Development of machine learning models for survival prediction in nasopharyngeal carcinoma using population-based data

作者：Guoxiang Lin, Qiyan Mo, Lu Han, Shaohan Sun, Zhi‐Qiang Xiao, Weiming Zhang · 发表于：Discover Oncology · 年份：2025 · DOI：10.1007/s12672-025-03790-8 · 被引用次数：3 · 研究领域：Head and Neck Cancer Studies、Radiomics and Machine Learning in Medical Imaging、Lung Cancer Research Studies

BACKGROUND: Despite significant improvements in treatment efficacy, nasopharyngeal carcinoma (NPC) remains one of the most common and threatening head and neck cancers. This study sought to build a risk stratification model that categorizes NPC patients into distinct prognostic groups based on readily available demographic and clinical variables, with the goal of enhancing patient care and improving survival rates. METHODS: Data for patients diagnosed with NPC between 2010 and 2018 were obtained from the SEER program. Kaplan-Meier analysis and Cox proportional hazards regression were applied to evaluate the effects of treatment modalities on overall survival (OS) and cancer-specific survival (CSS). Prognostic variables from multivariable Cox models were used to construct nomograms for predicting 1-, 3-, and 5-year OS and CSS. Several survival-focused machine learning algorithms, including the random survival forest (RSF), were trained and compared using the concordance index (C-index) and integrated Brier score. An interactive web-based calculator was developed based on the best-performing model. RESULTS: The analysis included 9,816 patients diagnosed between 2000 and 2020. Combined radiotherapy and chemotherapy significantly improved survival in locoregionally advanced and advanced NPC compared with other treatment regimens. Cox-based nomograms achieved a C-index of 0.71 (95% CI: 0.70-0.72), outperforming the conventional staging system. The RSF model demonstrated the highes...