Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
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Clinical Manifestations and Imaging Characteristics of Gliomatosis Cerebri with Pathological Confirmation

  • Zhang, Chun-Pu (Department of Neurosurgery, Affiliated Hospital of Taishan Medical University) ;
  • Li, Hua-Qing (ICU, Affiliated Hospital of Taishan Medical University) ;
  • Zhang, Wei-Tao (Department of Otolaryngology, Central Hospital, Shangdong Energy Feicheng Mining Group Co. Ltd.) ;
  • Liu, Ming-Hui (Central Hospital, Shangdong Energy Feicheng Mining Group Co. Ltd.) ;
  • Pan, Wen-Jing (ICU, Affiliated Hospital of Taishan Medical University)
  • Published : 2014.06.15
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Abstract

Objective: To explore the clinical manifestations and imaging characteristics of gliomatosis cerebri to raise the awareness and improve its diagnostic accuracy for patients. Materials and Methods: Clinical data, imaging characteristics and pathological examination of 12 patients with GC from Jan., 2008 to Jan., 2012 were analyzed retrospectively. Results: Patients with GC were clinically manifested with headache, vomiting, repeated seizures, fatigue and unstable walking, most of whom had more than 2 lesions involving in parietal lobe, followed by temporal lobe, frontal lobe, periventricular white matter and corpus callosum. Magnetic resonance imaging (MRI) showed diffuse distribution, T1-weighted images (T1WI) with equal and low signals and T2-weighted images (T2WI) with bilateral symmetrical high diffuse signals. There was no reinforcement by enhancement scanning and signals were different in diffusion-weighted images (DWI). The higher the tumor staging, the stronger the signals. Pathological examination showed neuroastrocytoma in which tumor tissues were manifested by infiltrative growth in blood vessels and around neurons. Conclusions: In clinical diagnosis of GC, much attention should be paid to the diffuse distribution of imaging characteristics, incomplete matching between clinical and imaging characteristics and confirmation by combining with histopathological examination.

