Molecular classification of brain tumor biopsies using solid-state magic angle spinning proton magnetic resonance spectroscopy and robust classifiers

International Journal of Oncology

Volumn 33, Issue 5, 2008, Pages 1017-1025

  Andronesi, Ovidiu C ^a,b   Blekas, Konstantinos D ^c   Mintzopoulos, Dionyssios ^a,b   Astrakas, Loukas ^a,c   Black, Peter M ^d   Tzika, A Aria ^a,b  

^a HARVARD MEDICAL SCHOOL   (United States)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

^c UNIVERSITY OF IOANNINA   (Greece)

^d BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

Brain CNS cancers; Ex vivo highresolution magic angle spinning magnetic resonance spectroscopy; Neural networks; Support vector machines; Tumor biomarkers

Indexed keywords

ADOLESCENT; ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN TUMOR; CLASSIFICATION ALGORITHM; CLASSIFIER; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; HUMAN; HUMAN TISSUE; INTERMETHOD COMPARISON; POSITIVE FEEDBACK; PRIORITY JOURNAL; PROTON NUCLEAR MAGNETIC RESONANCE; REDUNDANCY ANALYSIS; RELEVANCE VECTOR MACHINE; RELIABILITY; SENSITIVITY AND SPECIFICITY; SOLID STATE; SUPPORT VECTOR MACHINE; TUMOR BIOPSY; TUMOR CLASSIFICATION; ALGORITHM; BIOPSY; COMPARATIVE STUDY; METABOLISM; METHODOLOGY; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PATHOLOGY; PROGNOSIS; REPRODUCIBILITY; SIGNAL PROCESSING; SPIN LABELING;

PROTON; TUMOR MARKER;

ADOLESCENT; ADULT; ALGORITHMS; BIOPSY; BRAIN NEOPLASMS; HUMANS; MAGNETIC RESONANCE SPECTROSCOPY; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PROGNOSIS; PROTONS; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED; SPIN LABELS; TUMOR MARKERS, BIOLOGICAL; YOUNG ADULT;

EID: 58149185983     PISSN: 10196439     EISSN: 17912423     Source Type: Journal    
DOI: 10.3892/ijo_00000090     Document Type: Article

References (73)

