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MRI image analysis in patients with a tumor of the central nervous system : an attempt of developing a management algorithm

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PL
Analiza obrazu MRI u chorych z guzem ośrodkowego układu nerwowego : próba opracowania algorytmu postępowania
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EN
Magnetic resonance imaging study is currently the reference method for the detection and diagnosis of the central nervous system tumors. A large number of tumors, especially high-grade, has a higher water content in the cells, which results in prolongation of MRI T1 and T2 what appearance as increased signal intensity in in T2-weighted images and the reduction in T1-weighted images. MRI can be performed with administration of contrast agent, which shortens T1 and increases signal on T1-weighted sequences. This allows to identify areas of increased angiogenesis), which is the exponent of the cancer malignancy degree and its biological activity. Obtained MRI images are analyzed and evaluated by a radiologist and a clinician. Most of the time it is the "by the eye" analysis, which is based on the MRI image evaluation by the generally accepted radiological standards. However, this method is relatively inaccurate. which in turn can bring to the wrong diagnosis of the disease and implementation or even lack of implementation of appropriate treatment. More and more researches are conducted in this area, but developed methods are usually very complicated and difficult to carry out by the "layman" which is the clinician. That is why the attempt is made, to develop a simple and clear algorithm for MRI image analysis in patients with the central nervous system tumors, allowing for quick and objective evaluation of magnetic resonance imaging study.
PL
Badanie metodą rezonansu magnetycznego jest aktualnie metodą referencyjną przy wykrywaniu i diagnozowaniu nowotworów centralnego układu nerwowego. Duża część nowotworów, zwłaszcza o wysokim stopniu złośliwości, charakteryzuje się większą zawartością wody w komórkach, co w badaniu MRI skutkuje wydłużeniem T1 i T2, uwidocznionym jako nasilenie sygnału w obrazach T2-zależnych oraz jego obniżeniem w obrazach T1-zależnych. MRI można przeprowadzić z podaniem środka kontrastowego, co powoduje skrócenie czasu T1 i podniesienie
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  • Department of Neurotraumatology CM UMK
Bibliografia
  • [1] Aksoy F.G., Lev M.H., 2000. Dynamic Contrast − Enhanced Brain Perfusion Imaging: Technique and Clinical Applications. Sem Ultrasound CT MRI, 21 (6), pp. 462-477.
  • [2] Al-Okaili R.N., Krejza J., Woo J.H., Wolf R.L., O’Rourke D.M., Judy K.D., Poptani H., Melhem E.R., 2007. Intraaxial brain masses: MR imaging–based diagnostic strategy – initial experience. Radiology 243, pp. 539-550.
  • [3] Aronen H.J., Gazit I.E., Louis D.N., Buchbinder B.R., Pardo F.S., Weisskoff R.M., Griffith R.H., Cosgrove G.R., Halpern E.F., Hochberg F.H., Rosen B.R., 1994. Cerebral Blood Volume Maps of Gliomas: Comparison with Tumor Grade and Histologic Findings. Radiology 191, pp. 41-51.
  • [4] Brem S., Cotran R., Folkman J., 1972. Tumor angiogenesis: a quantitative method for histologic grading. J Natl Cancer Inst 48, pp. 347-356.
  • [5] Emblem K.E., Zoellner F.G., Tennoe B., Nedregaard B., Nome T., Due- Tonnessen P., Hald J.K., Scheie D., Bjornerud A., 2008. Predictive modeling in glioma grading from MR perfusion images using support vector machines. Magn Reson Med 60, pp. 945-952.
  • [6] Folkman J., 1992. The role of angiogenesis in tumor growth. Semin Cancer Biol, 3, pp. 65-71.
  • [7] Folkman J., 1990. What is the evidence that tumors are angiogenesis dependent? J Natl Cancer Inst 82, pp. 4-6.
