dc.contributor.author |
Akbarpour Ghazani, Mehran |
|
dc.contributor.author |
Saghafian, Mohsen |
|
dc.contributor.author |
Jalali, Peyman |
|
dc.contributor.author |
Soltani, Madjid |
|
dc.coverage.spatial |
England |
|
dc.date.accessioned |
2022-11-17T01:59:41Z |
|
dc.date.available |
2022-11-17T01:59:41Z |
|
dc.date.issued |
2021-11 |
|
dc.identifier.citation |
(2021). Proceedings of the Institution of Mechanical Engineers Part H: Journal of Engineering in Medicine, 235(11), 1335-1355. |
|
dc.identifier.issn |
0954-4119 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/61887 |
|
dc.description.abstract |
Uncontrolled proliferation of cells in a tissue caused by genetic mutations inside a cell is referred to as a tumor. A tumor which grows rapidly encounters a barrier when it grows to a certain size in presence of preexisting vasculature. This is the time when it has to find a way to go on the growth. The tumor starts to secrete tumor angiogenic factors (TAFs) and stimulate preexisting vessels to grow new sprouts. These new sprouts will find their way to the tumor in the extracellular matrix (ECM) by the gradient of TAF. As these new capillaries anastomose and reach tumor, fresh oxygen is available for the tumor and it will reinitiate the growth. Number of initial sprouts, distance of initial tumor cells from the vessel(s) and initial density of the tumor at the time of sprout formation are questions which are to be investigated. In the present study, the aim is to find the response of tumor cells and vessels to the reciprocal effects of each other in different circumstances in the tissue. Together with a mathematical formulation, a radial basis function (RBF) neural network is established to predict the number of tumor cells at different circumstances including size and distance of initial tumors from the parent vessel. A final formulation is given for the final number of tumor cells as a function of initial tumor size and distance between a parent vessel and a tumor. Results of this simulation demonstrate that, increasing the distance between a tumor and a parent vessel decreases the number of final tumor cells. Specially, this decrement becomes faster beyond a certain distance. Moreover, initial tumors in bigger domains must become much bigger before inducing angiogenesis which makes it harder for them to survive. |
|
dc.format.medium |
Print-Electronic |
|
dc.language |
eng |
|
dc.publisher |
SAGE Publications |
|
dc.relation.ispartofseries |
Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
Humans |
|
dc.subject |
Neoplasms |
|
dc.subject |
Tumor Burden |
|
dc.subject |
Models, Biological |
|
dc.subject |
Computer Simulation |
|
dc.subject |
Tumor Microenvironment |
|
dc.subject |
Neural Networks, Computer |
|
dc.subject |
Mathematical oncology |
|
dc.subject |
angiogenesis |
|
dc.subject |
artificial neural network |
|
dc.subject |
hybrid tumor modeling |
|
dc.subject |
radial basis function |
|
dc.subject |
vascular tumor |
|
dc.subject |
Cancer |
|
dc.subject |
0903 Biomedical Engineering |
|
dc.subject |
0913 Mechanical Engineering |
|
dc.title |
Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment. |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1177/09544119211028380 |
|
pubs.issue |
11 |
|
pubs.begin-page |
1335 |
|
pubs.volume |
235 |
|
dc.date.updated |
2022-10-04T01:34:26Z |
|
dc.rights.holder |
Copyright: The authors |
en |
dc.identifier.pmid |
34247529 (pubmed) |
|
pubs.author-url |
https://www.ncbi.nlm.nih.gov/pubmed/34247529 |
|
pubs.end-page |
1355 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
research-article |
|
pubs.subtype |
Journal Article |
|
pubs.elements-id |
910790 |
|
pubs.org-id |
Bioengineering Institute |
|
dc.identifier.eissn |
2041-3033 |
|
pubs.record-created-at-source-date |
2022-10-04 |
|
pubs.online-publication-date |
2021-07-10 |
|