Name
Abstract | ||
|---|---|---|
Mathematical models have been ubiquitously employed in various applications. One of these applications that arose in the past few decades is cerebral tumor growth modeling. Simultaneously, medical imaging techniques, such as magnetic resonance imaging, computed tomography, and positron emission tomography, have witnessed great developments and become the primary clinical procedure in tumors diagnosis and detection. Studying tumor growth via mathematical models from medical images is an important application that is believed to play significant role in cancer treatment by predicting tumor evolution, quantifying the response to therapy, and the effective treatment planning of chemotherapy and/or radiotherapy. In this paper, we focus on the macroscopic growth modeling of brain tumors, mainly glioma, and highlight the current achievements in the state-of-the-art methods. In addition, we discuss some challenges and perspectives on this research that can further promote the research of this field. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2839681 | IEEE ACCESS |
Keywords | Field | DocType |
Mathematical modeling,cerebral tumors,glioma growth,macroscopic models,diffusive model,biomechanical model,chemotherapy,radiotherapy | Diffusion MRI,Computer science,Medical imaging,Glioma,Radiation treatment planning,Radiation therapy,Positron emission tomography,Medical physics,Cancer,Magnetic resonance imaging,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 2 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed El-Azab | 1 | 2 | 0.37 |
| Yousry M. AbdulAzeem | 2 | 7 | 2.50 |
| Ahmed M. Anter | 3 | 44 | 7.37 |
| Qingmao Hu | 4 | 160 | 19.73 |
| Tianfu Wang | 5 | 382 | 55.46 |
| Baiying Lei | 6 | 271 | 34.50 |