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    A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer by Yin Li, Yin Li, Yin Li, Shuang Li, Shuang Li, Ruolin Xiao, Ruolin Xiao, Xi Li, Yongju Yi, Yongju Yi, Liangyou Zhang, Liangyou Zhang, You Zhou, You Zhou, Yun Wan, Chenhua Wei, Liming Zhong, Liming Zhong, Wei Yang, Wei Yang, Lin Yao, Lin Yao, Lin Yao

    Published 2025-02-01
    “…This study aims to develop and validate a transformer-based deep learning (DL) model based on pelvic MR images, integrated with clinical features, to predict RCLM.MethodsA total of 819 patients with histologically confirmed rectal cancer who underwent preoperative pelvis MRI and carcinoembryonic antigen (CEA) tests were enrolled. Six state-of-the-art DL methods (Resnet18, EfficientNetb0, MobileNet, ShuffleNet, DenseNet, and our transformer-based model) were trained and tested on T2WI and DWI to predict RCLM. …”
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