1. Legler JM, Gloeckler Ries LA, Smith MA, et al: RESPONSE: re: brain and other central nervous system cancers: recent trends in incidence and mortality. J Natl Cancer Inst 92: 77-78, 2000.
2. Eichler AF and Loeffler JS: Multidisciplinary management of brain metastases. Oncologist 12: 884-898, 2007.
3. Jain RK, Di Tomaso E, Duda DG, Loeffler JS, Sorensen AG and Batchelor TT: Angiogenesis in brain tumours. Nat Rev Neurosci 8: 610-622, 2007.
4. Jain RK: Taming vessels to treat cancer. Sci Am 299: 56-63, 2008.
5. Packer RJ, Gajar A, Vezina G, et al: Phase III study of craniospinal radiation therapy followed by adjuvant chemotherapy for newly diagnosed average-risk medulloblastoma. J Clin Oncol 24: 4202-4208, 2006.
6. Merchant TE, Zhu Y, Thompson SJ, Sontag MR, Heideman RL and Kun LE: Preliminary results from a Phase II trail of conformal radiation therapy for pediatric patients with localised low-grade astrocytoma. and ependymoma. Int J Radiat Oncol Biol Phys 52: 325-332, 2002.
7. Louis DN, Ohgaki H, Wiestler OD and Caveneee WK: WHO classification of tumors of the central nervous system. In: World Health Organization Press, Geneva, Switzerland, 2007.
8. Louis DN, Holland EC and Cairncross JG: Glioma classification: a molecular reappraisal. Am J Pathol 159: 779-786, 2001.
9. Tzika AA: Proton magnetic resonance spectroscopic imaging as a cancer biomarker for pediatric brain tumors (Review). Int J Oncol 32: 517-526, 2008.
10. Cheng LL. Ma MJ, Becerra L, et al: Quantitative neuropathology by high resolution magic angle spinning proton magnetic resonance spectroscopy. Proc Natl Acad Sci USA 94: 6408-6413, 1997.
11. Cheng LL, Anthony DC, Comite AR, Black PM, Tzika AA and Gonzalez RG: Quantification of microheterogeneity in glioblastoma multiforme with ex vivo high-resolution magic-angle spinning (HRMAS) proton magetic resonance spectroscopy. Neuro-oncology 2: 87-95, 2000.
12. Tzika AA, Cheng LL, Goumnerova L, et al: Biochemical characterization of pediatric brain tumors by using in vivo and ex vivo magnetic resonance spectroscopy. J Neurosurg 96: 1023-1031, 2002.
13. Morvan D, Demidem A, Papon J, De Latour M and Madebriont JC: Melanoma tumors acquire a new phospholipid metabolism phenotype under cystemustine as revealed by high-resolution magic angle spinning proton nuclear magnetic resonance spectroscopy of intact tumor samples. Cancer Res 62: 1890-1897, 2002.
14. 1H MR spectra of a pediatric posterior fossa lesion. Int J Mol Med 16: 301-307, 2005.
15. 1H MR spectra. Int J Mol Med 18: 859-869, 2006.
16. Tzika AA, Astrakas L, Cao HH, et al: Combination of high-resolution magic angle spinning proton magnetic resonance spectroscopy and microscale genomics to type brain tumor biopsies. Int J Mol Med 20: 199-208, 2007.
17. Ryner LN, Sorenson JA and Thomas MA: 3D localized 2D NMR spectroscopy on an MRI scanner. J Magn Reson B 107: 126-137, 1995.
18. Thomas MA, Ryner LN, Mehta MP, Turski PA and Sorenson JA: Localized 2D J-resolved 1H MR spectroscopy of human brain tumors in vivo. J Magn Reson Imaging 6: 453-459, 1996.
19. Binesh N, Yue K, Fairbanks L and Thomas MA: Reproducibility of localized 2D correlated MR spectroscopy. Magn Reson Med 48: 942-948, 2002.
20. Thomas MA, Wyckoff N, Yue K, et al: Two-dimensional MR spectroscopic characterization of breast cancer in vivo. Technol Cancer Res Treat 4: 99- 106, 2005.
21. Hagberg G, Buzlina AP, Mader I, Roser W, Radue EW and Seelig J: In vivo proton MR spectroscopy of human gliomas: definition of metabolic coordinates for multi-dimensional classification. Magn Reson Med 34: 242-252, 1995.
22. Lisboa PJG, Kirby SPJ, Vellido A, Lee YYB and El-Deredy W: Assessment of statistical and neural networks, methods in NMR spectral classification and metabolite selection. NMR Biomed 11: 225-234, 1998.
23. Hagberg G: From magnetic resonance spectroscopy to classification of tumors: a review of pattern recognition methods. NMR Biomed 11: 148-156, 1998.
24. Poptani H, Kaartinen J, Gupta RK, Niemitz M, Hiltunen Y and Kauppinen RA: Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks. J Cancer Res Clin Oncol 125: 343-349, 1999.
