Multiparameter Computational Modeling of Tumor Invasion

被引：97

作者：

Bearer, Elaine L. ^{[2
,3
,4
]}

Lowengrub, John S. ^{[5
,6
]}

Frieboes, Hermann B. ^{[1
]}

Chuang, Yao-Li ^{[1
]}

Jin, Fang ^{[5
]}

Wise, Steven M. ^{[5
,13
]}

Ferrari, Mauro ^{[7
,8
,9
,11
]}

Agus, David B. ^{[14
]}

Cristini, Vittorio ^{[1
,8
,10
,12
]}

机构：

[1] Univ Texas Hlth Sci Ctr, Sch Hlth Informat Sci, Houston, TX 77030 USA

[2] Brown Univ, Dept Pathol & Lab Med, Providence, RI 02912 USA

[3] Brown Univ, Div Engn, Providence, RI 02912 USA

[4] CALTECH, Dept Biol, Pasadena, CA 91125 USA

[5] Univ Calif Irvine, Dept Math, Irvine, CA 92717 USA

[6] Univ Calif Irvine, Dept Biomed Engn, Irvine, CA 92717 USA

[7] Univ Texas Hlth Sci Ctr, Div Nanomed, Houston, TX 77030 USA

[8] Univ Texas Hlth Sci Ctr, Dept Biomed Engn, Houston, TX 77030 USA

[9] Univ Texas MD Anderson Canc Ctr, Dept Expt Therapeut, Houston, TX 77030 USA

[10] Univ Texas MD Anderson Canc Ctr, Dept Syst Biol, Houston, TX 77030 USA

[11] Rice Univ, Dept Bioengn, Houston, TX USA

[12] Univ Texas Austin, Dept Biomed Engn, Austin, TX 78712 USA

[13] Univ Tennessee, Dept Math, Knoxville, TN 37996 USA

[14] Univ So Calif, USC Ctr Appl Mol Med, Los Angeles, CA USA

来源：

CANCER RESEARCH | 2009年 / 69卷 / 10期

基金：

美国国家科学基金会;

关键词：

GROWTH-FACTOR-RECEPTOR; NONLINEAR SIMULATION; COMPUTER-SIMULATION; MALIGNANT GLIOMA; GLIOBLASTOMA; ANGIOGENESIS; CANCER; MICROENVIRONMENT; THERAPY; MORPHOLOGY;

D O I：

10.1158/0008-5472.CAN-08-3834

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Clinical outcome prognostication in oncology is a guiding principle in therapeutic choice. A wealth of qualitative empirical evidence links disease progression with tumor morphology, histopathology, invasion, and associated molecular phenomena. However, the quantitative contribution of each of the known parameters in this progression remains elusive. Mathematical modeling can provide the capability to quantify the connection between variables governing growth, prognosis, and treatment outcome. By quantifying the link between the tumor boundary morphology and the invasive phenotype, this work provides a quantitative tool for the study of tumor progression and diagnostic/prognostic applications. This establishes a framework for monitoring system perturbation towards development of therapeutic strategies and correlation to clinical outcome for prognosis. [Cancer Res 2009;69(10):4493-501]

引用

页码：4493 / 4501

页数：9

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