Morphological and dynamic contrast enhanced MR imaging features for the differentiation of chordoma and giant cell tumors in the Axial Skeleton

Purpose

To characterize the morphological and dynamic-contrast-enhanced (DCE) MRI features of chordoma and giant cell tumor (GCT) of bone occurring in the axial skeleton.

Materials and Methods

A total of 13 patients with chordoma and 26 patients with GCT who received conventional T1, T2, and DCE-MRI on 3 Tesla MR scanners were retrospectively identified and analyzed. Two radiologists evaluated morphological features independently, including the lesion location, expansile bone changes, vertebral compression, presence of paraspinal soft tissue mass, fibrous septa, and the signal intensity on T1WI and T2WI. The inter-observer agreement was evaluated by kappa test. The DCE kinetics was measured to obtain the initial area under curve (IAUC) and the wash-out slope; also the two-compartmental pharmacokinetic model was applied to obtain K^trans and k_ep. The diagnostic accuracy was evaluated by CHAID decision tree and ROC analysis.

Results

Chordomas were more likely to show soft tissue mass than GCTs (13/13 = 100% versus 15/26 = 58%; P = 0.007), as well as fibrous septa (9/13 = 69% versus 0; P < 0.001). In decision tree analysis, presence of fibrous septa and lesion location yield 31/39 = 79% accuracy. The DCE-MRI pharmacokinetic parameters K^trans and k_ep of GCTs were significantly higher than those of chordomas, 0.13 ± 0.65 versus 0.06 ± 0.04 (1/min) for K^trans, 0.62 ± 0.22 versus 0.17 ± 0.12 (1/min) for k_ep, P < 0.001 for both. If using kep = 0.43/min as the cut-off value, it achieved 100% sensitivity and 92% specificity to differentiate chordoma from GCT, with an overall accuracy of 37/39 = 95%. The IAUC was highly correlated with K^trans (r = 0.94), and the slope was highly correlated with k_ep (r = 0.95).

Conclusion

Several morphological features were significantly different between chordoma and GCT, but their diagnostic performance was inferior to that of DCE-MRI. J. Magn. Reson. Imaging 2016

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