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

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

16 Scopus citations

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 (US)
Article number	045012
Journal	Biomedical Physics and Engineering Express
Volume	7
Issue number	4
DOIs	https://doi.org/10.1088/2057-1976/ac02a6
State	Published - Jul 2021

Keywords

Computational fluid dynamics
Computational oncology
Convection-enhanced delivery
Glioblastoma multiforme
Radiation therapy

ASJC Scopus subject areas

General Nursing

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10.1088/2057-1976/ac02a6

Cite this

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 and Engineering Express, 7(4), Article 045012. https://doi.org/10.1088/2057-1976/ac02a6

Woodall, RT, Hormuth, DA, Wu, C, Abdelmalik, MRA, Phillips, WT, Bao, A, Hughes, TJR, Brenner, AJ & Yankeelov, TE 2021, 'Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme', Biomedical Physics and Engineering Express, vol. 7, no. 4, 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",

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.}",

note = "Publisher Copyright: {\textcopyright} 2021 IOP Publishing Ltd.",

year = "2021",

month = jul,

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

language = "English (US)",

volume = "7",

journal = "Biomedical Physics and Engineering Express",

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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

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JO - Biomedical Physics and Engineering Express

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

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