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<p>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.</p><p></p>

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Treatment response-adapted risk index model for survival prediction and adjuvant chemotherapy selection in nonmetastatic nasopharyngeal carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月1日

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  • Yang Liu,
  • Wenbin Yan,
  • Yupei Chen,
  • Jingjing Miao,
  • Hua Zhang,
  • Jingbo Wang,
  • Ye Zhang,
  • Xiaodong Huang,
  • Kai Wang,
  • Yuan Qu,
  • Xuesong Chen,
  • Jianghu Zhang,
  • Jingwei Luo,
  • Ye-Xiong Li,
  • Chong Zhao,
  • Jun Ma,
  • Runye Wu,
  • Junlin Yi

摘要

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.