heimberger AB (2013) Immune heterogeneity of glioblastoma subtypes: extrapolation from the cancer genome atlas. Cancer Immunol Res 1(2):112–122

    Article  CAS  PubMed  Google Scholar 

  17. Egger J, Kapur T, Fedorov A, Pieper S, Miller JV, Veeraraghavan H, Freisleben B, Golby AJ, Nimsky C, Kikinis R (2013) Gbm volumetry using the 3d slicer medical image computing platform. Sci Rep 3:1364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. ElBanan MG, Amer AM, Zinn PO, Colen RR (2015) Imaging genomics of glioblastoma: state of the art bridge between genomics and neuroradiology. Neuroimaging Clin N Am 25(1):141–153

    Article  PubMed  Google Scholar 

  19. Ellingson B, Sahebjam S, Kim H, Pope W, Harris R, Woodworth D, Lai A, Nghiemphu P, Mason W, Cloughesy T (2014) Pretreatment adc histogram analysis is a predictive imaging biomarker for bevacizumab treatment but not chemotherapy in recurrent glioblastoma. Am J Neuroradiol 35(4):673–679

    Article  CAS  PubMed  Google Scholar 

  20. Ellingson BM (2015) Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics. Curr Neurol Neurosci Rep 15(1):1–12

    Article  CAS  Google Scholar 

  21. Fridman WH, Galon J, Pagès F, Tartour E, Sautès-Fridman C, Kroemer G (2011) Prognostic and predictive impact of intra-and peritumoral immune infiltrates. Cancer Res 71(17):5601–5605

    Article  CAS  PubMed  Google Scholar 

  22. Galons JP, Altbach MI, Paine-Murrieta GD, Taylor CW, Gillies RJ (1999) Early increases in breast tumor xenograft water mobility in response to paclitaxel therapy detected by non-invasive diffusion magnetic resonance imaging. Neoplasia 1(2):113–117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Guo, A.C., Cummings, T.J., Dash, R.C., Provenzale, J.M.: Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics 1. Radiology 224(1), 177–183 (2002)

    Article  PubMed  Google Scholar 

  24. Gupta RK, Cloughesy TF, Sinha U, Garakian J, Lazareff J, Rubino G, Rubino L, Becker DP, Vinters HV, Alger JR (2000) Relationships between choline magnetic resonance spectroscopy, apparent diffusion coefficient and quantitative histopathology in human glioma. J Neuro-oncol 50(3):215–226

    Article  CAS  Google Scholar 

  25. Gutman DA, Cooper LA, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD Jr, Scarpace L, Mikkelsen T, Jain R et al (2013) Mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set. Radiology 267(2):560–569

    Article  PubMed  PubMed Central  Google Scholar 

  26. Gutman DA, Dunn WD Jr, Grossmann P, Cooper LA, Holder CA, Ligon KL, Alexander BM, Aerts HJ (2015) Somatic mutations associated with mri-derived volumetric features in glioblastoma. Neuroradiology 57(12):1227–1237

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hall DE, Moffat BA, Stojanovska J, Johnson TD, Li Z, Hamstra DA, Rehemtulla A, Chenevert TL, Carter J, Pietronigro D et al (2004) Therapeutic efficacy of dti-015 using diffusion magnetic resonance imaging as an early surrogate marker. Clin Cancer Res 10(23):7852–7859

    Article  CAS  PubMed  Google Scholar 

  28. Hamstra DA, Chenevert TL, Moffat BA, Johnson TD, Meyer CR, Mukherji SK, Quint DJ, Gebarski SS, Fan X, Tsien CI et al (2005) Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci USA 102(46):16759–16764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hamstra DA, Galbán CJ, Meyer CR, Johnson TD, Sundgren PC, Tsien C, Lawrence TS, Junck L, Ross DJ, Rehemtulla A et al (2008) Functional diffusion map as an early imaging biomarker for high-grade glioma: correlation with conventional radiologic response and overall survival. J Clin Oncol 26(20):3387–3394

    Article  PubMed  PubMed Central  Google Scholar 

  30. Hayashida Y, Hirai T, Morishita S, Kitajima M, Murakami R, Korogi Y, Makino K, Nakamura H, Ikushima I, Yamura M et al (2006) Diffusion-weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. Am J Neuroradiol 27(7):1419–1425

    CAS  PubMed  Google Scholar 

  31. Henson JW, Gaviani P, Gonzalez RG (2005) Mri in treatment of adult gliomas. Lancet Oncol 6(3):167–175

    Article  PubMed  Google Scholar 

  32. Jackson RJ, Fuller GN, Abi-Said D, Lang FF, Gokaslan ZL, Shi WM, Wildrick DM, Sawaya R (2001) Limitations of stereotactic biopsy in the initial management of gliomas. Neuro-oncology 3(3):193–200

