Abstract #1907

Radiomics and Machine Learning for Prediction of Recurrence in Meningiomas

Ching-Chung Ko^1,2, Yang Zhang³, Kai-Ting Chang³, Jeon-Hor Chen^3,4, and Min-Ying Lydia Su³

¹Department of Medical Imaging, Chi Mei Medical Center, Tainan, Taiwan, ²Department of Pharmacy, Chia Nan University of Pharmacy and Science, Tainan, Taiwan, ³Department of Radiological Sciences, University of California, Irvine, CA, United States, ⁴E-Da Hospital/I-Shou University, Kaohsiung, Taiwan

A subset of benign meningiomas may show early progression/recurrence (P/R) after surgery. In clinical practice, one of the main challenges in the treatment of meningiomas is to determine factors that correlate with P/R. This study investigated the role of radiomics and machine learning for the prediction of P/R in meningiomas. 128 patients diagnosed with WHO grade I meningioma were studied. Total 214 descriptors were extracted from the various MR sequences. The prediction accuracy of P/R was 74% and the AUC of the prediction model was 0.80.

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