Abstract #0944

Neurite orientation dispersion and density imaging-based texture features in differentiating glioblastoma from solitary brain metastasis

Guohua Zhao¹, Mengyang He², Yizhou Su³, Yusong Lin³, Eryuan Gao¹, Jie Bai¹, Xiaoyue Ma¹, Huiting Zhang⁴, Xu Yan⁴, Guang Yang⁵, and Jingliang Cheng¹

¹The Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China, ³Collaborative Innovation Center for Internet Healthcare, Zhengzhou, China, ⁴MR Scientific Marketing, Siemens Healthcare, Shanghai, China, ⁵Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, TumorPreoperative differentiation between glioblastomas (GBM) and solitary brain metastases (SBM) would aid in appropriate treatment planning and follow-up. Neurite orientation dispersion and density imaging (NODDI) can identify diverse tissue components within tumors. Texture analysis can be used to extract and quantify these tissue inhomogeneities. In this study, we extracted texture features using NODDI and validated the NODDI metric map models and a combination model to discriminate between GBM and SBM. Finally, the combined NODDI model achieved the best discriminative power. Texture analysis based on NODDI has great potential for distinguishing between GBM and SBM.

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