BACKGROUND: Normal Tissue Complication Probability (NTCP) models predict temporal lobe injury risk post-intensity-modulated radiotherapy in nasopharyngeal carcinoma patients. Optimal parameter estimation methods for NTCP models need refinement.

PURPOSE: To identify optimal method for parameter estimation in Normal Tissue Complication Probability models for temporal lobe injury following intensity-modulated radiotherapy in nasopharyngeal carcinoma patients.

MATERIALS AND METHODS: In this study, all patients underwent curative intensity-modulated radiation therapy at two research centers. Data of temporal lobes from three cohorts [Data-A, n = 278(training set); Data-B, n = 119(external validation set); Data-C, n = 215(internal validation set)]. Five NTCP models were considered, including the Serial Reconstruction Unit (SRU) model, Poisson model, Lyman model, Logit model and Logistic model. Three parameter estimation methods, namely Bayesian estimation (BE), Least Squares Estimation (LSE) and Maximum Likelihood Estimation (MLE), were applied to calibrate the five NTCP models. Area Under Curve (AUC), confusion matrices, dose-response curves were used to compare the performance of the models.

RESULTS: Six hundred twelve patients were enrolled, with 278 in the Data-A; 119 in the Data-B; 215 in the Data-C. The Poisson-NTCP model was evaluated using AUC and R² values across three parameter estimation methods (BE, LSE, and MLE) on three datasets. The results were as follows: Data-A: BE (AUC: 0.938, R²: 0.953), LSE (0.942, 0.986), MLE (0.940, 0.843); Data-B: BE (0.744, 0.958), LSE (0.743, 0.697), MLE (0.745, 0.857); Data-C: BE (0.867, 0.915), LSE (0.862, 0.916), MLE (0.865, 0.896). Compared with the remaining models, the Poisson-NTCP model based on BE had also better performance of fitting dose-response curve and recall rate, accuracy and specificity of confusion matrix.

CONCLUSION: Bayesian Estimation (BE) is the best parameter estimation method among the three parameter estimation methods. The Poisson-NTCP model based on BE exhibited the best fit to the data in predicting post-IMRT incidence of TLI in NPC.