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Abstract #1823

Diffusion-Weighted Imaging for Predicting Lymph Node Metastasis in Endometrial Cancer: Added Values of Computer-Aided Segmentation and Radiomic Machine Learning

Tiing Yee Siow1, Yu-Chun Lin1, Lan-Yan Yang2, Yu-Ting Huang1, Yen-Ling Huang1, and Gigin Lin1

1Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan, 2Clinical Trial Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan

We aim to investigate added values of computer-aided segmentation and radiomic machine learning based on diffusion-weighted magnetic resonance (MR) imaging for predicting nodal metastasis in endometrial cancer. Decision-tree machine learning comprised the apparent diffusion coefficient (ADC), whole tumor volumetric and lymph nodes (LNs) segmentations, MR morphological measurement, and relevant clinical parameters. We concluded that a combination of clinical and MR radiomics generates a prediction model for LNmetastasis in endometrial cancer, with diagnostic performance surpassing the conventional ADC and size criteria.

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