The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly more prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration of more complex models correspondingly requires more detailed clinical data. This raises questions about how much data should be collected and when, in order to minimize the total amount of data used and the time until a model can be calibrated accurately. To address these questions, we propose a Bayesian information-theoretic procedure, using a gradient-based score function to determine the optimal data collection times for model calibration. The novel score function introduced in this work eliminates the need for a weight parameter used in a previous study's score function, while still yielding accurate and efficient model calibration using even fewer scans on a sample set of synthetic data, simulating tumors of varying levels of radiosensitivity. We also conduct a robust analysis of the calibration accuracy and certainty, using both error and uncertainty metrics. Unlike the error analysis of the previous study, the inclusion of uncertainty analysis in this work|as a means for deciding when the algorithm can be terminated|provides a more realistic option for clinical decision-making, since it does not rely on data that will be collected later in time.
翻译:临床环境越来越普遍地使用数学模型来预测肿瘤增长和治疗反应。这些模型的复杂程度大致范围很广,而比较复杂的模型的校准也相应地需要更详细的临床数据。这就提出了应收集多少数据的问题,以及何时应收集多少数据,以及何时应收集多少数据,以尽量减少所用数据的总数,以及何时才能准确校准模型。为了解决这些问题,我们提议采用巴耶斯信息理论程序,使用梯度计分函数来确定模型校准的最佳数据收集时间。这项工作中引入的新式评分功能消除了以前一项研究评分函数中使用的加权参数的需要,同时仍然用更少的抽样合成数据集扫描得出准确而有效的模型校准,模拟放射敏度水平不一的肿瘤。我们还用错误和不确定的度量度对校准准确性和确定性进行有力分析。与上次研究的误差分析不同,将不确定性分析纳入这项工作是确定何时算法可以终止的一个手段。在临床决策中不依赖一个更现实的选项。