Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy.
Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy.
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DOI：
10.1002/mrm.23203
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发表时间：
2011-12
影响因子：
3.3
通讯作者：
Yankeelov, Thomas E.
中科院分区：
文献类型：
--
作者：
Atuegwu, Nkiruka C.;Arlinghaus, Lori R.;Li, Xia;BrianWelch, E.;Chakravarthy, Bapsi A.;Gore, John C.;Yankeelov, Thomas E.
在治疗过程中获得的扩散加权磁共振成像（DW-MRI）数据可用于估计肿瘤增殖率，并且可以使用估计的率来预测治疗结束时肿瘤细胞。在一个周期之后，在所有voxel和整个感兴趣的肿瘤区域（ROI）计算了明显的差异系数（ADC）值（ROI）。从前两个时间点，然后与肿瘤生长的逻辑模型一起使用，以预测预测的肿瘤细胞数量与相应的实验数据相关。 0.004），在将3×3的平均滤波器应用于ADC数据后，逐素分析产生的Pearson相关系数为0.70±0.10（P <0.05）。
Diffusion-weighted magnetic resonance imaging (DW-MRI) data obtained early in the course of therapy can be used to estimate tumor proliferation rates, and the estimated rates can be used to predict tumor cellularity at the conclusion of therapy. Six patients underwent DW-MRI immediately before, after one cycle, and after all cycles of neoadjuvant chemotherapy. Apparent diffusion coefficient (ADC) values were calculated for each voxel and for a whole tumor region of interest (ROI). Proliferation rates were estimated using the ADC data from the first two time points, and then used with the logistic model of tumor growth to predict cellularity after therapy. The predicted number of tumor cells was then correlated to the corresponding experimental data. Pearson’s correlation coefficient for the ROI analysis yielded 0.95 (p = 0.004), and, after applying a 3×3 mean filter to the ADC data, the voxel-by-voxel analysis yielded a Pearson correlation coefficient of 0.70±0.10 (p < 0.05).
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DOI：
10.1039/b921497f
发表时间：
2010-08
期刊：
Integrative biology : quantitative biosciences from nano to macro
影响因子：
--
作者：
Yankeelov TE;Atuegwu NC;Deane NG;Gore JC
通讯作者：
Gore JC
影响因子：
2.5
作者：
Anderson, AW;Xie, J;Gore, JC
通讯作者：
Gore, JC
影响因子：
3.5
作者：
Atuegwu NC;Gore JC;Yankeelov TE
通讯作者：
Yankeelov TE
影响因子：
3.3
作者：
Ellingson, Benjamin M.;LaViolette, Peter S.;Schmainda, Kathleen M.
通讯作者：
Schmainda, Kathleen M.
影响因子：
2.5
作者：
Yankeelov, Thomas E.;Lepage, Martin;Gore, John C.
通讯作者：
Gore, John C.