  • [8] Ginsberg L., Fuller G., Schomer D., Kau B.A., Kispert D.B., 1996. Does lack of enhancement of brain tumors on MR
imaging correlate with low grade malignancy? A histopathologic study. [In:] Proceedings of the American Society 
of Neuroradiology, Seattle–Washington, pp. 32-33.
  • [9] Glotsos D., Tohka J., Ravazoula P., Cavouras D., Nikiforidis G., 2005. Automated diagnosis of brain tumors astrocytomas using probabilistic neural network clustering and support vector machines. Int J Neural Syst 15, pp. 1-11.
  • [10] Greenberg M.S., Arredonto N., 2010. Handbook of neurosurgery – 7th edition, Thieme.
  • [11] Higano S., Yun X., Kumabe T., Watanabe M., Mugikura S., Umetsu A., Sato A., Yamada T., Takahashi S., 2006. Malignant astrocytic tumors: clinical im- portance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology 241, pp. 839-846.
  • [12] Huang Y., Lisboa P.J.G., El-Deredy W., 2003. Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection. Stat Med 22, pp. 147-164.
  • [13] Knopp E.A., Cha S., Johnson G., Mazumdar A., Golfinos J.G., Zagzag D., Miller D.C., Kelly P.J., Kricheff I.I., 1999. Glial Neoplasms: Dynamic Contrast − Enhanced T2* − weighted MR Imaging. Radiology 211, pp. 791-798.
  • [14] Kremer S., Grand S., Remy C., Esteve F., Lefournier V., Pasquier B., Hoffmann D., Benabid A.L., Le Bas J.F., 2002. Cerebral blood volume mapping by MR imaging in the initial evaluation of brain tumors. J Neuroradiol 29, pp. 105-113.
  • [15] Law M., Cha S., Knopp E. A., Johnson G., Arnett J., Litt A.W., 2002. High−Grade Gliomas and Solitary metastases: Diffe − rentiation by Using Perfusion and Proton Spectroscopic MR Imaging. Radiology 222, pp. 715-721.
  • [16] Lev M.H., Hochberg F., 1998. Perfusion Magnetic Resonance Imaging to Assess Brain Tumor Responses to New Therapies. Cancer Control 5 (2), pp. 115-123.
  • [17] Lev M.H., Ozsunar Y., Henson J.W., Rasheed A.A., Barest G.D., Harsh G.R., Fitzek M.M., Chiocca E.A., Rabinov J.D., Csavoy A.N., Rosen B.R., Hochberg F.H., Schaefer P.W., Gonzalez R.G., 2004. Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping com- pared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas. Am J Neuroradiol 25, pp. 214-221.
  • [18] Liberski P.P., Kozubski W., Biernat W., Kordek R., 2011. Neuroonkologia kliniczna. Wyd. Czelej.
  • [19] Louis D.N., Ohgaki H., Wiestler O.D., Cavenee W.K., Burger P.C., Jouvet A., Scheithauer B.W., 2007. The 2007 WHO Classification of Tumours of the Central Nervous System. Springer-Verlag.
  • [20] Lüdemann L., Grieger W., Wurm R., Budzisch M., Hamm B., Zimmer C., 2001. Comparison of dynamic contrast−enhanced MRI with WHO tumor grading for gliomas. Eur Radiol 11, pp. 1231-1241.
  • [21] Majo ́s C., Julia`-Sape ́ M., Alonso J., Serrallonga M., Aguilera C., Acebes J.J., Aru ́s C., Gili J., 2004. Brain tumor classification by proton MR spectroscopy: comparison of diagnostic accuracy at short and long TE. Am J Neuro-radiol 25, pp. 1696-1704.
  • [22] Melhem E.R., Davatzikos C., 2008. Multi-parametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images. Acad Radiol 15, pp. 966-977.
  • [23] Østergaard L., Hochberg F.H., Rabinov J.D., Sorensen A.G., Lev M., Kim L., Weisskoff R.M., Gonzalez R.G., Gyldensted C., Rosen B.R., 1999. Early changes measured by magnetic resonance imaging in cerebral blood flow, blood volume, and blood–brain barrier permeability following dexamethasone treatment in patients with brain tumors. J Neurosurg 90, pp. 300-305.
