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Abstract #0255

Automatic segmentation and classification of GBM and SBM using a 3D deep learning model on multiparametric MRI: a multi-center study

Mingzhen Wu1,2, Jixin Luan3, Haijie Wang4, Xiaomin Wang1, Xiaoyun Liang4, Chuanchen Zhang2, and Yang Zhao1
1Department of Radiology, The Second Hospital of Tianjin Medical University, Tianjin, China, 2Department of Radiology, Liaocheng People's Hospital, Liaocheng, China, 3China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, 4Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Cancer, 3D convolutional neural network, glioblastoma, solitary brain metastasis, magnetic resonance imaging, automatic segmentation and classification

Motivation: Differentiating between glioblastoma (GBM) and solitary brain metastasis (SBM) before surgery is challenging yet essential for clinical decisions.

Goal(s): To construct a three-dimensional (3D) deep learning (DL) model for the automated segmentation and classification of GBM and SBM based on multiparametric MRI.

Approach: 314 patients from multiple medical centers were recruited. Tumor segmentation was performed using no-new-UNet, followed by the development and comparison of 3D DL, 2D DL, and radiomics models for classification. Additionally, three radiologists with varying experience levels conducted a two-round classification analysis, with and without a 3D DL model as a reference.

Results: 3D DL model showed the best performance.

Impact: Reading with 3D DL model improves the diagnostic accuracy of radiologists, thereby enhancing patient treatment and prognosis potentially. Considering its promising performance, it is recommended for routine clinical application.

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