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Abstract #0437

Using deep learning to identify LNM and LVSI of endometrial cancer from conventional MRI: a preliminary two-center study

Yida Wang1, He Zhang2, Xiance Zhao3, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, 2Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China, 3Philips Healthcare, Shanghai, China

Synopsis

Keywords: Uterus, CancerWe developed a multi-task deep learning model using multi-parametric MRI to simultaneously predict lymphatic nodes metastasis (LNM) and lymphatic vascular space invasion (LVSI) in patients with endometrial cancer. Cross-modality attention mechanism was integrated with the model to learn the within and cross modality-specific features which could enhance the performance of network. In this study, we also treated endometrial cancer regions as the anatomical prior knowledge to capture the discriminative information from the whole MR images. The results showed the proposed model predicted LNM and LVSI with a high accuracy in both internal and external test datasets.

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