1. Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features
- Author
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Yumei Jin, Hongkun Yin, Huiling Zhang, Yewu Wang, Shengmei Liu, Ling Yang, and Bin Song
- Subjects
Rectal cancer ,Tumor deposits ,Deep learning ,Hybrid neural network ,Medical physics. Medical radiology. Nuclear medicine ,R895-920 - Abstract
Abstract Background Tumor deposits (TDs) are associated with poor prognosis in rectal cancer (RC). This study aims to develop and validate a deep learning (DL) model incorporating T2-MR image and clinical factors for the preoperative prediction of TDs in RC patients. Methods and methods A total of 327 RC patients with pathologically confirmed TDs status from January 2016 to December 2019 were retrospectively recruited, and the T2-MR images and clinical variables were collected. Patients were randomly split into a development dataset (n = 246) and an independent testing dataset (n = 81). A single-channel DL model, a multi-channel DL model, a hybrid DL model, and a clinical model were constructed. The performance of these predictive models was assessed by using receiver operating characteristics (ROC) analysis and decision curve analysis (DCA). Results The areas under the curves (AUCs) of the clinical, single-DL, multi-DL, and hybrid-DL models were 0.734 (95% CI, 0.674–0.788), 0.710 (95% CI, 0.649–0.766), 0.767 (95% CI, 0.710–0.819), and 0.857 (95% CI, 0.807–0.898) in the development dataset. The AUC of the hybrid-DL model was significantly higher than the single-DL and multi-DL models (both p
- Published
- 2023
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