APA
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Woodall, R. T., II, D. A. H., Wu, C., Abdelmalik, M., Phillips, W., Bao, A., … Yankeelov, T. (2021). Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme. Biomedical Engineering and Physics Express.
Chicago/Turabian
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Woodall, Ryan T., David A. Hormuth II, Chengyue Wu, M. Abdelmalik, W. Phillips, A. Bao, T. Hughes, A. Brenner, and T. Yankeelov. “Patient Specific, Imaging-Informed Modeling of Rhenium-186 Nanoliposome Delivery via Convection-Enhanced Delivery in Glioblastoma Multiforme.” Biomedical engineering and physics express (2021).
MLA
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Woodall, Ryan T., et al. “Patient Specific, Imaging-Informed Modeling of Rhenium-186 Nanoliposome Delivery via Convection-Enhanced Delivery in Glioblastoma Multiforme.” Biomedical Engineering and Physics Express, 2021.
BibTeX Click to copy
@article{ryan2021a,
title = {Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme},
year = {2021},
journal = {Biomedical engineering and physics express},
author = {Woodall, Ryan T. and II, David A. Hormuth and Wu, Chengyue and Abdelmalik, M. and Phillips, W. and Bao, A. and Hughes, T. and Brenner, A. and Yankeelov, T.}
}
Convection-enhanced delivery of rhenium-186 (186Re)-nanoliposomes is a promising approach to provide precise delivery of large localized doses of radiation for patients with recurrent glioblastoma multiforme. Current approaches for treatment planning utilizing convection-enhanced delivery are designed for small molecule drugs and not for larger particles such as 186Re-nanoliposomes. To enable the treatment planning for 186Re-nanoliposomes delivery, we have developed a computational fluid dynamics approach to predict the distribution of nanoliposomes for individual patients. In this work, we construct, calibrate, and validate a family of computational fluid dynamics models to predict the spatio-temporal distribution of 186Re-nanoliposomes within the brain, utilizing patient-specific pre-operative magnetic resonance imaging (MRI) to assign material properties for an advection-diffusion transport model. The model family is calibrated to single photon emission computed tomography (SPECT) images acquired during and after the infusion of 186Re-nanoliposomes for five patients enrolled in a Phase I/II trial (NCT Number NCT01906385), and is validated using a leave-one-out bootstrapping methodology for predicting the final distribution of the particles. After calibration, our models are capable of predicting the mid-delivery and final spatial distribution of 186Re-nanoliposomes with a Dice value of 0.69 ± 0.18 and a concordance correlation coefficient of 0.88 ± 0.12 (mean ± 95% confidence interval), using only the patient-specific, pre-operative MRI data, and calibrated model parameters from prior patients. These results demonstrate a proof-of-concept for a patient-specific modeling framework, which predicts the spatial distribution of nanoparticles. Further development of this approach could enable optimizing catheter placement for future studies employing convection-enhanced delivery.