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Jolliffe I (2002) Principal component analysis. Wiley Online Library, Hoboken

    Google Scholar 

  34. Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, Wakasa K, Yamada R (2001) The role of diffusion-weighted imaging in patients with brain tumors. Am J Neuroradiol 22(6):1081–1088

    CAS  PubMed  Google Scholar 

  35. Lazovic J, Jensen MC, Ferkassian E, Aguilar B, Raubitschek A, Jacobs RE (2008) Imaging immune response in vivo: cytolytic action of genetically altered t cells directed to glioblastoma multiforme. Clin Cancer Res 14(12):3832–3839

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Le Bihan D, Douek P, Argyropoulou M, Turner R, Patronas N, Fulham M (1993) Diffusion and perfusion magnetic resonance imaging in brain tumors. Top Magn Reson Imaging 5(2):25–31

    Article  PubMed  Google Scholar 

  37. Lee KC, Moffat BA, Schott AF, Layman R, Ellingworth S, Juliar R, Khan AP, Helvie M, Meyer CR, Chenevert TL et al (2007) Prospective early response imaging biomarker for neoadjuvant breast cancer chemotherapy. Clin Cancer Res 13(2):443–450

    Article  CAS  PubMed  Google Scholar 

  38. Mardor Y, Roth Y, Lidar Z, Jonas T, Pfeffer R, Maier SE, Faibel M, Nass D, Hadani M, Orenstein A et al (2001) Monitoring response to convection-enhanced taxol delivery in brain tumor patients using diffusion-weighted magnetic resonance imaging. Cancer Res 61(13):4971–4973

    CAS  PubMed  Google Scholar 

  39. Mardor Y, Roth Y, Ocherashvilli A, Spiegelmann R, Tichler T, Daniels D, Maier SE, Nissim O, Ram Z, Baram J et al (2004) Pretreatment prediction of brain tumors response to radiation therapy using high b-value diffusion-weighted mri. Neoplasia 6(2):136–142

    Article  PubMed  PubMed Central  Google Scholar 

  40. McConville P, Hambardzumyan D, Moody JB, Leopold WR, Kreger AR, Woolliscroft MJ, Rehemtulla A, Ross BD, Holland EC (2007) Magnetic resonance imaging determination of tumor grade and early response to temozolomide in a genetically engineered mouse model of glioma. Clin Cancer Res 13(10):2897–2904

    Article  CAS  PubMed  Google Scholar 

  41. McLendon R, Friedman A, Bigner D, Van Meir EG, Brat DJ, Mastrogianakis GM, Olson JJ, Mikkelsen T, Lehman N, Aldape K et al (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455(7216):1061–1068

    Article  CAS  Google Scholar 

  42. Mischel PS, Shai R, Shi T, Horvath S, Lu KV, Choe G, Seligson D, Kremen TJ, Palotie A, Liau LM et al (2003) Identification of molecular subtypes of glioblastoma by gene expression profiling. Oncogene 22(15):2361–2373

    Article  CAS  PubMed  Google Scholar 

  43. Moton S, Elbanan M, Zinn PO, Colen RR (2015) Imaging genomics of glioblastoma: biology, biomarkers, and breakthroughs. Top Magn Reson Imaging 24(3):155–163

    Article  PubMed  Google Scholar 

  44. Naeini, K.M., Pope, W.B., Cloughesy, T.F., Harris, R.J., Lai, A., Eskin, A., Chowdhury, R., Phillips, H.S., Nghiemphu, P.L., Behbahanian, Y., et al.: Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images. Neuro-oncology 15, 626–634 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Nicholas S, Mathios D, Ruzevick J, Jackson C, Yang I, Lim M (2013) Current trends in glioblastoma multiforme treatment: radiation therapy and immune checkpoint inhibitors. Brain Tumor Res Treat 1(1):2–8

    Article  PubMed  PubMed Central  Google Scholar 

  46. Paldino M, Desjardins A, Friedman H, Vredenburgh J, Barboriak D (2014) A change in the apparent diffusion coefficient after treatment with bevacizumab is associated with decreased survival in patients with recurrent glioblastoma multiforme. Br J Radiol 85(1012):382–389

    Article  Google Scholar 

  47. Palucka K, Banchereau J (2012) Cancer immunotherapy via dendritic cells. Nat Rev Cancer 12(4):265–277

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Pardoll DM (2012) The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer 12(4):252–264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, Cahill DP, Nahed BV, Curry WT, Martuza RL et al (2014) Single-cell rna-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344(6190):1396–1401

