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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Estimating the Intensity and Anisotropy of Tumor Treating Fields Jsing Singular Value Decomposition. Towards a More Comprehensive Estimation of Anti-tumor Efficacy
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Estimating the Intensity and Anisotropy of Tumor Treating Fields Jsing Singular Value Decomposition. Towards a More Comprehensive Estimation of Anti-tumor Efficacy

机译：用奇异值分解估计肿瘤治疗领域的强度和各向异性。寻求更全面的抗肿瘤功效评估

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摘要

Tumor treating fields (TTFields) is an anticancer treatment that inhibits tumor growth with alternating electrical fields. Finite element (FE) methods have been used to estimate the TTFields intensity as a measure of treatment “dose”. However, TTFields efficacy also depends on field direction and exposure time. Here we propose a new FE based approach, which uses all these parameters to quantify the average field intensity and the amount of unwanted directional field correlation (fractional anisotropy, FA). The method is based on principal component decomposition of the sequential TTFields over one duty cycle. Using a realistic head model of a glioblastoma patient, we observed significant unwanted FA in many regions of the brain, which may potentially affect therapeutic efficacy. FA varied between different array layouts and indicated a different order of array performance than predicted from the field intensity. Tumor resection nullified differences in field distributions between layouts and increased FA considerably. Our results question the rationale for the use of macroscopically orthogonal array layouts to reduce field correlation and rather indicate that arrays should be placed to maximize pathology coverage and field intensity. The proposed calculation framework has several potential applications, incl. improved treatment planning, technology development, and accurate prognostication models. Future studies are required to validate the method.
机译：肿瘤治疗场（TTFields）是一种抗癌治疗方法，可通过交替电场来抑制肿瘤生长。有限元（FE）方法已用于估计TTFields强度，作为治疗“剂量”的量度。但是，TTFields的功效还取决于电场方向和曝光时间。在这里，我们提出了一种新的基于有限元的方法，该方法使用所有这些参数来量化平均场强和不想要的定向场相关量（分数各向异性，FA）。该方法基于一个工作周期内顺序TTField的主成分分解。使用胶质母细胞瘤患者的真实头部模型，我们在大脑的许多区域观察到了明显的有害FA，这可能会影响治疗效果。 FA在不同的阵列布局之间变化，表明阵列性能的顺序与根据场强预测的顺序不同。肿瘤切除消除了布局之间野外分布的差异，并显着增加了FA。我们的结果对使用宏观正交阵列布局以减少场相关性的理由提出了质疑，而是表明应放置阵列以最大程度地提高病理学覆盖率和场强。所提出的计算框架具有几个潜在的应用，包括。改进了治疗计划，技术开发和准确的预测模型。需要进一步的研究以验证该方法。

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