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Research ArticleHead and Neck Imaging
Open Access

MRI Signal Intensity and Electron Ultrastructure Classification Predict the Long-Term Outcome of Skull Base Chordomas

J. Bai, J. Shi, S. Zhang, C. Zhang, Y. Zhai, S. Wang, M. Li, C. Li, P. Zhao, S. Geng, S. Gui, L. Jing and Y. Zhang
American Journal of Neuroradiology May 2020, DOI: https://doi.org/10.3174/ajnr.A6557
J. Bai
aFrom the Department of Neurosurgery (J.B., P.Z., S. Geng, S. Gui, Y. Zhang), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
bBeijing Neurosurgical Institute (J.B., S.Z., C.Z., Y. Zhai, S.W., M.L., C.L., Y. Zhang), Capital Medical University, Beijing, China
cChina National Clinical Research Center for Neurological Diseases (J.B., P.Z., S. Geng, S. Gui, Y. Zhang), Beijing, China
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J. Shi
dDepartment of Neurosurgery (J.S.), Tsinghua University Yuquan Hospital, Beijing, China
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S. Zhang
bBeijing Neurosurgical Institute (J.B., S.Z., C.Z., Y. Zhai, S.W., M.L., C.L., Y. Zhang), Capital Medical University, Beijing, China
eDepartment of Neurosurgery (S.Z.), Anshan Central Hospital, Anshan, China
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C. Zhang
bBeijing Neurosurgical Institute (J.B., S.Z., C.Z., Y. Zhai, S.W., M.L., C.L., Y. Zhang), Capital Medical University, Beijing, China
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Y. Zhai
bBeijing Neurosurgical Institute (J.B., S.Z., C.Z., Y. Zhai, S.W., M.L., C.L., Y. Zhang), Capital Medical University, Beijing, China
fDepartment of Neurosurgery (Y. Zhai), First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
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S. Wang
bBeijing Neurosurgical Institute (J.B., S.Z., C.Z., Y. Zhai, S.W., M.L., C.L., Y. Zhang), Capital Medical University, Beijing, China
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M. Li
bBeijing Neurosurgical Institute (J.B., S.Z., C.Z., Y. Zhai, S.W., M.L., C.L., Y. Zhang), Capital Medical University, Beijing, China
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C. Li
bBeijing Neurosurgical Institute (J.B., S.Z., C.Z., Y. Zhai, S.W., M.L., C.L., Y. Zhang), Capital Medical University, Beijing, China
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P. Zhao
aFrom the Department of Neurosurgery (J.B., P.Z., S. Geng, S. Gui, Y. Zhang), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
cChina National Clinical Research Center for Neurological Diseases (J.B., P.Z., S. Geng, S. Gui, Y. Zhang), Beijing, China
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S. Geng
aFrom the Department of Neurosurgery (J.B., P.Z., S. Geng, S. Gui, Y. Zhang), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
cChina National Clinical Research Center for Neurological Diseases (J.B., P.Z., S. Geng, S. Gui, Y. Zhang), Beijing, China
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S. Gui
aFrom the Department of Neurosurgery (J.B., P.Z., S. Geng, S. Gui, Y. Zhang), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
cChina National Clinical Research Center for Neurological Diseases (J.B., P.Z., S. Geng, S. Gui, Y. Zhang), Beijing, China
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L. Jing
gDepartment of Health Statistics (L.J.), Shanxi Medical University, Taiyuan, China.
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Y. Zhang
aFrom the Department of Neurosurgery (J.B., P.Z., S. Geng, S. Gui, Y. Zhang), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
bBeijing Neurosurgical Institute (J.B., S.Z., C.Z., Y. Zhai, S.W., M.L., C.L., Y. Zhang), Capital Medical University, Beijing, China
cChina National Clinical Research Center for Neurological Diseases (J.B., P.Z., S. Geng, S. Gui, Y. Zhang), Beijing, China
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Abstract

BACKGROUND AND PURPOSE: MR imaging is a useful and widely used evaluation for chordomas. Prior studies have classified chordomas into cell-dense type and matrix-rich type according to the ultrastructural features. However, the relationship between the MR imaging signal intensity and ultrastructural classification is unknown. We hypothesized that MR imaging signal intensity may predict both tumor ultrastructural classification and prognosis.

MATERIALS AND METHODS: Seventy-nine patients with skull base chordomas who underwent 95 operations were included in this retrospective single-center series. Preoperative tumor-to-pons MR imaging signal intensity ratios were calculated and designated as ratio on T1 FLAIR sequence (RT1), ratio on T2 sequence (RT2), and ratio on enhanced T1 FLAIR sequence (REN), respectively. We assessed the relationships among signal intensity ratios, ultrastructural classification, and survival.

RESULTS: Compared with the matrix-rich type group, the cell-dense type chordomas showed lower RT2 (cell-dense type: 1.90 ± 0.38; matrix-rich type: 2.61 ± 0.60 P < .001). The model of predicting cell-dense type based on RT2 had an area under the curve of 0.83 (95% CI, 0.75–0.92). In patients without radiation therapy, both progression-free survival (P = .003) and overall survival (P = .002) were longer in the matrix-rich type group than in the cell-dense type group. REN was a risk factor for progression-free survival (hazard ratio = 10.24; 95% CI, 1.73–60.79); RT2 was a protective factor for overall survival (hazard ratio  = 0.33; 95% CI, 0.12–0.87); and REN was a risk factor for overall survival (hazard ratio = 4.76; 95% CI, 1.51–15.01).

CONCLUSIONS: The difference in MR imaging signal intensity in chordomas can be explained by electron microscopic features. Both signal intensity ratios and electron microscopic features may be prognostic factors.

ABBREVIATIONS:

CDT
cell-dense type
MRT
matrix-rich type
OS
overall survival
PFS
progression-free survival
RT1, RT2, and REN
ratios of tumor-to-pons signal intensity in the T1 FLAIR sequence, T2 sequence, and enhanced T1 FLAIR sequence, respectively.

Footnotes

  • J. Bai and J. Shi contributed equally to this work.

  • This work was supported by the Beijing Municipal Science and Technology Commission (No. Z171100000117002) and the Research Special Fund for Public Welfare Industry of Health (No. 201402008).

  • © 2020 by American Journal of Neuroradiology

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Cite this article
J. Bai, J. Shi, S. Zhang, C. Zhang, Y. Zhai, S. Wang, M. Li, C. Li, P. Zhao, S. Geng, S. Gui, L. Jing, Y. Zhang
MRI Signal Intensity and Electron Ultrastructure Classification Predict the Long-Term Outcome of Skull Base Chordomas
American Journal of Neuroradiology May 2020, DOI: 10.3174/ajnr.A6557

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MRI Signal Intensity and Electron Ultrastructure Classification Predict the Long-Term Outcome of Skull Base Chordomas
J. Bai, J. Shi, S. Zhang, C. Zhang, Y. Zhai, S. Wang, M. Li, C. Li, P. Zhao, S. Geng, S. Gui, L. Jing, Y. Zhang
American Journal of Neuroradiology May 2020, DOI: 10.3174/ajnr.A6557
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