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- W4360990717 abstract "Treatment planning based on explicit dosimetry has the potential to improve a patient's therapeutic outcome. PDT treatment planning for solid tumors needs to result in the lowest number of diffusers required, their positions and assigned optical powers to restrict the clinical effective light power density to the target volume considering the spatial and temporal co-localization of the photons, photosensitizers, and oxygen concentration. The current treatment planning tools utilize only the population's average photosensitizer concentrations in the target and surrounding tissues. However, the photosensitizer and oxygen concentrations (pO2) show variability within and between subjects. The objective of this study is to develop innovative methods for predicting spatially-resolved photosensitizer distribution and integrating this information into clinical personalized iPDT treatment planning Using intravital microscopy in combination with the dorsal skinfold and cranial window chamber models, the blood flow (BF), blood volume (BF), and mean transit time (MTT) is correlated with the temporal resolved spatial PS distribution to derive the extravasation diffusion constant k of two PSs for two oncological indications. Breast(EMT6), colon(CT26), and glioma (RG2) tumor models are utilized and combined with at least two different PSs (Fotolon and porphyrin lipid nanoparticles, ePS) to demonstrate that this technique has a high potential for general applicability. Once a PS's extravasation diffusion constants k for the host tissue and tumor compromised vasculature have been determined, clinical translatability is based on MRI established blood volume and flow quantification to predict the spatial distribution of PS. 3D photosensitizer distribution is quantified by spatial frequency domain imaging (SFDI) and compared to predict the spatial distribution of PS upon co-registration of the fluorescence microscopy and MRI datasets." @default.
- W4360990717 created "2023-03-30" @default.
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- W4360990717 date "2023-03-01" @default.
- W4360990717 modified "2023-10-17" @default.
- W4360990717 title "Predicting spatially-resolved photosensitizer accumulation for personalized iPDT treatment planning" @default.
- W4360990717 doi "https://doi.org/10.1016/j.pdpdt.2023.103395" @default.
- W4360990717 hasPublicationYear "2023" @default.
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