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Pope W, Mirsadraei L, Lai A, Eskin A, Qiao J, Kim H, Ellingson B, Nghiemphu P, Kharbanda S, Soriano R et al (2012) Differential gene expression in glioblastoma defined by adc histogram analysis: relationship to extracellular matrix molecules and survival. Am J Neuroradiol 33(6):1059–1064

    Article  CAS  PubMed  Google Scholar 

  51. Pope WB (2015) Genomics of brain tumor imaging. Neuroimaging Clin N Am 25(1):105–119

    Article  PubMed  Google Scholar 

  52. Pope WB, Kim HJ, Huo J, Alger J, Brown MS, Gjertson D, Sai V, Young JR, Tekchandani L, Cloughesy T et al (2009) Recurrent glioblastoma multiforme: Adc histogram analysis predicts response to bevacizumab treatment 1. Radiology 252(1):182–189

    Article  PubMed  Google Scholar 

  53. Pope WB, Qiao XJ, Kim HJ, Lai A, Nghiemphu P, Xue X, Ellingson BM, Schiff D, Aregawi D, Cha S et al (2012) Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent gbm treated with bevacizumab: a multi-center study. J Neuro-oncol 108(3):491–498

    Article  CAS  Google Scholar 

  54. Prins RM, Soto H, Konkankit V, Odesa SK, Eskin A, Yong WH, Nelson SF, Liau LM (2011) Gene expression profile correlates with t-cell infiltration and relative survival in glioblastoma patients vaccinated with dendritic cell immunotherapy. Clin Cancer Res 17(6):1603–1615

    Article  CAS  PubMed  Google Scholar 

  55. Rao A, Rao G, Gutman DA, Flanders AE, Hwang SN, Rubin DL, Colen RR, Zinn PO, Jain R, Wintermark M et al (2015) A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma. J Neurosurg 67:1–10

    Google Scholar 

  56. Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304

    Article  PubMed  Google Scholar 

  57. Rolle CE, Sengupta S, Lesniak MS (2010) Challenges in clinical design of immunotherapy trials for malignant glioma. Neurosurg Clin N Am 21(1):201–214

    Article  PubMed  PubMed Central  Google Scholar 

  58. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  59. Ryken TC, Aygun N, Morris J, Schweizer M, Nair R, Spracklen C, Kalkanis SN, Olson JJ (2014) The role of imaging in the management of progressive glioblastoma. J Neuro-oncol 118(3):435–460

    Article  CAS  Google Scholar 

  60. Sadeghi N, D’Haene N, Decaestecker C, Levivier M, Metens T, Maris C, Wikler D, Balériaux D, Salmon I, Goldman S (2008) Apparent diffusion coefficient and cerebral blood volume in brain gliomas: relation to tumor cell density and tumor microvessel density based on stereotactic biopsies. Am J Neuroradiol 29(3):476–482

    Article  CAS  PubMed  Google Scholar 

  61. Saraswathy S, Crawford FW, Lamborn KR, Pirzkall A, Chang S, Cha S, Nelson SJ (2009) Evaluation of mr markers that predict survival in patients with newly diagnosed gbm prior to adjuvant therapy. J Neuro-oncol 91(1):69–81

    Article  Google Scholar 

  62. Schaefer PW, Grant PE, Gonzalez RG (2000) Diffusion-weighted mr imaging of the brain 1. Radiology 217(2):331–345

    Article  CAS  PubMed  Google Scholar 

  63. Schag CC, Heinrich RL, Ganz P (1984) Karnofsky performance status revisited: reliability, validity, and guidelines. J Clin Oncol 2(3):187–193

    CAS  PubMed  Google Scholar 

  64. Sims JS, Ung TH, Neira JA, Canoll P, Bruce JN (2015) Biomarkers for glioma immunotherapy: the next generation. J Neuro-oncol 123:359–372

    Article  CAS  Google Scholar 

  65. Stadnik TW, Chaskis C, Michotte A, Shabana WM, van Rompaey K, Luypaert R, Budinsky L, Jellus V, Osteaux M (2001) Diffusion-weighted mr imaging of intracerebral masses: comparison with conventional mr imaging and histologic findings. Am J Neuroradiol 22(5):969–976

    CAS  PubMed  Google Scholar 

  66. Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, Ludwin SK, Allgeier A, Fisher B, Belanger K et al (2009) Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase iii study: 5-year analysis of the eortc-ncic trial. Lancet Oncol 10(5):459–466

