MR Imaging Grading System for Skull Base Chordoma.

Abstract

BACKGROUND AND PURPOSE Skull base chordoma has been widely studied in recent years, however, imaging characteristics of this tumor have not been well elaborated. The purpose of this study was to establish an MR imaging grading system for skull base chordoma. MATERIALS AND METHODS In this study, 156 patients with skull base chordomas were retrospectively assessed. Tumor-to-pons signal intensity ratios were calculated from pretreatment MR images RT1 (ratio of tumor to pons signal intensity in T1 FLAIR sequence), RT2 (ratio of tumor to pons signal intensity in T2 sequence) and REN (ratio of tumor to pons signal intensity in enhanced T1 FLAIR sequence), and significant ratios for overall survival and progression-free survival were selected to establish a grading system. Clinical variables among different MR imaging grades were then analyzed to evaluate the usefulness of the grading system. RESULTS RT2 (P < .001) and REN (P = .04) were identified as significant variables affecting progression-free survival. After analysis, the classification criteria were set as follows: MR grade I, RT2 > 2.49 and REN ≤ 0.77; MR grade II, RT2 > 2.49 and REN > 0.77, or RT2 ≤ 2.49 and REN ≤ 0.77; and MR grade III, RT2 ≤ 2.49 and REN > 0.77. MR grade III tumors had a more abundant tumor blood supply than MR grade I tumors (P < .001), and the intraoperative blood loss of MR grade III tumors was higher than that of MR grade I tumors (P = .002). Additionally, skull base chordoma progression risk increased by 2.071 times for every single MR grade increase (P < .001). CONCLUSIONS A higher RT2 value was a negative indicator of tumor progression, whereas a higher REN value was a positive risk factor of tumor progression. MR grade III tumors showed a more abundant blood supply than MR grade I tumors, and the risk of skull base chordoma progression increased with every single MR grade increase.

DOI: 10.3174/ajnr.A5152

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Cite this paper

@article{Tian2017MRIG, title={MR Imaging Grading System for Skull Base Chordoma.}, author={K Tian and Liang Wang and J Ma and K Wang and D. Li and J Du and G Jia and Zhiliang Wu and Jia Zhang}, journal={AJNR. American journal of neuroradiology}, year={2017}, volume={38 6}, pages={1206-1211} }