Liu Yang, Yan Wenbin, Chen Yupei, Miao Jingjing, Zhang Hua, Wang Jingbo, Zhang Ye, Huang Xiaodong, Wang Kai, Qu Yuan, Chen Xuesong, Zhang Jianghu, Luo Jingwei, Li Ye-Xiong, Zhao Chong, Ma Jun, Wu Runye, Yi Junlin
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
NPJ Digit Med. 2025 Sep 1;8(1):564. doi: 10.1038/s41746-025-01918-2.
Dynamic response to therapy is strongly associated with cancer outcomes. We aim to develop the response-adapted individualized risk index (RAIRI) as an individual prognostic approach and predictive biomarker for adjuvant chemotherapy (AC) benefit in nasopharyngeal carcinoma (NPC) based on pretreatment clinical characteristics, longitudinal cell-free Epstein-Barr virus DNA, and MRI-based tumor regression measurements collected during treatment. Using Bayesian joint model, we developed and validated RAIRI, a dynamic and multidimensional model, with 2148 patients in training, internal validation, external validation, and RCT cohorts (ClinicalTrials.gov NCT02958111 2016-11-04 and NCT02143388 2014-05-18). RAIRI predictions were refined over time using serially collected longitudinal data. RAIRI demonstrated accurate calibration and high prognostic accuracy, superior to conventional models. In RCT cohort, RAIRI identified approximately 70% of low-risk patients who did not benefit from AC, whereas the high-risks experienced substantial benefits from AC. Therefore, RAIRI could provide real-time updated quantitative survival estimates for individuals and facilitate personalized AC selection.
对治疗的动态反应与癌症预后密切相关。我们旨在开发适应性反应个体风险指数（RAIRI），作为一种基于治疗前临床特征、纵向游离Epstein-Barr病毒DNA以及治疗期间基于MRI的肿瘤消退测量结果的个体预后方法和预测生物标志物，用于评估鼻咽癌（NPC）辅助化疗（AC）的获益情况。使用贝叶斯联合模型，我们开发并验证了RAIRI，这是一个动态多维度模型，在训练、内部验证、外部验证和随机对照试验队列（ClinicalTrials.gov NCT02958111 2016年11月4日和NCT02143388 2014年5月18日）中纳入了2148例患者。通过连续收集纵向数据，随着时间推移对RAIRI预测进行优化。RAIRI显示出准确的校准和高预后准确性，优于传统模型。在随机对照试验队列中，RAIRI识别出约70%未从AC中获益的低风险患者，而高风险患者从AC中获得了显著益处。因此，RAIRI可为个体提供实时更新的定量生存估计，并有助于个性化AC选择。
