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Abstract #1720

A radiomics model for preoperative prediction of brain invasion in meningioma based on MRI

jing Zhang1, kuan Yao2, Zhenyu Liu3, Junlin Zhou1, Guojing Zhang4, and Yuntai Cao1
1Radiology, Lanzhou University Second Hospital, Lanzhou, China, 2School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, Shanghai, China, 3CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, Beijing, China, 4Lanzhou University Second Hospital, Lanzhou, China

Objectives Using a radiomics method to predict brain invasion by meningioma. Methods 1595 quantitative imaging features were extracted. LASSO was performed to select features. SVM was used to fit the predictive model. Furthermore, a nomogram was constructed, and validated using decision curve analysis (DCA). Results 8 features were significantly correlated with brain invasion. The radiomics model derived from the fusing MRI sequences resulted in the best discrimination ability, with AUC of0.855(95%CI, 0.829-0.882), sensitivity of 80.32% (95%CI, 75.56%-85.25%). Conclusions The radiomics model developed in this study provided a new non-invasive way to facilitate the preoperative prediction of brain invasion in meningioma.

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