Development of a normal tissue complication probability (NTCP) model using an artificial neural network for radiation-induced necrosis after carbon ion re-irradiation in locally recurrent nasopharyngeal carcinoma

BACKGROUND: The aim of the present study was to build a normal tissue complication probability (NTCP) model using an artificial neural network (ANN) for radiation-induced necrosis after carbon ion re-irradiation in locally recurrent nasopharyngeal carcinoma (rNPC), and to determine the predictive parameters applied to the model. METHODS: A total of 150 patients with rNPC treated at Shanghai Proton and Heavy Ion Center during 2015–2019 were selected to determine the dominant factors causing mucosal necrosis after carbon therapy. An ANN was built to study both dose-volume histogram (DVH) and clinical factors. Simple oversampling and data normalization were used in the training process. Ten-fold cross validation was conducted to prevent overfitting. RESULTS: Of the DVH factors, the prediction accuracy ranged from 58.3–65.2%, whereas planning target volume (PTV) receiving dose more than 25 GyE (PTV.V25) yielded the best prediction accuracy. Of the clinical factors, baseline necrosis, sex, and biologically equivalent dose (BED) of initial treatment could increase the accuracy of PTV.V25 by 0.5%, 0.5%, and 1.5%, respectively. CONCLUSIONS: An ANN was built to predict radiation-induced necrosis after re-irradiation in rNPC. The best accuracy and area under receiver-operating characteristic (ROC) curve (AUC) were 66.7% and 0.689. The most predictive dosimetric and clinical parameters were PTV.V25 and BED of initial treatment.