Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment

Ryan T. Woodall, David A. Hormuth, Michael R.A. Abdelmalik, Chengyue Wu, Xinzeng Feng, William T. Phillips, Ande Bao, Thomas J.R. Hughes, Andrew J. Brenner, Thomas E. Yankeelov

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

LanguageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationPhysics of Medical Imaging
EditorsHilde Bosmans, Guang-Hong Chen, Taly Gilat Schmidt
Place of PublicationBellingham
PublisherSPIE
Number of pages13
ISBN (Electronic)9781510625433
DOIs
StatePublished - 1 Mar 2019
EventMedical Imaging 2019: Physics of Medical Imaging - San Diego, United States
Duration: 17 Feb 201920 Feb 2019

Publication series

NameProceedings of SPIE
Volume10948

Conference

ConferenceMedical Imaging 2019: Physics of Medical Imaging
CountryUnited States
CitySan Diego
Period17/02/1920/02/19

Fingerprint

Glioblastoma
Single-Photon Emission-Computed Tomography
Imaging techniques
Single photon emission computed tomography
Beta Particles
Convection
Gamma Rays
brain
Tumors
Brain
delivery
Brain Neoplasms
tumors
Nanoparticles
Calibration
tomography
Neoplasms
Radiotherapy
Therapeutics
Research Personnel

Keywords

  • Brachytherapy
  • Convection-enhanced delivery
  • Finite elements
  • Fluid dynamics
  • Glioblastoma
  • Liposomes
  • Modeling
  • Theranostics

Cite this

Woodall, R. T., Hormuth, D. A., Abdelmalik, M. R. A., Wu, C., Feng, X., Phillips, W. T., ... Yankeelov, T. E. (2019). Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment. In H. Bosmans, G-H. Chen, & T. G. Schmidt (Eds.), Medical Imaging 2019: Physics of Medical Imaging [109483M] (Proceedings of SPIE; Vol. 10948). Bellingham: SPIE. DOI: 10.1117/12.2512867
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./ Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment. Medical Imaging 2019: Physics of Medical Imaging. editor / Hilde Bosmans ; Guang-Hong Chen ; Taly Gilat Schmidt. Bellingham : SPIE, 2019. (Proceedings of SPIE).
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keywords = "Brachytherapy, Convection-enhanced delivery, Finite elements, Fluid dynamics, Glioblastoma, Liposomes, Modeling, Theranostics",
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Woodall, RT, Hormuth, DA, Abdelmalik, MRA, Wu, C, Feng, X, Phillips, WT, Bao, A, Hughes, TJR, Brenner, AJ & Yankeelov, TE 2019, Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment. in H Bosmans, G-H Chen & TG Schmidt (eds), Medical Imaging 2019: Physics of Medical Imaging., 109483M, Proceedings of SPIE, vol. 10948, SPIE, Bellingham, Medical Imaging 2019: Physics of Medical Imaging, San Diego, United States, 17/02/19. DOI: 10.1117/12.2512867

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. / Hilde Bosmans; Guang-Hong Chen; Taly Gilat Schmidt. Bellingham : SPIE, 2019. 109483M (Proceedings of SPIE; Vol. 10948).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Woodall RT, Hormuth DA, Abdelmalik MRA, Wu C, Feng X, Phillips WT et al. Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment. In Bosmans H, Chen G-H, Schmidt TG, editors, Medical Imaging 2019: Physics of Medical Imaging. Bellingham: SPIE. 2019. 109483M. (Proceedings of SPIE). Available from, DOI: 10.1117/12.2512867