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

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

doi:10.1088/2057-1976/ac02a6

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

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

Research output: Contribution to journal › Article › Academic › peer-review

16 Citations (Scopus)

Abstract

Convection-enhanceddeliveryofrhenium-186(¹⁸⁶Re)-nanoliposomesisapromisingapproachto provideprecisedeliveryoflargelocalizeddosesofradiationforpatientswithrecurrentglioblastoma multiforme.Currentapproachesfortreatmentplanningutilizingconvection-enhanceddeliveryare designedforsmallmoleculedrugsandnotforlargerparticlessuchas¹⁸⁶Re-nanoliposomes.Toenable thetreatmentplanningfor¹⁸⁶Re-nanoliposomesdelivery,wehavedevelopedacomputationalfluid dynamicsapproachtopredictthedistributionofnanoliposomesforindividualpatients.Inthiswork,we construct,calibrate,andvalidateafamilyofcomputationalfluiddynamicsmodelstopredictthespatiotemporaldistributionof¹⁸⁶Re-nanoliposomeswithinthebrain,utilizingpatient-specificpre-operative magneticresonanceimaging(MRI)toassignmaterialpropertiesforanadvection-diffusiontransport model.Themodelfamilyiscalibratedtosinglephotonemissioncomputedtomography(SPECT) imagesacquiredduringandaftertheinfusionof¹⁸⁶Re-nanoliposomesforfivepatientsenrolledina PhaseI/IItrial(NCTNumberNCT01906385),andisvalidatedusingaleave-one-outbootstrapping methodologyforpredictingthefinaldistributionoftheparticles.Aftercalibration,ourmodelsare capableofpredictingthemid-deliveryandfinalspatialdistributionof¹⁸⁶Re-nanoliposomeswithaDice valueof0.69 ± 0.18andaconcordancecorrelationcoefficientof0.88 ± 0.12(mean ± 95%confidence interval),usingonlythepatient-specific,pre-operativeMRIdata,andcalibratedmodelparametersfrom priorpatients.Theseresultsdemonstrateaproof-of-conceptforapatient-specificmodelingframework, whichpredictsthespatialdistributionofnanoparticles.Furtherdevelopmentofthisapproachcould enableoptimizingcatheterplacementforfuturestudiesemployingconvection-enhanceddelivery.

Original language	English
Article number	045012
Number of pages	15
Journal	Biomedical Physics & Engineering Express
Volume	7
Issue number	4
DOIs	https://doi.org/10.1088/2057-1976/ac02a6
Publication status	Published - Jul 2021

Funding

We thank the National Institutes of Health for funding through R01 CA235800, T32 EB007507, NCI 1R01 CA186193 and NCI 1U01CA253540, and 1U01 CA174706. We thank CPRIT for funding through RR160005. T.E.Y. is a CPRIT Scholar of Cancer Research. We thank the American Association of Physicists in Medicine for Research Seed Funding. We offer a sincere thank you to all the patients who volunteer to participate in our studies; your strength and courage are examples for all of us.

Funders	Funder number
National Institutes of Health
National Cancer Institute	R01CA186193, U01CA253540, R01CA235800, U01CA174706
National Institute of Biomedical Imaging and Bioengineering	T32EB007507

Keywords

Computational fluid dynamics
Computational oncology
Convection-enhanced delivery
Glioblastoma multiforme
Radiation therapy
Glioblastoma/diagnostic imaging
Brain Neoplasms/diagnostic imaging
Humans
Neoplasm Recurrence, Local
Radioisotopes
Rhenium
Convection

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10.1088/2057-1976/ac02a6

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Woodall, R. T., Hormuth, D. A., Wu, C., Abdelmalik, M. R. A., Phillips, W. T., Bao, A., Hughes, T. J. R., Brenner, A. J., & Yankeelov, T. E. (2021). Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme. Biomedical Physics & Engineering Express, 7(4), Article 045012. https://doi.org/10.1088/2057-1976/ac02a6

