Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses
Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses
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DOI：
10.1080/09553002.2019.1589013
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发表时间：
2019-03-14
影响因子：
2.6
通讯作者：
Enderling, Heiko
中科院分区：
文献类型：
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作者：
Sunassee, Enakshi D.;Tan, Dean;Enderling, Heiko
目的：具有相同肿瘤分级和分期的个体患者之间的放射治疗处方剂量和剂量分割方案差异不大。为了个性化放射治疗，需要一个预测模型来模拟放射反应。先前使用多个变量和参数的建模尝试已被证明可以产生出色的数据拟合，但代价是不可识别性和临床上不切实际的结果。材料和方法：我们开发了一个基于增殖饱和指数（PSI）的数学模型，该指数是治疗前肿瘤体积与承载能力比率的测量值，可调节内在的肿瘤生长和放射反应率。在自适应贝叶斯方法中，我们利用越来越多的个体患者数据点来预测患者对后续辐射剂量的特定反应。结果：模型分析表明，使用PSI作为唯一的患者特异性参数，模型模拟可以高精度地拟合纵向临床数据（R-2=0.84）。通过在治疗早期使用每日 CT 扫描分析肿瘤对放射的反应，可以在两周后高精度预测对剩余治疗部分的反应（c 指数 = 0.89）。结论：PSI 模型可能适合预测个体患者的治疗反应，并为中期治疗方案的调整提供可行的决策点。所提出的工作在治疗之前和治疗期间提供了一种可操作的图像衍生生物标志物，以个性化和适应放射治疗。
Purpose: Radiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of non-identifiability and clinically unrealistic results. Materials and methods: We develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients to predict patient-specific responses to subsequent radiation doses. Results: Model analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy (R-2=0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index = 0.89). Conclusion: The PSI model may be suited to forecast treatment response for individual patients and offers actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy.