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Abstract #2142

A Large Scale Radiomics Profiling Strategy for Glioma Overall Survival Prediction

Pan Sun1, Defeng Wang2, Queenie Chan3, and Lin Shi1

1Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China, 2Research Center for Medical Image Computing, The Chinese University of Hong Kong, Hong Kong, China, 3Philips Healthcare, Hong Kong, China

Glioma is the most common brain intracranial malignancy, which accounts for about 80% of malignant brain tumors in adults and its median survival rate is 12 months. In clinical, how to accurately predict the glioma overall survival (GOS) is a crucial work and it will be beneficial to monitor tumor progression, execute surgery as well as plan radiotherapy and follow-up studies. However, the glioma generally has highly heterogeneity degrees in the histological tumor sub-regions. we propose a comprehensive multi-modality MRI radiomics way of predicting the GOS. Different features are proposed committing to different image modalities. A feature selection strategy is applied for the optimal features and then random forest is contributed to the classification of short-survivors and long-survivors. With the performance evaluation criteria, our model showed promising classification ability for the brain tumor.

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