MRI-based deep learning model predicts recurrent nasopharyngeal carcinoma in post-radiation nasopharyngeal necrosis.
Authors
Affiliations (11)
Affiliations (11)
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China; Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China; Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, PR China. Electronic address: [email protected].
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China; Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China. Electronic address: [email protected].
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China; Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China. Electronic address: [email protected].
- Department of Nasopharyngeal Head and Neck Tumor Radiotherapy, Zhongshan City People's Hospital, Zhongshan, PR China. Electronic address: [email protected].
- School of Computer Science and Technology, Guangdong University of Technology, Guangdong Provincial Key Laboratory of Cyber-Physical System, Guangzhou, PR China. Electronic address: [email protected].
- School of Computer Science and Technology, Guangdong University of Technology, Guangdong Provincial Key Laboratory of Cyber-Physical System, Guangzhou, PR China. Electronic address: [email protected].
- School of Computer Science and Technology, Guangdong University of Technology, Guangdong Provincial Key Laboratory of Cyber-Physical System, Guangzhou, PR China. Electronic address: [email protected].
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, PR China. Electronic address: [email protected].
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China; Department of Experimental Research, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China. Electronic address: [email protected].
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China; Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China. Electronic address: [email protected].
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China; Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China. Electronic address: [email protected].
Abstract
The pretreatment identification of post-radiation nasopharyngeal necrosis (PRNN) combined with recurrent nasopharyngeal carcinoma (referred to as cancer-infiltrative PRNN) is crucial for the diagnosis and treatment of PRNN. As the first study to identify recurrent nasopharyngeal carcinoma in patients with PRNN, we aimed to develop a deep learning (DL)-based predictive model using routine MRI to distinguish cancer-infiltrative PRNN from cancer-free PRNN. MRIs of 437 patients with PRNN were manually labeled and randomly divided into training and validation cohorts. Video Swin Transformer and Multilayer Perceptron were employed to construct the DL model. The integrated DL and clinical model (DC<sub>combined</sub> model) and the integrated radiomics and clinical model (RC<sub>combined</sub> model) were constructed using linear weighted fusion of the prediction results from the two models. The predictive value of each model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. The DC<sub>combined</sub> model significantly outperformed the radiologists in terms of AUC (0.83 vs. 0.60, p < 0.001), accuracy (0.78 vs. 0.60, p = 0.002), and sensitivity (0.86 vs. 0.62, p = 0.002) in the validation cohort. The DC<sub>combined</sub> model showed the highest validation sensitivity of 0.86 (95 % CI 0.77-0.94), whereas the RC<sub>combined</sub> model demonstrated the highest specificity of 0.88 (95 % CI 0.81-0.96). Our DC<sub>combined</sub> model based on DL can noninvasively distinguish cancer-infiltrative PRNN from cancer-free PRNN with higher AUC, accuracy, and sensitivity than those of radiologists and better sensitivity than that of the RC<sub>combined</sub> model based on radiomics.