Development and Validation of a Predictive Model for Radiotherapy Outcomes in Patients with Head and Neck Tumors

Objective To analyze the predictive value of oropharyngeal bacterial infection (the primary adverse radiotherapy outcome investigated in this study) in patients with head and neck tumors (HNTs), identify risk factors, and develop and validate a nomogram prediction model. Methods A total of 260 HNT patients who underwent radiotherapy between January 2021 and January 2023 were enrolled. Univariate and multivariate analyses were performed to screen for factors influencing radiotherapy outcomes. A nomogram prediction model was constructed and validated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Results The peripheral blood nCD64 index, peripheral blood CD14 index, neutrophil count, CRP, and serum albumin levels were identified as independent risk factors affecting radiotherapy outcomes (P < 0.05). The constructed nomogram prediction model exhibited excellent discriminative ability (C-index = 0.945, 0.873), good calibration curve agreement, and DCA confirmed its clinical utility. Conclusion The peripheral blood nCD64 index, CD14 index, neutrophil count, CRP, and serum albumin levels influence radiotherapy outcomes. The established nomogram prediction model demonstrates high accuracy and clinical applicability. Advances in knowledge This study develops and validates a novel nomogram incorporating immune-inflammatory biomarkers (nCD64, CD14, neutrophils, CRP, and albumin) to predict radiotherapy outcomes in head and neck cancer, improving personalized prognostic assessment.