A mechanically coupled reaction–diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth. Issue 128 (31st March 2017)
- Record Type:
- Journal Article
- Title:
- A mechanically coupled reaction–diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth. Issue 128 (31st March 2017)
- Main Title:
- A mechanically coupled reaction–diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth
- Authors:
- Hormuth, David A.
Weis, Jared A.
Barnes, Stephanie L.
Miga, Michael I.
Rericha, Erin C.
Quaranta, Vito
Yankeelov, Thomas E. - Abstract:
- Abstract : While gliomas have been extensively modelled with a reaction–diffusion (RD) type equation it is most likely an oversimplification. In this study, three mathematical models of glioma growth are developed and systematically investigated to establish a framework for accurate prediction of changes in tumour volume as well as intra-tumoural heterogeneity. Tumour cell movement was described by coupling movement to tissue stress, leading to a mechanically coupled (MC) RD model. Intra-tumour heterogeneity was described by including a voxel-specific carrying capacity (CC) to the RD model. The MC and CC models were also combined in a third model. To evaluate these models, rats ( n = 14) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging over 10 days to estimate tumour cellularity. Model parameters were estimated from the first three imaging time points and then used to predict tumour growth at the remaining time points which were then directly compared to experimental data. The results in this work demonstrate that mechanical–biological effects are a necessary component of brain tissue tumour modelling efforts. The results are suggestive that a variable tissue carrying capacity is a needed model component to capture tumour heterogeneity. Lastly, the results advocate the need for additional effort towards capturing tumour-to-tissue infiltration.
- Is Part Of:
- Journal of the Royal Society interface. Volume 14:Issue 128(2017)
- Journal:
- Journal of the Royal Society interface
- Issue:
- Volume 14:Issue 128(2017)
- Issue Display:
- Volume 14, Issue 128 (2017)
- Year:
- 2017
- Volume:
- 14
- Issue:
- 128
- Issue Sort Value:
- 2017-0014-0128-0000
- Page Start:
- Page End:
- Publication Date:
- 2017-03-31
- Subjects:
- mathematical model -- cancer -- forecast -- brain -- glioma
Physical sciences -- Research -- Periodicals
Life sciences -- Research -- Periodicals
Interdisciplinary research -- Periodicals
570.5 - Journal URLs:
- https://royalsocietypublishing.org/journal/rsif ↗
- DOI:
- 10.1098/rsif.2016.1010 ↗
- Languages:
- English
- ISSNs:
- 1742-5689
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library STI - ELD Digital store
- Ingest File:
- 25061.xml