A model evaluation study for treatment planning of laser-induced thermal therapy.
A model evaluation study for treatment planning of laser-induced thermal therapy.
复制标题
DOI：
10.3109/02656736.2015.1055831
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Fuentes D
中科院分区：
文献类型：
--
作者：
Fahrenholtz SJ;Moon TY;Franco M;Medina D;Danish S;Gowda A;Shetty A;Maier F;Hazle JD;Stafford RJ;Warburton T;Fuentes D
提出了一种交叉验证分析，用于评估先验规划磁共振引导激光诱导热治疗（MRgLITT）程序在治疗局灶性病变脑组织中的计算机模型预测准确性。两个数学模型被认为是。(1)对瞬态Pennes生物传热方程进行了谱元离散化，预测了灌注组织中的激光诱导加热。(2)也被认为是一个封闭形式的算法预测的稳态传热从一个线性叠加的分析点源加热功能。预测准确性通过留一交叉验证（LOOCV）进行回顾性评估。模拟的热剂量和N = 22个MR测温数据集内包含的热剂量信息之间的Dice相似系数（DSC）定量评价建模预测。在LOOCV分析期间，瞬态模型的DSC平均值和中位数分别为0.7323和0.8001，22个DSC值中有15个超过DSC ≥ 0.7的成功标准。稳态模型的DSC平均值和中位数分别为0.6431和0.6770，22个中有10个通过。单样本单侧Wilcoxon符号秩检验表明，瞬态FEM模型达到了预测成功标准，DSC ≥ 0.7，具有统计学显著性。
A cross validation analysis evaluating computer model prediction accuracy for a priori planning magnetic resonance-guided laser induced thermal therapy (MRgLITT) procedures in treating focal diseased brain tissue is presented. Two mathematical models are considered. (1) A spectral element discretization of the transient Pennes bioheat transfer equation is implemented to predict the laser induced heating in perfused tissue. (2) A closed-form algorithm for predicting the steady state heat transfer from a linear superposition of analytic point source heating functions is also considered. Prediction accuracy is retrospectively evaluated via leave-one-out cross validation (LOOCV). Modeling predictions are quantitatively evaluated in terms of a Dice similarity coefficient (DSC) between the simulated thermal dose and thermal dose information contained within N = 22 MR thermometry datasets. During LOOCV analysis, the transient model’s DSC mean and median is 0.7323 and 0.8001, respectively, with 15 of 22 DSC values exceeding the success criterion of DSC ≥ 0.7. The steady state model’s DSC mean and median is 0.6431 and 0.6770, respectively, with 10 of 22 passing. A one-sample, one-sided Wilcoxon signed rank test indicates that the transient FEM model achieves the prediction success critera, DSC ≥ 0.7, at a statistically significant level.