利用数学建模通过实时临床数据预测放疗患者预后
Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling
AI总结：
针对头颈癌放疗预后预测中临床数据稀疏与模型复杂度的矛盾，本研究提出简洁的肿瘤房室模型，结合新型统计方法与贝叶斯粒子滤波思路，实现带不确定性的患者特异性预后预测，并验证了其效果与局限。
摘要：
来自头颈癌患者的纵向肿瘤体积数据显示，治疗前大小和分期相近的肿瘤，对相同的放疗分割方案的反应可能存在极大差异。数学模型常被用于在此背景下预测治疗结果，且有望指导临床决策并为个性化分割方案提供依据。阻碍模型在该场景下有效应用的问题在于，临床测量数据稀疏，而产生全范围的患者可能反应又需要复杂的模型。在本研究中，我们提出了一个描述肿瘤体积与肿瘤组成的房室模型（compartment model），该模型尽管相对简单，却能够产生广泛的患者反应类型。随后我们开发了全新的统计学方法，并利用一组已有的临床数据构建了一个预测模型，可同时预测肿瘤体积进展情况以及在患者整个治疗过程中不断变化的相关不确定性水平。为捕捉患者间的异质性，所有模型参数均为患者特异性的，我们还开发了一种类似自助粒子滤波的贝叶斯方法，将一组训练数据作为先验知识进行建模。我们在未见过的子集数据上验证了所提方法，既展示了训练后模型的预测能力，也说明了其局限性。
英文摘要：
Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient's course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations.
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