A machine learning based prediction model for short term efficacy of nasopharyngeal carcinoma

The radiological dosimetric parameters and clinical features were screened by machine learning to construct a prediction model for the short-term efficacy of locally advanced Nasopharyngeal Carcinoma (LANPC). Patients diagnosed with Nasopharyngeal Carcinoma were retrospectively collected in the study. Twenty-four clinical features and twelve radiological dosimetric features were included. Three machine learning algorithms were used to construct predictive models for the short-term efficacy of LANPC. Kaplan-Meier log-rank method was used to compare the prognosis of patients with different efficacies in the model. The reliability of the model was evaluated using the calibration curve and the area under the curve (AUC). There were 194 patients who met the inclusion criteria. Among the three models being constructed, Random forest (RSF) model showed the best predictive ability, with AUC values of 1.000 in the training group and 0.944 in the test group, followed by XGBoost decision tree (GBDT) model (0.866/0.849) and decision tree (DT) model (0.848/0.783). In RSF model, the 3-year and 5-year overall survival rates of patients in complete remission (CR) group were 98.9% (95% CI 0.9688-1.0000) and 89.7% (95% CI 0.8256-0.9752), respectively.While for patients in non-CR group, the 3-year and 5-year overall survival (OS) rate was 100% (95%CI 1.000~1.000) and 98.8% (95% CI 0.9652-1.0000), respectively. There has statistically significant difference between the two groups (P = 0.0037). RSF model constructed by machine-learning algorithm based on radiological dosimetric parameters and clinical characteristics can better predict the short-term efficacy of LANPC, and is an effective tool to evaluate the short-term efficacy of different LANPC patients during treatment.