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Abstract #3085

Presurgical Differentiation between Malignant Haemangiopericytoma and Angiomatous Meningioma by a Radiomics Approach based on Texture Analysis

Xuanxuan Li1, Yiping Lu2, Jianxun Qu3, Bo Yin2, and Daoying Geng2

1Radiology, Huashan Hospital Affiliated to Fudan University, Shanghai, China, 2Huashan Hospital Affiliated to Fudan University, Shanghai, China, 3Department of MR Research, GE Healthcare, Shanghai, China

We attempted to assess whether a machine-learning model based on texture analysis (TA) could yield a more accurate diagnosis in differentiating malignant haemangiopericytoma (HPC) from angiomatous meningioma (AM). Our sample population consisted of 23 malignant HPCs and 43 AM. We compared the diagnostic ability of three classifiers based on texture features extracted from each modality (T2FLAIR, T1-CE, and DWI) to the classifier based on clinical features from three neuro-radiologists. The T1W-CE classifier performed the best.

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