Radiologic classification of brain stem tumors: Correlation of magnetic resonance imaging appearance with clinical outcome

被引:116
|
作者
Fischbein, NJ
Prados, MD
Wara, W
Russo, C
Edwards, MSB
Barkovich, AJ
机构
[1] Department of Radiology, University of California at San Francisco, San Francisco, VA
关键词
brain stem tumors; magnetic resonance imaging; neoplasm; glioma; gadolinium;
D O I
10.1159/000121010
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Although tumors of the brain stem have traditionally been classified as a single entity, these tumors are increasingly being recognized as a heterogeneous group, with some subgroups having better prognoses for long-term survival. Although several systems for classification of brain stem tumors have been proposed, none have been based on data derived from contrast-enhanced magnetic resonance (MR) imaging. In this review, we present a classification scheme based on our review of the literature and of the MR scans of 64 patients with brain stem tumors. In addition, we assess the contribution of gadolinium to the classification of brain stem tumors and correlate the various tumor subtypes, based on MR appearance, with prognosis. Our results suggest that the most important factor in determining prognosis based on MR characteristics is whether the tumor is diffuse or focal. Focal tumors have an excellent prognosis regardless of the site of tumor origin. Diffuse tumors of the mesencephalon and pens have a significantly poorer prognosis than focal tumors (p = 0.0013), with diffuse pontine tumors having the worst prognosis. Differentiation of diffuse and focal medullary tumors was difficult, possibly explaining the lack of significant difference in the survival of patients with diffuse versus focal medullary tumors. The presence or absence of enhancement after the administration of paramagnetic contrast has no significant relation with outcome, overall or within specific tumor subgroups.
引用
收藏
页码:9 / 23
页数:15
相关论文
共 50 条
  • [1] Magnetic resonance imaging of primary brain-stem tumors in children
    Jurkiewicz, Elzbieta
    Pakula-Kosciesza, Iwona
    Walecki, Jerzy
    Drogosiewicz, Monika
    Perek-Polnik, Monika
    Barszcz, Slawomir
    Bekiesinska-Figatowska, Monika
    POLISH JOURNAL OF RADIOLOGY, 2006, 71 (01) : 20 - 25
  • [2] Magnetic resonance imaging diagnosis of brain tumors in dogs
    Bentley, R. Timothy
    VETERINARY JOURNAL, 2015, 205 (02) : 204 - 216
  • [3] Malignant fatty tumors: classification, clinical course, imaging appearance and treatment
    Peterson, JJ
    Kransdorf, MJ
    Bancroft, LW
    O'Connor, MI
    SKELETAL RADIOLOGY, 2003, 32 (09) : 493 - 503
  • [4] Benign fatty tumors: classification, clinical course, imaging appearance, and treatment
    Laura W. Bancroft
    Mark J. Kransdorf
    Jeffrey J. Peterson
    Mary I. O’Connor
    Skeletal Radiology, 2006, 35 : 719 - 733
  • [5] Benign fatty tumors: classification, clinical course, imaging appearance, and treatment
    Bancroft, Laura W.
    Kransdorf, Mark J.
    Peterson, Jeffrey J.
    O'Connor, Mary I.
    SKELETAL RADIOLOGY, 2006, 35 (10) : 719 - 733
  • [6] An Efficient Ensemble Approach for Brain Tumors Classification Using Magnetic Resonance Imaging
    Saeed, Zubair
    Torfeh, Tarraf
    Aouadi, Souha
    Ji, Xiuquan
    Bouhali, Othmane
    INFORMATION, 2024, 15 (10)
  • [7] BRAIN-STEM GLIOMAS - A CLASSIFICATION-SYSTEM BASED ON MAGNETIC-RESONANCE-IMAGING
    BARKOVICH, AJ
    KRISCHER, J
    KUN, LE
    PACKER, R
    ZIMMERMAN, RA
    FREEMAN, CR
    WARA, WM
    ALBRIGHT, L
    ALLEN, JC
    HOFFMAN, HJ
    PEDIATRIC NEUROSURGERY, 1991, 16 (02) : 73 - 83
  • [8] Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning
    Rasheed, Zahid
    Ma, Yong-Kui
    Ullah, Inam
    Al Shloul, Tamara
    Tufail, Ahsan Bin
    Ghadi, Yazeed Yasin
    Khan, Muhammad Zubair
    Mohamed, Heba G.
    BRAIN SCIENCES, 2023, 13 (04)
  • [9] Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging
    Reyes, Daniel
    Sanchez, Javier
    HELIYON, 2024, 10 (03)
  • [10] Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors
    Rollin, N
    Guyotat, J
    Streichenberger, N
    Honnorat, J
    Minh, VAT
    Cotton, F
    NEURORADIOLOGY, 2006, 48 (03) : 150 - 159