A transformer-based deep learning model for preoperative prediction of lymphovascular invasion in laryngeal squamous cell carcinoma: a multicenter study

作者：Helei Yan, Jiaxin Yao, Jing Hou, Lei Liu, Yizhen Li, Guizhi Wang, Shengyi Dou, Yunyun Wang, Xiaoping Yu, Yan Gao, Donghai Huang, Xingwei Wang, Yuanzheng Qiu, Xin Zhang, Yong Liu, Shanhong Lu · 发表于：International Journal of Surgery · 年份：2025 · DOI：10.1097/js9.0000000000004012 · 被引用次数：3 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Head and Neck Cancer Studies、Advanced Radiotherapy Techniques

BACKGROUND: To explore and compare the potential value of radiomics models based on contrast-enhanced computed tomography (CT) for noninvasive preoperative prediction of lymphovascular invasion (LVI) in laryngeal squamous cell carcinoma (LSCC). MATERIALS AND METHODS: This multicenter diagnostic study retrospectively enrolled patients with LSCC from three tertiary hospitals who underwent surgical treatment. Standardized preprocessing was performed on the CT images, followed by region-of-interest segmentation and extraction of traditional radiomics features and deep learning (DL) features. Features were selected using least absolute shrinkage and selection operator (LASSO) regression. Traditional radiomics models and deep learning radiomics (DLR) models were established using logistic regression, random forest, and multilayer perceptron algorithms, respectively. A transformer-based hybrid model was developed by integrating radiomics and DL features. The predictive performance of the three types of models was evaluated and compared using the area under the curve (AUC), decision curve analysis (DCA), sample probability distribution histograms, confusion matrices, calibration curves, net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: A total of 1024 patients were allocated to the training set (center1, n = 291), internal validation set ( n = 126), and external test sets (Center 2, n = 437; Center 3, n = 170). Three radiomics models and thre...