25. Tate AR, Foxall PJ, Holmes E, et al: Distinction between normal and renal cell carcinoma kidney cortical biopsy samples using pattern recognition of (I)H magic angle spinning (MAS) NMR spectra. NMR Biomed 13: 64-71, 2000.
26. Majos C, Alonso J, Aguilera C, et al: Adult primitive neuroectodermal tumor: proton MR spectroscopic findings with possible application for differential diagnosis. Radiology 225: 556-566, 2002.
27. Huang Y, Lisboa PJG and El-Deredy W: Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection. Stat Med 22: 147-164, 2003.
28. Tate AR, Majos C, Moreno A, Howe FA, Griffiths JR and Arus C: Automated classification of short echo time in in vivo 1H brain tumor spectra: a multicenter study. Magn Reson Med 49: 29-36, 2003.
29. Devos A, Lukas L, Suykens JA, et al: Classification of brain tumours using short echo time 1H MR spectra. J Magn Reson 170: 164-175, 2004.
30. Tate AR, Underwood J, Acosta DM, et al: Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. NMR Biomed 19: 411-434, 2006.
31. Opstad KS, Ladroue C, Bell BA, Griffiths JR and Howe FA: Linear discriminant analysis of brain tumour (1)H MR spectra: a comparison of classification using whole spectra. versus metabolite quantification. NMR Biomed 20: 763-770, 2007.
32. Reynolds GM, Peet AC and Arvanitis TN: Generating prior probabilities for classifiers of brain tumours using belief networks. BMC Med Inform Decis Making 7: 27, 2007.
33. Poullet JB, Martinez-Bisbal MC, Valverde D, et al: Quantification and classification of high-resolution magic angle spinning data for brain tumor diagnosis. Conf Proc IEEE Eng Med Biol Soc 2007: 5407-5410, 2007.
34. Glotsos D, Spyridonos P, Cavouras D, Ravazoula P, Dadioti PA and Nikiforidis G: An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine. Med Inform Internet Med 30: 179-193, 2005.
35. Glotsos D, Tohka J, Ravazoula P, Cavouras D and Nikiforidis G: Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines. Int J Neural Syst 15: 1-11, 2005.
36. Georgiadis P, Cavouras D, Kalatzis I, et al: Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features. Comput Methods Programs Biomed 89: 24-32, 2008.
37. Lakhani SR and Ashworth A: Microarray and histopathological analysis of tumours: the future and the past? Nat Rev Cancer 1: 151-157, 2001.
38. Julia-Sape M, Acosta D, Majos C, et al: Comparison between neuroirnaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database. J Neurosurg 105: 6-14, 2006.
39. Andronesi OC, Mintzopoulos D, Struppe J, Black PM and Tzika AA: Solid-state NMR adiabatic TOBSY sequences provide enhanced sensitivity for multidimensional high-resolution magic-angle-spinning (1)H MR spectroscopy. J Magn Reson 193: 251-258, 2008.
40. Levitt MH: Advances in NMR. In: Encyclopedia of Nuclear Magnetic Resonance. Harris DMGaRK (ed). John Wiley & Sons, Ltd., Chichester, pp 165-196, 2002.
41. Baldus M, Iuliucci RJ and Meier BH: Probing through-bond connectivities and through-space distances in solids by magic-angle-spinning nuclear magnetic resonance. J Am Chem Soc 119: 1121-1124, 1997.
42. Andronesi OC, Becker S, Seidel K, Heise H, Young HS and Baidus M: Determination of membrane protein structure and dynamics by magic -angle-spinning solid-state NMR spectroscopy. J Am Chem Soc 127: 12965-12974, 2005.
43. Kupce E and Freeman R: Adiabatic pulses for wide-band inversion and broad-band decoupling. J Magn Reson A 115: 273-276, 1995.
44. Vapnik V: Statistical Learning Theory: Wiley-Interscience, 1998.
45. Hagan MT and Menhaj MB: Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5: 989-993, 1994.
46. Suykens J, van Gestel T, De Brabanter J, De Moor B and Vandewalle J: Least squares support vector machines. KU Leuven, Belgium, 2002.
47. Krogh A: What are artificial neural networks? Nat Biotechnol 26: 195-197, 2008.
48. Ding C and Peng H: Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3: 185-205, 2005.
49. Remy C, Arus C, Ziegler A, et al: In vivo, ex vivo, and in vivo one- and two-dimensional nuclear magnetic resonance spectroscopy of an intracerebral glioma in rat brain: assignment of resonance. J Neurochem 62: 166-179, 1994.
