Abstract #0524

Multi-Task Radiomics Approach for Prediction of IDH Mutation Status and Early Recurrence of Gliomas from Preoperative MRI

Hongxi Yang¹, Ankang Gao², Yida Wang¹, Yong Zhang², Jingliang Cheng², Yang Song³, and Guang Yang¹

¹Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, ²Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ³MR Scientific Marketing, Siemens Healthcare, Shanghai, China

Synopsis

Keywords: Tumors, BrainWe retrospectively enrolled 243 patients to develop a multi-task radiomics approach to predict IDH mutation status and early recurrence simultaneously in patients with WHO II-IV gliomas from preoperative multi-parametric MRI (mp-MRI). Firstly, multi-task LASSO was performed to find features shared between the two tasks, which were then combined with task-specific features selected recursively to build radiomics models. The models achieved test AUCs of 0.826 and 0.770 for IDH mutation status identification and early recurrence prediction, respectively. Shared features enabled the models to achieve satisfactory performance with minimum number of features, avoiding overfitting and making the models more interpretable.

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