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Abstract #1907

Radiomics and Machine Learning for Prediction of Recurrence in Meningiomas

Ching-Chung Ko1,2, Yang Zhang3, Kai-Ting Chang3, Jeon-Hor Chen3,4, 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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