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Abstract #5089

Prediction of Lymphovascular Invasion in Rectal Cancer Using Deep Learning Models Based on Multi-Parametric MRI

Qin Xue1, Beichen Xie1, Yancong Sun1, Yongchao Niu2, Huijia Yin1, Jinhui Duan1, Zhihao Li3, Kaiyu Wang4, and Ruifang Yan1
1The First Affiliated Hospital of Xinxiang Medical University, Weihui, China, 2Xinxiang Central Hospital, Xinxiang, China, 3GE Healthcare, lnc, Xian, China, 4GE Healthcare, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Tumors, rectal cancer

Motivation: Lymphovascular invasion (LVI) of rectal cancer is an independent risk factor for poor prognosis. However, achieving an accurate preoperative diagnosis using MRI remains challenging.

Goal(s): A deep learning model was constructed based on multi-parameter MRI to accurately predict the LV1 status of rectal cancer before surgery.

Approach: The largest tumor layer and its upper and lower layers were selected as input for the deep learning network to construct the DW1-DL, T2-FS-DL, T1CE-DL, and combined-DL models, followed by external validation.

Results: All models demonstrated strong predictive performance, with the combined-DL model achieving the highest AUC(0.878~0.971).

Impact: This study enhances preoperative diagnosis of lymphovascular invasion in rectal cancer using deep learning and multi-parameter MRI, leading to potential improved treatment strategies, reduced unnecessary surgeries, and better patient outcomes.

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