50. Florian C, Preece N, Bhakoo K and Williams S: Characteristic metabolic profiles revealed by 1H NMR spectroscopy for three types of human brain and nervous system tumours. NMR Biomed 8: 253-264, 1996.
51. Govindaraju V, Young K and Maudsley AA: Proton NMR chumical shifts and coupling constants for brain metabolites. NMR Biomed 13: 129-153, 2000.
52. Cheng LL, Chang IW, Louis DN and Gonzalez RG: Correlation of high-resolution magic angle spinning proton magnetic resonance spectroscopy with histopathology of intact human brain tumor specimens. Cancer Res 58: 1825-1832, 1998.
53. Astrakas LG, Zurakowski D, Tzika AA, et al: Noninvasive magnetic resonance spectroscopic imaging biomarkers to predict the clinical grade of pediatric brain tumors. Clin Cancer Res 10: 8220-8228, 2004.
54. Mischel PS and Cloughesy T: Using molecular information to guide brain tumor therapy. Nat Clin Pract Neurol 2: 232-233, 2006.
55. Golub TR, Slonim DK, Tamayo P, et al: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531-537, 1999.
56. Ramaswamy S, Tamayo P, Rifkin R. et al: Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 98: 15149-15154, 2001.
57. Yeang C, Ramaswamy S, Tamayo P, et al: Molecular classification of multiple tumor types-Bionformatics. 17: S316-S322, 2001.
58. Nutt CL, Mani DR, Betensky RA, et al: Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res 63: 1602-1607, 2003.
59. Griffin JL and Shockcor JP: Metabolic profiles of cancer cells. Nat Rev Cancer 4: 551-561, 2004.
60. Ramaswamy S. Ross KN, Lander ES and Golub TR: A molecular signature of metastasis in primary solid tumors. Nat Genet 33: 49-54, 2003.
61. Schwartz SA, Weil RJ, Johnson MD, Toms SA and Caprioli RM: Proteiri profiling in brain tumors using mass spectrometry: feasibility of a new technique for the analysis of protein expression. Clin Cancer Res 10: 981-987, 2004.
62. Kostka D and Spang R: Microarray based diagnosis profits from better documentation of gene expression signatures. PLoS Comput Biol 4: e22, 2008.
63. Hakumaki JM. Poptani H, Sandmair AM, Yla-Herttuala S and Kauppinen RA: 1H MRS detects polyunsaturated fatty acid accumulation during gene therapy of glioma: implications for the in vivo detection of apoptosis. Nat Med 5: 1323-1327, 1999.
64. Kinoshita Y, Kajiwara IP, Yokota A and Koga Y: Proton magnetic resonance spectroscopy of astrocytic tumors: an in vitro study. Neurol Med Chir (Tokyo) 33: 350-359, 1993.
65. Kinoshita Y, Kajiwara H, Yokota A and Roga Y: Proton magnetic resonance spectroscopy of brain tumors: an in vitro study. Neurosurgery 35: 606-613, 1994.
66. Moreno-Torres A, Martinez-Perez I, Baquero M, et al: Taurine detection by proton magnetic resonance spectroscopy in medulloblastoma: contribution to noninvasive differential diagnosis with cerebellar astrocytoma. Neurosurgery 55: 824-829, 2004.
67. Kinoshita Y and Yokota A: Absolute concentrations of metabolites in human brain tumors using in vitro proton magnetic resonance spectroscopy. NMR Biomed 10: 2-12, 1997.
68. Kovanlikaya A, Panigrahy A, Krieger MD, et al: Untreated pediatric primitive neuroectodermal tumor in vivo: quantitation of taurine with MR spectroscopy. Radiology 236: 1020-1025, 2005.
69. Chakravarti A, Tyndall E, Palanichamy K, Mehta M, Aldape K and Loeffler J: Impact of molecular profiling on clinical trial design for glioblastoma. Curr Oncol Rep 9: 71-79, 2007.
70. Tzika AA, Zarifi MK, Goumnerova L, et al: Neuroimaging in pediatric brain tumors: Gd-DTPA-enhanced, hemodynamic, and diffusion MR imaging compared with MR spectroscopic imaging. AJNR Am J Neuroradiol 23: 322-333, 2002.
71. Tzika AA, Astrakas LG, Zarifi MK, et al: Multiparametric MR assessment of pediatric brain tumors. Neuroradiology 45: 1-10, 2003.
72. Tzika AA, Astrakas LG, Zarifi MK, et al: Spectroscopic and perfusion magnetic resonance imaging predictors of progression in pediatric brain tumors. Cancer 100: 1246-1256, 2004.
73. Lee MC and Nelson SJ: Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy. Artif Intell Med 43: 61-74, 2008.