  • [24]Principi M., Italiani M., Guiducci A., Aprile I., Muti M., Giulianelli G., Ottoviano P., 2003. Perfusion MRI in the evaluation of the relationship between tumour growth, necrosis and angiogenesis in glioblastomas and grade 1 meningiomas. Neuroradiology 45, pp. 205-211.
  • [25] Provenzale J.M., Mukundan S., Baroriak D.P., 2006. Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment. Radiology 239, pp. 632-649.
  • [26] Provenzale J.M., Mukundan S., Baroriak D.P., 2006. Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology 239, pp. 632-649.
  • [27] Provenzale J.M., Wang G.R., Brenner T., Petrella J.R., Sorensen A.G., 2002. Comparison of Permeability in High−Grade and Low−Grade Brain Tumors Using Dynamic Susceptibility Contrast MR Imaging. Am J Roentgenol 178, pp. 711-716.
  • [28] Roberts H.C., Roberts T.P.L., Brasch R.C., Dillon W.P., 2000. Quantitative measurement of microvascular permeability in human brain tumors achieved using dynamic contrast − enhanced MR imaging: correlation with histologic grade. Am J Neuroradiol 21, pp. 891-899.
  • [29] Roberts H.C., Roberts T.P.L., Lee T.Y., Dillon W.P., 2002. Dynamic Contrast − Enhanced CT of Human Brain Tumors: 
Quantitative Assessment of Blood Volume, Blood Flow, and Microvascular Permeability: Report of Two Cases. Am J Neuroradiol 23, pp. 828-832.
  • [30] Siegal T., Rubinstein R.I., Tzuk-Shina T., Gomori J.M., 1997. Utility of relative cerebral blood volume mapping derived from perfusion magnetic resonance imaging in the routine follow up of brain tumors. J Neurosurg 86, pp. 22-27.
  • [31] Smith S.M., Jenkinson M., Woolrich M.W., Beckmann C.F., Behrens T.E., Johansen-Berg H., Bannister P.R., De Luca M., Drobnjak I., Flitney D.E., Niazy R.K., Saunders J., Vickers J., Zhang Y., De Stefano N., Brady J.M., Matthews P.M., 2004. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 (suppl 1).
  • [32] Sugahara T., Korogi Y., Kochi M., Ikushima I., Hirai T., Okuda T., Shigematsu Y., Liang L., Ge Y., Ushio Y., Takahashi M., 1998. Correlation of MR Imaging − Determinated Cerebral Blood Maps with Histologic and Angiographic Determination of Vascularity of Gliomas. Am J Roengenol 171, pp. 1479-1486.
  • [33] Walecki J., Chojnacka E., 2007. Diagnostyka obrazowa guzów wewnątrz-czaszkowych – część I – guzy neuroepitelialne. Onkologia w praktyce klinicznej, tom 3, nr 4, pp. 177-197.
  • [34] Weber M.A., Zoubaa S., Schlieter M., Juttler E., Huttner H.B., Geletneky K., Ittrich C., Lichy M.P., Kroll A., Debus J., Giesel F.L., Hartmann M., Essig M., 2006. Diagnostic performance of spectroscopic and perfusion MRI for distinc- tion of brain tumors. Neurology 66, pp. 1899-1906.
  • [35] Young R.J., Knopp E.A., 2006. Brain MRI: tumor evaluation. J Magn Reson Imaging 24, pp. 709-724.
  • [36] Zacharaki E.I., Wang S., Chawla S., Wolf R., Melhem E.R., Davatzikos C., 2009. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magnetic Resonance in Medicine 62, pp. 1609-1618.
  • [37] Zimny A., Sąsiadek M., 2005. Badania perfuzyjne TK i MR – nowe narzędzie w diagnostyce guzów wewnątrzczaszkowych. Adv Clin Exp Med. 14; 3, pp. 583-592.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT1-0041-0046
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