Abstract #3064

Interpretable Meningioma Grading and Segmentation with Multiparametric Deep Learning

Yohan Jun*¹, Yae Won Park*², Hyungseob Shin¹, Yejee Shin¹, Jeong Ryong Lee¹, Kyunghwa Han², Soo Mee Lim³, Seung-Koo Lee², Sung Soo Ahn**², and Dosik Hwang**¹

¹Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, ²Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea, Republic of, ³Department of Radiology, Ewha Womans University College of Medicine, Seoul, Korea, Republic of

Synopsis

Preoperative prediction of meningioma grade is important because it influences treatment planning, including surgical resection and stereotactic radiosurgery strategy. The aim of this study was to establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation. We demonstrated that the interpretable multiparametric DL grading model that combined the T2-weighted and contrast-enhanced T1-weighted images can enable fully automatic grading of meningiomas along with segmentation.

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