Nasopharyngeal carcinoma (NPC) has an insidious onset and lacks obvious early symptoms, often leading to diagnosis at middle or late stages and significantly worsening patient prognosis. Regional medical big data platforms integrate clinical, genomic, and imaging data sources. These platforms provide advanced technical support and novel research perspectives, such as the identification of new biomarkers and predictive modeling approaches, to facilitate early detection of NPC. This review summarizes recent advances in NPC early detection enabled by these platforms. It highlights the application and benefits of multimodal screening strategies, including Epstein-Barr virus (EBV)-related biomarker assays, DNA methylation analysis, imaging screening techniques, and machine learning algorithms. We synthesize the latest research findings and analyze the potential of multi-omics data integration and artificial intelligence technologies to improve screening accuracy. Moreover, we discuss how these approaches can reduce false-positive findings in NPC early detection. We also explore future directions for achieving precise and personalized NPC early detection through big data platforms. This review aims to provide both theoretical foundations and practical guidance for building regional medical big data platforms, thereby facilitating the optimization and wider implementation of NPC early detection systems by enhancing data integration and analytical capabilities.
Guowen Zhan (Sat,) studied this question.