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

abstract = "Convection-enhanceddeliveryofrhenium-186(186Re)-nanoliposomesisapromisingapproachto provideprecisedeliveryoflargelocalizeddosesofradiationforpatientswithrecurrentglioblastoma multiforme.Currentapproachesfortreatmentplanningutilizingconvection-enhanceddeliveryare designedforsmallmoleculedrugsandnotforlargerparticlessuchas186Re-nanoliposomes.Toenable thetreatmentplanningfor186Re-nanoliposomesdelivery,wehavedevelopedacomputationalfluid dynamicsapproachtopredictthedistributionofnanoliposomesforindividualpatients.Inthiswork,we construct,calibrate,andvalidateafamilyofcomputationalfluiddynamicsmodelstopredictthespatiotemporaldistributionof186Re-nanoliposomeswithinthebrain,utilizingpatient-specificpre-operative magneticresonanceimaging(MRI)toassignmaterialpropertiesforanadvection-diffusiontransport model.Themodelfamilyiscalibratedtosinglephotonemissioncomputedtomography(SPECT) imagesacquiredduringandaftertheinfusionof186Re-nanoliposomesforfivepatientsenrolledina PhaseI/IItrial(NCTNumberNCT01906385),andisvalidatedusingaleave-one-outbootstrapping methodologyforpredictingthefinaldistributionoftheparticles.Aftercalibration,ourmodelsare capableofpredictingthemid-deliveryandfinalspatialdistributionof186Re-nanoliposomeswithaDice valueof0.69 ± 0.18andaconcordancecorrelationcoefficientof0.88 ± 0.12(mean ± 95%confidence interval),usingonlythepatient-specific,pre-operativeMRIdata,andcalibratedmodelparametersfrom priorpatients.Theseresultsdemonstrateaproof-of-conceptforapatient-specificmodelingframework, whichpredictsthespatialdistributionofnanoparticles.Furtherdevelopmentofthisapproachcould enableoptimizingcatheterplacementforfuturestudiesemployingconvection-enhanceddelivery.",

keywords = "Computational fluid dynamics, Computational oncology, Convection-enhanced delivery, Glioblastoma multiforme, Radiation therapy, Glioblastoma/diagnostic imaging, Brain Neoplasms/diagnostic imaging, Humans, Neoplasm Recurrence, Local, Radioisotopes, Rhenium, Convection",

author = "Woodall, {Ryan T.} and Hormuth, {David A.} and Chengyue Wu and Abdelmalik, {Michael R.A.} and Phillips, {William T.} and Ande Bao and Hughes, {Thomas J.R.} and Brenner, {Andrew J.} and Yankeelov, {Thomas E.}",

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doi = "10.1088/2057-1976/ac02a6",

language = "English",

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T1 - Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme

AU - Woodall, Ryan T.

AU - Hormuth, David A.

AU - Wu, Chengyue

AU - Abdelmalik, Michael R.A.

AU - Phillips, William T.

AU - Bao, Ande

AU - Hughes, Thomas J.R.

AU - Brenner, Andrew J.

AU - Yankeelov, Thomas E.

