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RT Journal Article
SR Electronic
T1 Preoperative Assessment of Meningioma Consistency Using a Combination of MR Elastography and DTI
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1755
OP 1761
DO 10.3174/ajnr.A8385
VO 45
IS 11
A1 Bao, Yuting
A1 Qiu, Suhao
A1 Li, Zhenyu
A1 Yang, Guangzhong
A1 Feng, Yuan
A1 Yue, Qi
YR 2024
UL http://www.ajnr.org/content/45/11/1755.abstract
AB BACKGROUND AND PURPOSE: Preoperative assessment of meningioma consistency is beneficial for optimizing surgical strategy and prognosis of patients. We aim to develop a noninvasive prediction model for meningioma consistency utilizing MR elastography and DTI.MATERIALS AND METHODS: Ninety-four patients (52 ± 22 years old, 69 women, 25 men) diagnosed with meningioma were recruited in the study. Each patient underwent preoperative T1WI, T2WI, DTI, and MR elastography. Combined MR elastography–DTI model was developed based on multiple logistic regression. Intraoperative tumor descriptions served as clinical criteria for evaluating meningioma consistency. The diagnostic efficacy in determining meningioma consistency was evaluated by using a receiver operating characteristic curve. Further validation was conducted in 27 stereotactic biopsies by using indentation tests and underlying mechanism was investigated by histologic analysis.RESULTS: Among all the imaging modalities, MR elastography demonstrated the highest efficacy with the shear modulus magnitude (|G*|) achieving an area under the curve (AUC) of 0.81 (95% CI: 0.699–0.929). When combined with DTI, the diagnostic accuracy further increased (AUC: 0.88, 95% CI: 0.784–0.971), surpassing any technique alone. Indentation measurement based on stereotactic biopsies further demonstrated that the MR elastography–DTI model was suitable for predicting intratumor consistency. Histologic analysis suggested that meningioma consistency may be correlated with tumor cell density and fibrous content.CONCLUSIONS: The MR elastography–DTI combined model is effective in noninvasive prediction of meningioma consistency.AUCarea under the curveFAfractional anisotropyMDmean diffusivityROCreceiver operating characteristicSIsignal intensity|G*|shear modulus magnitude