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References

  1. Boele FW, Leeuw IM, Cuijpers P, et al (2014). Internet-based guided self-help for glioma patients with depressive symptoms: design of a randomized controlled trial. BMC Neurol, 14, 81. https://doi.org/10.1186/1471-2377-14-81
  2. Cheng HB, Xie C, Zhang RY, et al (2013). Xeroderma pigmentosum complementation group of polymorphisms influence risk of glioma. Asian Pac J Cancer Prev, 14, 4083-7. https://doi.org/10.7314/APJCP.2013.14.7.4083
  3. Claes A, Idema AJ, Wesseling P (2007). Diffuse glioma growth: a guerilla war. Acta Neuropathol, 114, 443-58. https://doi.org/10.1007/s00401-007-0293-7
  4. Cai L, Gao S, Li Y, et al (2011). 11C-Methionine or 11C-Choline PET is superior to MRI in the evaluation of gliomatosis cerebri. Clin Nucl Med, 36, 127-9. https://doi.org/10.1097/RLU.0b013e318203bc08
  5. Chen S, Tanaka S, Giannini C, et al (2013). Gliomatosis cerebri: clinical characteristics, management, and outcomes. J Neurooncol, 112, 267-5. https://doi.org/10.1007/s11060-013-1058-x
  6. Federau C, Meuli R, O'Brien K, et al (2014). Perfusion Measurement in Brain Gliomas with Intravoxel Incoherent Motion MRI. Am J Neuroradiol, 35, 256-62. https://doi.org/10.3174/ajnr.A3686
  7. Fan GG, Deng QL, Wu ZH, et al (2006). Usefulness of diffusion/perfusion-weighted MRI in patients with non-enhancing supratentorial brain gliomas: a valuable tool to predict tumour grading. Br J Radiol, 79, 652-8. https://doi.org/10.1259/bjr/25349497
  8. Herrlinger U, Felsberg J, Kuker W, et al (2002). Gliomatosis cerebri: molecular pathology and clinical course. Ann Neurol, 52, 390-9. https://doi.org/10.1002/ana.10297
  9. Hu LS, Eschbacher JM, Heiserman JE, et al (2012). Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. Neuro Oncol, 14, 919-30. https://doi.org/10.1093/neuonc/nos112
  10. Igissinov N, Akshulakov S, Igissinov S, et al (2013). Malignant tumours of the central nervous system in Kazakhstan--incidence trends from 2004-2011. Asian Pac J Cancer Prev, 14, 4181-6. https://doi.org/10.7314/APJCP.2013.14.7.4181
  11. Jazayeri SB, Rahimi-Movaghar V, Shokraneh F, et al (2013). Epidemiology of primary CNS tumors in Iran: a systematic review. Asian Pac J Cancer Prev, 14, 3979-85. https://doi.org/10.7314/APJCP.2013.14.6.3979
  12. Law M, Yang S, Wang H, et al (2003). Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. Am J Neuroradiol, 24, 1989-98.
  13. Landi A, Piccirilli M, Mancarella C, et al (2011). Gliomatosis cerebri in young patients' report of three cases and review of the literature. Child's Nervous System, 27, 19-25. https://doi.org/10.1007/s00381-010-1137-7
  14. Liang HJ, Yan YL, Liu ZM, et al (2013). Association of XRCC3 Thr241Met polymorphisms and gliomas risk: evidence from a meta-analysis. Asian Pac J Cancer Prev, 14, 4243-7. https://doi.org/10.7314/APJCP.2013.14.7.4243
  15. Liu X, Tian W, Kolar B, et al (2011). MR diffusion tensor and perfusion-weighted imaging in preoperative grading of supratentorial nonenhancing gliomas. Neuro Oncol, 13, 447-55. https://doi.org/10.1093/neuonc/noq197
  16. Liu Z L, Zhou Q, Zeng Q S, et al (2012). Noninvasive evaluation of cerebral glioma grade by using diffusion-weighted imaging-guided single-voxel proton magnetic resonance spectroscopy. J Int Med Res, 40, 76-84. https://doi.org/10.1177/147323001204000108
  17. Lober R M, Cho Y J, Tang Y, et al (2014). Diffusion-weighted MRI derived apparent diffusion coefficient identifies prognostically distinct subgroups of pediatric diffuse intrinsic pontine glioma. J Neurooncol, 117, 175-82. https://doi.org/10.1007/s11060-014-1375-8
  18. Narasimhaiah D, Miquel C, Verhamme E, et al (2012). IDH1 mutation, a genetic alteration associated with adult gliomatosis cerebri. Neuropathology, 32, 30-7. https://doi.org/10.1111/j.1440-1789.2011.01216.x
  19. Park S, Suh YL, Nam D H, et a1 (2009). Gliomatosis cerebri: clinicopathologic study of 33 cases and comparison of mass forming and diffuse types. Clin Neuropathol, 28, 73-82. https://doi.org/10.5414/NPP28073
  20. Paulus W, Kleihues P (2010). Genetic profiling of CNS tumors extends histological classification. Acta Neuropathol, 120, 269-70. https://doi.org/10.1007/s00401-010-0710-1
  21. Porter K R, McCarthy B J, Freels S, et al (2010). Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology. Neuro Oncol, 12, 520-7. https://doi.org/10.1093/neuonc/nop066
  22. Romeike BFM, Mawrin C (2008). Gliomatosis cerebri: growing evidence for diffuse gliomas with wide invasion. Expert Rev Neurother, 8, 587-97. https://doi.org/10.1586/14737175.8.4.587
  23. Rooney A G, McNamara S, Mackinnon M, et al (2013). Screening for major depressive disorder in adults with cerebral glioma: an initial validation of 3 self-report instruments. Neuro Oncol, 15, 122-9. https://doi.org/10.1093/neuonc/nos282
  24. RudaR, Bertero L, Sanson M (2014). Gliomatosis cerebri: A review. Curr Treat Options Neurol, 16, 1-9.
  25. Rees JH (2011). Diagnosis and treatment in neuro-oncology: an oncological perspective. Br J Radiol, 2, S82-9.
  26. Sinha S, Bastin ME, Whittle IR, et al (2002). Diffusion tensor MR imaging of high-grade Gliomatosis cerebris. Am J Neuroradiol, 23, 520-7.
  27. Sun X, Vengoechea J, Elston R, et al (2012). A variable age of onset segregation model for linkage analysis, with correction for ascertainment, applied to glioma. Cancer Epidemiol Biomarkers Prev, 21, 2242-51. https://doi.org/10.1158/1055-9965.EPI-12-0703
  28. Taillibert S, Chodkiewicz C, Laigle-Donadey F, et al (2006). Gliomatosis cerebri: a review of 296 cases from the ANOCEF database and the literature. J Neurooncol, 76, 201-5. https://doi.org/10.1007/s11060-005-5263-0
  29. Zeybek U, Yaylim I, Ozkan NE, et al (2013). Cyclin D1 gene G870A variants and primary brain tumors. Asian Pac J Cancer Prev, 14, 4101-6. https://doi.org/10.7314/APJCP.2013.14.7.4101
  30. Zhang YB, Zhao W, Zeng RX (2013). Autophagic degradation of caspase-8 protects U87MG cells against H2O2-induced oxidative stress. Asian Pac J Cancer Prev, 14, 4095-9. https://doi.org/10.7314/APJCP.2013.14.7.4095

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