Hierarchical dynamic model for risk-stratified screening of nasopharyngeal carcinoma.

Early detection of nasopharyngeal carcinoma through Epstein-Barr virus serology is hampered by a low positive predictive value. This study aims to develop a hierarchical dynamic model to refine risk stratification among individuals initially identified as medium- or high-risk by Epstein-Barr virus serology. By integrating longitudinal Epstein-Barr virus antibody data with age, sex, and family history, the model is trained using data from the PRO-NPC-001 program. In the validation cohort, the high-risk model using one-year data achieves a positive predictive value of 18.2% (about a fourfold increase over serology screening), with a negative predictive value of 97.7% and an area under the curve of 0.783. With two-year data, the positive predictive values for the high-risk and medium-risk models are 8.8% and 1.1%, respectively, with area under the curve values of 0.859 and 0.687. Compared with serology-only screening, the hierarchical dynamic models reduce the need for follow-up examinations by 74.2% in high-risk individuals, yielding cost savings of up to 65.6%. These findings demonstrate that hierarchical dynamic models significantly enhance current serological screening strategies for nasopharyngeal carcinoma, though further prospective validation is warranted.