Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme

Autor: Michael Abdelmalik, William T. Phillips, David A. Hormuth, Chengyue Wu, Thomas J. R. Hughes, Ande Bao, Ryan T. Woodall, Andrew Brenner, Thomas E. Yankeelov
Přispěvatelé: Energy Technology, Group Van Brummelen
Rok vydání: 2021
Předmět:
Computer science
0206 medical engineering
02 engineering and technology
Computational fluid dynamics
Single-photon emission computed tomography
Glioblastoma multiforme
Convection
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
medicine
Calibration
Humans
Computational oncology
Radiation treatment planning
General Nursing
Bootstrapping (statistics)
Radioisotopes
Convection-enhanced delivery
medicine.diagnostic_test
Brain Neoplasms
Magnetic resonance imaging
020601 biomedical engineering
Confidence interval
Radiation therapy
Neoplasm Recurrence
Rhenium
Concordance correlation coefficient
Local
Glioblastoma/diagnostic imaging
Brain Neoplasms/diagnostic imaging
Neoplasm Recurrence
Local
Glioblastoma
Convection-Enhanced Delivery
Biomedical engineering
Zdroj: Biomed Phys Eng Express
Biomedical Physics & Engineering Express, 7(4):045012. Institute of Physics
ISSN: 2057-1976
DOI: 10.1088/2057-1976/ac02a6
Popis: 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.
Databáze: OpenAIRE
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