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Mendeley readers
| Chapter title |
Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details
|
|---|---|
| Chapter number | 11 |
| Book title |
Cancer Systems Biology
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7493-1_11 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7492-4, 978-1-4939-7493-1
|
| Authors |
David A. HormuthII, Stephanie L. Eldridge, Jared A. Weis, Michael I. Miga, Thomas E. Yankeelov, David A. Hormuth |
| Abstract |
Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth. |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 26% |
| Student > Bachelor | 4 | 17% |
| Student > Postgraduate | 3 | 13% |
| Student > Doctoral Student | 1 | 4% |
| Researcher | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 7 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 6 | 26% |
| Engineering | 4 | 17% |
| Physics and Astronomy | 2 | 9% |
| Medicine and Dentistry | 2 | 9% |
| Computer Science | 1 | 4% |
| Other | 2 | 9% |
| Unknown | 6 | 26% |