Journal
CANCER RESEARCH
Volume 69, Issue 10, Pages 4493-4501Publisher
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/0008-5472.CAN-08-3834
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Funding
- Cullen Trust of Health Care
- National Cancer Institute, Department of Defense
- NSF Division of Mathematical Sciences
- Center for Cancer Nanotechnology Excellence [NIGMS-GM47368, NINDS-NSO-46810, NCI U54, TR CA119367]
- [NSF-DMS 0818104]
- [NIH-P50GM76516]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0818030, 0818126] Funding Source: National Science Foundation
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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]
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