Unveiling acute toxicity trends in nasopharyngeal carcinoma: A global bibliometric perspective

Radiotherapy and chemotherapy are cornerstone treatments for nasopharyngeal carcinoma (NPC); however, acute toxicities such as oral mucositis, dermatitis, and myelosuppression substantially impair patients' quality of life. Although delayed complications have been extensively studied, research trends in acute toxicity lack systematic bibliometric evaluation. This study analyzed 492 publications from the Web of Science Core Collection (1999-2025) using bibliometrix, VOSviewer, and CiteSpace to assess publication output, institutional contributions, keyword evolution, citation patterns, and international collaboration. Annual publications peaked in 2018 (42 articles) and stabilized at approximately 24-26 per year thereafter. China was the leading contributor (295 publications), with Sun Yat-sen University Cancer Center as the most productive institution (226 publications). Core journals included the International Journal of Radiation Oncology Biology Physics (41 articles) and Oral Oncology (28 articles). Keyword and thematic analyses suggest a shift from an earlier emphasis on irradiation and cisplatin-based therapy toward optimization of intensity-modulated radiotherapy, management of oral mucositis, and stage-specific treatment strategies. Citation-burst analysis identified several influential studies, including a parotid-sparing radiotherapy trial and a 2011 Lancet Oncology publication with 1,283 citations. Collaboration networks were centered in China, with contributions from the United States, Italy, and Southeast Asia. Overall, this study provides a quantitative overview of the evolving research landscape of acute toxicities in NPC. The findings suggest increasing attention to toxicity management and supportive care, while also highlighting geographic imbalances in the current evidence base. Future work may benefit from broader database inclusion, integration of multilingual literature, and incorporation of emerging approaches such as artificial intelligence-assisted toxicity prediction.