    Article  CAS  PubMed  Google Scholar 

  67. Sunwoo L, Choi SH, Park CK, Kim JW, Yi KS, Lee WJ, Yoon TJ, Song SW, Kim JE, Kim JY et al (2013) Correlation of apparent diffusion coefficient values measured by diffusion mri and mgmt promoter methylation semiquantitatively analyzed with ms-mlpa in patients with glioblastoma multiforme. J Magn Reson Imaging 37(2):351–358

    Article  PubMed  Google Scholar 

  68. Thomas AA, Fisher JL, Rahme GJ, Hampton TH, Baron U, Olek S, Schwachula T, Rhodes CH, Gui J, Tafe LJ et al (2015) Regulatory T cells are not a strong predictor of survival for patients with glioblastoma. Neuro-oncology 17(6):801–809

  69. Thomas G, Wang J, Mahmood Z, ElBanan MG, Zinn PO, Colen RR (2015) Diffusion imaging genomic mapping identifies genomic targets involved in invasion and poor prognosis. In: American Society of Newuroradiology 52th annual meeting

  70. Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc 63(2):411–423

    Article  Google Scholar 

  71. Van Elsas A, Hurwitz AA, Allison JP (1999) Combination immunotherapy of b16 melanoma using anti-cytotoxic t lymphocyte-associated antigen 4 (ctla-4) and granulocyte/macrophage colony-stimulating factor (gm-csf)-producing vaccines induces rejection of subcutaneous and metastatic tumors accompanied by autoimmune depigmentation. J Exp Med 190(3):355–366

    Article  PubMed  PubMed Central  Google Scholar 

  72. Vauléon E, Tony A, Hamlat A, Etcheverry A, Chiforeanu DC, Menei P, Mosser J, Quillien V, Aubry M (2012) Immune genes are associated with human glioblastoma pathology and patient survival. BMC Med Genomics 5(1):41

    Article  PubMed  PubMed Central  Google Scholar 

  73. Verhaak, R.G., Hoadley, K.A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M.D., Miller, C.R., Ding, L., Golub, T., Mesirov, J.P., et al.: Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in pdgfra, idh1, egfr, and nf1. Cancer Cell 17(1), 98–110 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Vezhnevets V, Konouchine V (2005) Growcut: interactive multi-label nd image segmentation by cellular automata. In: Proceedings of graphicon, citeseer, pp 150–156

  75. Vrabec M, Van Cauter S, Himmelreich U, Van Gool SW, Sunaert S, De Vleeschouwer S, Šuput D, Demaerel P (2011) Mr perfusion and diffusion imaging in the follow-up of recurrent glioblastoma treated with dendritic cell immunotherapy: a pilot study. Neuroradiology 53(10):721–731

    Article  PubMed  Google Scholar 

  76. Weller M, Cloughesy T, Perry JR, Wick W (2013) Standards of care for treatment of recurrent glioblastoma are we there yet? Neuro-oncology 15(1):4–27

    Article  PubMed  Google Scholar 

  77. Wen Q, Jalilian L, Lupo JM, Molinaro AM, Chang SM, Clarke J, Prados M, Nelson SJ (2015) Comparison of adc metrics and their association with outcome for patients with newly diagnosed glioblastoma being treated with radiation therapy, temozolomide, erlotinib and bevacizumab. J Neuro-oncol 121(2):331–339

    Article  CAS  Google Scholar 

  78. Zhang Z, Jiang H, Chen X, Bai J, Cui Y, Ren X, Chen X, Wang J, Zeng W, Lin S (2014) Identifying the survival subtypes of glioblastoma by quantitative volumetric analysis of mri. J Neuro-oncol 119(1):207–214

    Article  Google Scholar 

  79. Zinn PO, Hatami M, Colen RR (2015) Diffusion mri adc mapping of glioblastoma edema/tumor invasion and associated gene signatures. Neurosurgery 62:210

    Article  Google Scholar 

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Acknowledgments

Data used in this research were obtained from The Cancer Imaging Archive (TCIA) sponsored by the Cancer Imaging Program, DCTD/NCI/NIH. The results published here are in part based upon data generated by the The Cancer Genome Atlas (TCGA) Research Network: http://cancergenome.nih.gov/.

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  1. Scientific Informatics, Merck Research Laboratories, 470 Atlantic Avenue, Suite 305, Boston, MA, 02210, USA

    Guido H. Jajamovich, Chandni R. Valiathan & Sangeetha Somayajula

  2. Department of Genetics and Pharmacogenomics, Merck Research Laboratories, 33 Avenue Louis Pasteur, Boston, MA, 02115, USA

    Razvan Cristescu

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Jajamovich, G.H., Valiathan, C.R., Cristescu, R. et al. Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma. J Neurooncol 129, 289–300 (2016). https://doi.org/10.1007/s11060-016-2174-1

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