Modeling tumor growth and treatment response based on quantitative imaging data.
Modeling tumor growth and treatment response based on quantitative imaging data.
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DOI：
10.1039/b921497f
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发表时间：
2010-08
期刊：
影响因子：
--
通讯作者：
Gore JC
中科院分区：
文献类型：
--
作者：
Yankeelov TE;Atuegwu NC;Deane NG;Gore JC
我们回顾了预测肿瘤生长和治疗反应的当前方法，该方法将非侵入性成像数据与癌症进展的数学模型相结合，并提出了一些新的方向，以将定量成像测量结果与此类数值分析相结合。从历史上看，肿瘤建模已通过仅通过侵入性方法或分离的体外或离体系统来测量的参数描述。这限制了此类模型的实际实用性，因为不可能通过实验测试其预测。三维磁共振成像，单光子发射计算机断层扫描和正电子发射断层扫描技术的最新进展为非侵入性肿瘤的相关分子和细胞特征的测量提供了新的机会，并具有高空间分辨率。这些数据可以纳入肿瘤的数学模型中。我们重点介绍了这种方法的一些最新示例，并确定了几个简单的示例，这些示例允许可以通过成像来衡量的参数来重塑常规的肿瘤生长模型，从而提高了可以在设计和约束模型中测试的可能性，这些模型可以在临床实践。我们希望这种观点能在这个不断发展且令人兴奋的领域中刺激进一步的工作。
We review current approaches to predicting tumor growth and treatment response that combine non-invasive imaging data with mathematical models of cancer progression, and propose some new directions for integrating quantitative imaging measurements with such numerical analyses. Historically, tumor modeling has been described by parameters that are measurable by invasive methods only or in isolated in vitro or ex vivo systems. This limits the practical usefulness of such models because it is not possible to test their predictions experimentally. Recent advances in three-dimensional magnetic resonance imaging, single photon emission computed tomography, and positron emission tomography techniques provide new opportunities to acquire measurements of relevant molecular and cellular features of tumors non-invasively and with high spatial resolution. Such data can be incorporated into mathematical models of tumors. We highlight some recent examples of this approach and identify several simple examples that allow for conventional mathematical models of tumor growth to be recast in terms of parameters that can be measured by imaging, thus raising the possibility of designing and constraining models that can be tested in clinical practice. It is our hope that this Perspective will stimulate further work in this evolving and exciting field.
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影响因子：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
影响因子：
11.5
作者：
Hall, DE;Moffat, BA;Ross, BD
通讯作者：
Ross, BD
影响因子：
3.3
作者：
Damon, BM;Ding, ZH;Gore, JC
通讯作者：
Gore, JC
DOI：
10.1158/1078-0432.ccr-08-0239
发表时间：
2008-11-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Manning HC;Merchant NB;Foutch AC;Virostko JM;Wyatt SK;Shah C;McKinley ET;Xie J;Mutic NJ;Washington MK;LaFleur B;Tantawy MN;Peterson TE;Ansari MS;Baldwin RM;Rothenberg ML;Bornhop DJ;Gore JC;Coffey RJ
通讯作者：
Coffey RJ
影响因子：
5.5
作者：
Hagmann, Patric;Jonasson, Lisa;Meuli, Reto
通讯作者：
Meuli, Reto