Abstract
Glioblastoma multiforme is the most common and deadly form of primary brain cancer. Even with aggressive treatment consisting of surgical resection, chemotherapy, and external beam radiation therapy, response rates remain poor. In an attempt to improve outcomes, investigators have developed nanoliposomes loaded with 186Re, which are capable of delivering a large dose (< 1000 Gy) of highly localized β- radiation to the tumor, with minimal exposure to healthy brain tissue. Additionally, 186Re also emits gamma radiation (137 keV) so that it's spatio-temporal distribution can be tracked through single photon emission computed tomography. Planning the delivery of these particles is challenging, especially in cases where the tumor borders the ventricles or previous resection cavities. To address this issue, we are developing a finite element model of convection enhanced delivery for nanoliposome carriers of radiotherapeutics. The model is patient specific, informed by each individual's own diffusion-weighted and contrast-enhanced magnetic resonance imaging data. The model is then calibrated to single photon emission computed tomography data, acquired at multiple time points mid- and post-infusion, and validation is performed by comparing model predictions to imaging measurements obtained at future time points. After initial calibration to a one SPECT image, the model is capable of recapitulating the distribution volume of RNL with a DICE coefficient of 0.88 and a PCC of 0.80. We also demonstrate evidence of restricted flow due to large nanoparticle size in comparison to interstitial pore size.
| Original language | English |
|---|---|
| Title of host publication | Medical Imaging 2019 |
| Subtitle of host publication | Physics of Medical Imaging |
| Editors | Taly Gilat Schmidt, Guang-Hong Chen, Hilde Bosmans |
| Place of Publication | Bellingham |
| Publisher | SPIE |
| Number of pages | 13 |
| ISBN (Electronic) | 9781510625433 |
| DOIs | |
| Publication status | Published - 1 Mar 2019 |
| Event | Medical Imaging 2019: Physics of Medical Imaging - San Diego, United States Duration: 17 Feb 2019 → 20 Feb 2019 |
Publication series
| Name | Proceedings of SPIE |
|---|---|
| Volume | 10948 |
Conference
| Conference | Medical Imaging 2019: Physics of Medical Imaging |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 17/02/19 → 20/02/19 |
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Keywords
- Brachytherapy
- Convection-enhanced delivery
- Finite elements
- Fluid dynamics
- Glioblastoma
- Liposomes
- Modeling
- Theranostics
- convection-enhanced delivery
- modeling
- liposomes
- fluid dynamics
- glioblastoma
- theranostics
- brachytherapy
- finite elements
Cite this
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Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment. / Woodall, Ryan T.; Hormuth, David A.; Abdelmalik, Michael R.A.; Wu, Chengyue; Feng, Xinzeng; Phillips, William T.; Bao, Ande; Hughes, Thomas J.R.; Brenner, Andrew J.; Yankeelov, Thomas E.
Medical Imaging 2019: Physics of Medical Imaging. ed. / Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans. Bellingham : SPIE, 2019. 109483M (Proceedings of SPIE; Vol. 10948).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment
AU - Woodall, Ryan T.
AU - Hormuth, David A.
AU - Abdelmalik, Michael R.A.
AU - Wu, Chengyue
AU - Feng, Xinzeng
AU - Phillips, William T.
AU - Bao, Ande
AU - Hughes, Thomas J.R.
AU - Brenner, Andrew J.
AU - Yankeelov, Thomas E.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - Glioblastoma multiforme is the most common and deadly form of primary brain cancer. Even with aggressive treatment consisting of surgical resection, chemotherapy, and external beam radiation therapy, response rates remain poor. In an attempt to improve outcomes, investigators have developed nanoliposomes loaded with 186Re, which are capable of delivering a large dose (< 1000 Gy) of highly localized β- radiation to the tumor, with minimal exposure to healthy brain tissue. Additionally, 186Re also emits gamma radiation (137 keV) so that it's spatio-temporal distribution can be tracked through single photon emission computed tomography. Planning the delivery of these particles is challenging, especially in cases where the tumor borders the ventricles or previous resection cavities. To address this issue, we are developing a finite element model of convection enhanced delivery for nanoliposome carriers of radiotherapeutics. The model is patient specific, informed by each individual's own diffusion-weighted and contrast-enhanced magnetic resonance imaging data. The model is then calibrated to single photon emission computed tomography data, acquired at multiple time points mid- and post-infusion, and validation is performed by comparing model predictions to imaging measurements obtained at future time points. After initial calibration to a one SPECT image, the model is capable of recapitulating the distribution volume of RNL with a DICE coefficient of 0.88 and a PCC of 0.80. We also demonstrate evidence of restricted flow due to large nanoparticle size in comparison to interstitial pore size.
AB - Glioblastoma multiforme is the most common and deadly form of primary brain cancer. Even with aggressive treatment consisting of surgical resection, chemotherapy, and external beam radiation therapy, response rates remain poor. In an attempt to improve outcomes, investigators have developed nanoliposomes loaded with 186Re, which are capable of delivering a large dose (< 1000 Gy) of highly localized β- radiation to the tumor, with minimal exposure to healthy brain tissue. Additionally, 186Re also emits gamma radiation (137 keV) so that it's spatio-temporal distribution can be tracked through single photon emission computed tomography. Planning the delivery of these particles is challenging, especially in cases where the tumor borders the ventricles or previous resection cavities. To address this issue, we are developing a finite element model of convection enhanced delivery for nanoliposome carriers of radiotherapeutics. The model is patient specific, informed by each individual's own diffusion-weighted and contrast-enhanced magnetic resonance imaging data. The model is then calibrated to single photon emission computed tomography data, acquired at multiple time points mid- and post-infusion, and validation is performed by comparing model predictions to imaging measurements obtained at future time points. After initial calibration to a one SPECT image, the model is capable of recapitulating the distribution volume of RNL with a DICE coefficient of 0.88 and a PCC of 0.80. We also demonstrate evidence of restricted flow due to large nanoparticle size in comparison to interstitial pore size.
KW - Brachytherapy
KW - Convection-enhanced delivery
KW - Finite elements
KW - Fluid dynamics
KW - Glioblastoma
KW - Liposomes
KW - Modeling
KW - Theranostics
KW - convection-enhanced delivery
KW - modeling
KW - liposomes
KW - fluid dynamics
KW - glioblastoma
KW - theranostics
KW - brachytherapy
KW - finite elements
UR - http://www.scopus.com/inward/record.url?scp=85068390320&partnerID=8YFLogxK
U2 - 10.1117/12.2512867
DO - 10.1117/12.2512867
M3 - Conference contribution
AN - SCOPUS:85068390320
T3 - Proceedings of SPIE
BT - Medical Imaging 2019
A2 - Schmidt, Taly Gilat
A2 - Chen, Guang-Hong
A2 - Bosmans, Hilde
PB - SPIE
CY - Bellingham
ER -