MRI-based deep learning model predicts recurrent nasopharyngeal carcinoma in post-radiation nasopharyngeal necrosis

BACKGROUND 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.

METHODS 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 (DCcombined model) and the integrated radiomics and clinical model (RCcombined 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.

RESULTS The DCcombined 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 DCcombined model showed the highest validation sensitivity of 0.86 (95 % CI 0.77-0.94), whereas the RCcombined model demonstrated the highest specificity of 0.88 (95 % CI 0.81-0.96).

CONCLUSIONS Our DCcombined 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 RCcombined model based on radiomics.