Photosensitizer spatial heterogeneity and its impact on personalized interstitial photodynamic therapy treatment planning

Personalized photodynamic therapy (PDT) treatment planning requires knowledge of the spatial and temporal co-localization of photons, photosensitizers (PSs), and oxygen. The inter- and intra-subject variability in the photosensitizer concentration can lead to suboptimal outcomes using standard treat...

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Published inJournal of biomedical optics Vol. 30; no. 1; p. 018001
Main Authors Saeidi, Tina, Wang, Shuran, Contreras, Hector A., Daly, Michael J., Betz, Vaughn, Lilge, Lothar
Format Journal Article
LanguageEnglish
Published United States Society of Photo-Optical Instrumentation Engineers 01.01.2025
SPIE
Subjects
Animals
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - drug therapy
Cancer
Cell Line, Tumor
Glioblastoma - diagnostic imaging
Glioblastoma - drug therapy
Glioma - diagnostic imaging
Glioma - drug therapy
Humans
Photochemotherapy
Photochemotherapy - methods
Photosensitizing Agents - pharmacokinetics
Photosensitizing Agents - therapeutic use
Precision Medicine - methods
Rats
Therapeutic
photosensitizer heterogeneity
PDT-SPACE
interstitial photodynamic therapy
pre-treatment planning
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Abstract Personalized photodynamic therapy (PDT) treatment planning requires knowledge of the spatial and temporal co-localization of photons, photosensitizers (PSs), and oxygen. The inter- and intra-subject variability in the photosensitizer concentration can lead to suboptimal outcomes using standard treatment plans. We aim to quantify the PS spatial variation in tumors and its effect on PDT treatment planning solutions. The spatial variability of two PSs is imaged at various spatial resolutions for an orthotopic rat glioma model and applied to human glioblastoma models to determine the spatial PDT dose, including in organs at risk. An open-source interstitial photodynamic therapy (iPDT) planning tool is applied to these models, deriving the spatial photosensitizer quantification resolution that consistently impacts iPDT source placement and power allocation. The studies revealed a bimodal photosensitizer distribution in the tumor. The concentration of the PS can vary by a factor of 2 between the tumor core and rim, with slight variation within the core but a factor of 5 in the rim. An average sampling volume of for photosensitizer quantification will result in significantly different iPDT planning solutions for each case. Assuming homogeneous photosensitizer distribution results in suboptimal therapeutic outcomes, we highlight the need to predict the photosensitizer distribution before source placement for effective treatment plans.
AbstractList Personalized photodynamic therapy (PDT) treatment planning requires knowledge of the spatial and temporal co-localization of photons, photosensitizers (PSs), and oxygen. The inter- and intra-subject variability in the photosensitizer concentration can lead to suboptimal outcomes using standard treatment plans.SignificancePersonalized photodynamic therapy (PDT) treatment planning requires knowledge of the spatial and temporal co-localization of photons, photosensitizers (PSs), and oxygen. The inter- and intra-subject variability in the photosensitizer concentration can lead to suboptimal outcomes using standard treatment plans.We aim to quantify the PS spatial variation in tumors and its effect on PDT treatment planning solutions.AimWe aim to quantify the PS spatial variation in tumors and its effect on PDT treatment planning solutions.The spatial variability of two PSs is imaged at various spatial resolutions for an orthotopic rat glioma model and applied in silico to human glioblastoma models to determine the spatial PDT dose, including in organs at risk. An open-source interstitial photodynamic therapy (iPDT) planning tool is applied to these models, deriving the spatial photosensitizer quantification resolution that consistently impacts iPDT source placement and power allocation.ApproachThe spatial variability of two PSs is imaged at various spatial resolutions for an orthotopic rat glioma model and applied in silico to human glioblastoma models to determine the spatial PDT dose, including in organs at risk. An open-source interstitial photodynamic therapy (iPDT) planning tool is applied to these models, deriving the spatial photosensitizer quantification resolution that consistently impacts iPDT source placement and power allocation.The ex vivo studies revealed a bimodal photosensitizer distribution in the tumor. The concentration of the PS can vary by a factor of 2 between the tumor core and rim, with slight variation within the core but a factor of 5 in the rim. An average sampling volume of 1    mm 3 for photosensitizer quantification will result in significantly different iPDT planning solutions for each case.ResultsThe ex vivo studies revealed a bimodal photosensitizer distribution in the tumor. The concentration of the PS can vary by a factor of 2 between the tumor core and rim, with slight variation within the core but a factor of 5 in the rim. An average sampling volume of 1    mm 3 for photosensitizer quantification will result in significantly different iPDT planning solutions for each case.Assuming homogeneous photosensitizer distribution results in suboptimal therapeutic outcomes, we highlight the need to predict the photosensitizer distribution before source placement for effective treatment plans.ConclusionsAssuming homogeneous photosensitizer distribution results in suboptimal therapeutic outcomes, we highlight the need to predict the photosensitizer distribution before source placement for effective treatment plans.
Personalized photodynamic therapy (PDT) treatment planning requires knowledge of the spatial and temporal co-localization of photons, photosensitizers (PSs), and oxygen. The inter- and intra-subject variability in the photosensitizer concentration can lead to suboptimal outcomes using standard treatment plans. We aim to quantify the PS spatial variation in tumors and its effect on PDT treatment planning solutions. The spatial variability of two PSs is imaged at various spatial resolutions for an orthotopic rat glioma model and applied to human glioblastoma models to determine the spatial PDT dose, including in organs at risk. An open-source interstitial photodynamic therapy (iPDT) planning tool is applied to these models, deriving the spatial photosensitizer quantification resolution that consistently impacts iPDT source placement and power allocation. The studies revealed a bimodal photosensitizer distribution in the tumor. The concentration of the PS can vary by a factor of 2 between the tumor core and rim, with slight variation within the core but a factor of 5 in the rim. An average sampling volume of for photosensitizer quantification will result in significantly different iPDT planning solutions for each case. Assuming homogeneous photosensitizer distribution results in suboptimal therapeutic outcomes, we highlight the need to predict the photosensitizer distribution before source placement for effective treatment plans.
Audience Academic
Author Lilge, Lothar
Wang, Shuran
Contreras, Hector A.
Betz, Vaughn
Saeidi, Tina
Daly, Michael J.
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Keywords photosensitizer heterogeneity
PDT-SPACE
interstitial photodynamic therapy
pre-treatment planning
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Snippet Personalized photodynamic therapy (PDT) treatment planning requires knowledge of the spatial and temporal co-localization of photons, photosensitizers (PSs),...
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SubjectTerms Animals
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - drug therapy
Cancer
Cell Line, Tumor
Glioblastoma - diagnostic imaging
Glioblastoma - drug therapy
Glioma - diagnostic imaging
Glioma - drug therapy
Humans
Photochemotherapy
Photochemotherapy - methods
Photosensitizing Agents - pharmacokinetics
Photosensitizing Agents - therapeutic use
Precision Medicine - methods
Rats
Therapeutic
Title Photosensitizer spatial heterogeneity and its impact on personalized interstitial photodynamic therapy treatment planning
URI http://www.dx.doi.org/10.1117/1.JBO.30.1.018001
https://www.ncbi.nlm.nih.gov/pubmed/39802351
https://www.proquest.com/docview/3154890262
https://pubmed.ncbi.nlm.nih.gov/PMC11724368
Volume 30
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