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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