论文标题
肿瘤治疗场的方法快速估计
A Method for Tumor Treating Fields Fast Estimation
论文作者
论文摘要
肿瘤治疗场（TTFields）是FDA批准的特定类型癌症的治疗方法，可显着延长患者的寿命。肿瘤内TTFIELD的强度与治疗结果相关：强度越大，患者可能生存的时间越长。因此，建议优化ttfields换能器阵列位置，以使其强度最大化。这种优化需要在模拟框架中对TTField的多次计算。但是，这些计算通常是使用有限元方法或类似耗时的方法进行的。因此，实际上只能检查有限数量的传感器阵列位置。为了克服这个问题，我们开发了一种快速估算TTFIELDS强度的方法。我们设计并实施了一种输入患者头部分割的方法，一张组织表电特性和换能器阵列的位置。该方法通过将一些相关参数纳入随机孔回归器中来输出TTFIELDS强度的空间估计。该方法对10位患者（20个TA布局）进行了评估。使用建议的方法，计算时间为1.5分钟，使用商业模拟为180-240分钟。与商业模拟的结果相比，平均误差为0.14 v/cm（SD = 0.06 V/cm）。这些结果表明，基于一些参数的TTFIELD的快速估计是可行的。提出的方法可以促进治疗优化并进一步延长患者的寿命。
Tumor Treating Fields (TTFields) is an FDA approved treatment for specific types of cancer and significantly extends patients life. The intensity of the TTFields within the tumor was associated with the treatment outcomes: the larger the intensity the longer the patients are likely to survive. Therefore, it was suggested to optimize TTFields transducer array location such that their intensity is maximized. Such optimization requires multiple computations of TTFields in a simulation framework. However, these computations are typically performed using finite element methods or similar approaches that are time consuming. Therefore, only a limited number of transducer array locations can be examined in practice. To overcome this issue, we have developed a method for fast estimation of TTFields intensity. We have designed and implemented a method that inputs a segmentation of the patients head, a table of tissues electrical properties and the location of the transducer array. The method outputs a spatial estimation of the TTFields intensity by incorporating a few relevant parameters in a random-forest regressor. The method was evaluated on 10 patients (20 TA layouts) in a leave-one-out framework. The computation time was 1.5 minutes using the suggested method, and 180-240 minutes using the commercial simulation. The average error was 0.14 V/cm (SD = 0.06 V/cm) in comparison to the result of the commercial simulation. These results suggest that a fast estimation of TTFields based on a few parameters is feasible. The presented method may facilitate treatment optimization and further extend patients life.
