Abstract
OBJECTIVE: This study set out to institute an effective nomogram to predict the prognosis of nasopharyngeal carcinoma (NPC) using magnetic resonance imaging (MRI)-detected residual tumor at the end of intensity-modulated radiotherapy (IMRT). BACKGROUND: This study retrospectively analyzed the prognostic factors of NPC using MRI-detected residual tumor at the end of IMRT, in order to individualize the treatment of patients with poor prognosis as early as possible. METHODS: Overall, 162 NPC patients with local or regional residual tumor at the end of IMRT were retrospectively analyzed. Based on multivariate Cox regression analysis using the backward stepwise method, a nomogram was generated to predict the prognosis of these patients. Identification, calibration, clinical applicability and reproducibility were evaluated by C-index, time-dependent AUC, calibration curve and bootstrap verification. According to the best cut-off value of total score of prognoses calculated by X-tile software, all patients were separated into either low-risk or high-risk group. RESULTS: The nomogram identified age, chemotherapy, N stage, lymph nodes necrosis are significant predictors of prognosis. The AUC of the prediction model is 0.754, and the consistency index is 0.724 (95% confidence interval is 0.659-0.788). The model has good discrimination ability. Through bootstrapping test, the consistency index, corrected slope was 0.723, 0.861, respectively. The calibration slope of predicting 3-year and 5-year overall survival was 1.006 and 1.071, respectively. The calibration curve showed satisfactory calibration effect and good net benefit. The best cut-off value of total score of prognoses calculated by X-tile software was 149.1. Kaplan-Meier survival curve showed that OS and DMFS in the high-risk group were substantially reduced compared to those in the low-risk group. CONCLUSION: We constructed and validated a new nomogram to help clinicians understand the prognosis of NPC patients with residue at the end of IMRT. With an estimate of the individual risk, clinicians can start treatment decisions as early as possible for high-risk patients with poor prognosis.