Prediction of prognosis in T4 or N3 locally advanced nasopharyngeal carcinoma receiving chemoradiotherapy using machine learning methods

Background This study aims to develop and validate a survival prediction model for T4 or N3 locally advanced nasopharyngeal carcinoma (NPC) patients undergoing chemoradiotherapy (CRT) using machine learning methods. Methods A total of 293 patients with locally advanced NPC (T4 or N3 stage) treated with CRT were included in the study. The cohort was divided into a training set (173 patients) and a validation set (120 patients). LASSO regression was used to identify significant prognostic factors, and Cox regression analysis was performed to assess the independent impact of these factors on progression-free survival (PFS). A nomogram was constructed based on the identified prognostic factors to predict 1-, 2-, and 3-year PFS. Model performance was validated using ROC curves, calibration curves, and decision curve analysis (DCA). Results The training cohort showed 1-, 2-, and 3-year PFS rates of 92.4%, 81.3%, and 75.2%, respectively. In the validation cohort, the 1-, 2-, and 3-year PFS rates were 90.1%, 83.5%, and 76.0%, respectively, with no significant differences between the groups (P = 0.94). The LASSO-Cox model identified N stage and Epstein-Barr virus (EBV) levels as key prognostic factors. The nomogram demonstrated good discrimination with AUC values of 0.802, 0.709, and 0.686 at 1, 2, and 3 years, respectively. The ROC curve shows the model’s performance with AUC values at 1 year (0.802), 2 years (0.709), and 3 years (0.686), demonstrating the model’s ability to distinguish between different survival outcomes. The calibration curves and DCA confirmed the model’s good agreement with observed outcomes and its clinical net benefit across different risk thresholds. Conclusion The survival prediction model based on LASSO and Cox regression provides a robust and interpretable tool for predicting PFS in patients with T4 or N3 locally advanced NPC undergoing CRT.