PY - 2021/7

Y1 - 2021/7

N2 - Convection-enhanceddeliveryofrhenium-186(186Re)-nanoliposomesisapromisingapproachto provideprecisedeliveryoflargelocalizeddosesofradiationforpatientswithrecurrentglioblastoma multiforme.Currentapproachesfortreatmentplanningutilizingconvection-enhanceddeliveryare designedforsmallmoleculedrugsandnotforlargerparticlessuchas186Re-nanoliposomes.Toenable thetreatmentplanningfor186Re-nanoliposomesdelivery,wehavedevelopedacomputationalfluid dynamicsapproachtopredictthedistributionofnanoliposomesforindividualpatients.Inthiswork,we construct,calibrate,andvalidateafamilyofcomputationalfluiddynamicsmodelstopredictthespatiotemporaldistributionof186Re-nanoliposomeswithinthebrain,utilizingpatient-specificpre-operative magneticresonanceimaging(MRI)toassignmaterialpropertiesforanadvection-diffusiontransport model.Themodelfamilyiscalibratedtosinglephotonemissioncomputedtomography(SPECT) imagesacquiredduringandaftertheinfusionof186Re-nanoliposomesforfivepatientsenrolledina PhaseI/IItrial(NCTNumberNCT01906385),andisvalidatedusingaleave-one-outbootstrapping methodologyforpredictingthefinaldistributionoftheparticles.Aftercalibration,ourmodelsare capableofpredictingthemid-deliveryandfinalspatialdistributionof186Re-nanoliposomeswithaDice valueof0.69 ± 0.18andaconcordancecorrelationcoefficientof0.88 ± 0.12(mean ± 95%confidence interval),usingonlythepatient-specific,pre-operativeMRIdata,andcalibratedmodelparametersfrom priorpatients.Theseresultsdemonstrateaproof-of-conceptforapatient-specificmodelingframework, whichpredictsthespatialdistributionofnanoparticles.Furtherdevelopmentofthisapproachcould enableoptimizingcatheterplacementforfuturestudiesemployingconvection-enhanceddelivery.

AB - Convection-enhanceddeliveryofrhenium-186(186Re)-nanoliposomesisapromisingapproachto provideprecisedeliveryoflargelocalizeddosesofradiationforpatientswithrecurrentglioblastoma multiforme.Currentapproachesfortreatmentplanningutilizingconvection-enhanceddeliveryare designedforsmallmoleculedrugsandnotforlargerparticlessuchas186Re-nanoliposomes.Toenable thetreatmentplanningfor186Re-nanoliposomesdelivery,wehavedevelopedacomputationalfluid dynamicsapproachtopredictthedistributionofnanoliposomesforindividualpatients.Inthiswork,we construct,calibrate,andvalidateafamilyofcomputationalfluiddynamicsmodelstopredictthespatiotemporaldistributionof186Re-nanoliposomeswithinthebrain,utilizingpatient-specificpre-operative magneticresonanceimaging(MRI)toassignmaterialpropertiesforanadvection-diffusiontransport model.Themodelfamilyiscalibratedtosinglephotonemissioncomputedtomography(SPECT) imagesacquiredduringandaftertheinfusionof186Re-nanoliposomesforfivepatientsenrolledina PhaseI/IItrial(NCTNumberNCT01906385),andisvalidatedusingaleave-one-outbootstrapping methodologyforpredictingthefinaldistributionoftheparticles.Aftercalibration,ourmodelsare capableofpredictingthemid-deliveryandfinalspatialdistributionof186Re-nanoliposomeswithaDice valueof0.69 ± 0.18andaconcordancecorrelationcoefficientof0.88 ± 0.12(mean ± 95%confidence interval),usingonlythepatient-specific,pre-operativeMRIdata,andcalibratedmodelparametersfrom priorpatients.Theseresultsdemonstrateaproof-of-conceptforapatient-specificmodelingframework, whichpredictsthespatialdistributionofnanoparticles.Furtherdevelopmentofthisapproachcould enableoptimizingcatheterplacementforfuturestudiesemployingconvection-enhanceddelivery.

KW - Computational fluid dynamics

KW - Computational oncology

KW - Convection-enhanced delivery

KW - Glioblastoma multiforme

KW - Radiation therapy

KW - Glioblastoma/diagnostic imaging

KW - Brain Neoplasms/diagnostic imaging

KW - Humans

KW - Neoplasm Recurrence, Local

KW - Radioisotopes

KW - Rhenium

KW - Convection

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U2 - 10.1088/2057-1976/ac02a6

DO - 10.1088/2057-1976/ac02a6

M3 - Article

C2 - 34050041

AN - SCOPUS:85107247901

SN - 2057-1976

VL - 7

JO - Biomedical Physics & Engineering Express

JF - Biomedical Physics & Engineering Express

IS - 4

M1 - 045012

ER -

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

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