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Abstract

Tumors exhibit chaotic vascular networks, causing hypoxic and thus more radioresistant subregions, which limit the success of radiotherapy. In this work, an analysis pipeline for 3D light-sheet fluorescence microscope (LSFM) images was established to characterize entire tumoral vascular networks. Two tumor lines with different radiosensitivities were irradiated with protons in two fractionation schemes and compared to untreated control tumors and brain samples. The vascular network characterization employed three parameter classes: (i) Parameters that were measurable on 2D histology and 3D LSFM showed a good agreement between the two methods. (ii) Conventional 3D quantities, e.g., the number of interconnected vascular branches, were useful to separate different tissue types. (iii) Graph-theoretic network parameters, e.g., the graph size, could be used for tissue discrimination and revealed a radiation-induced decrease in the global network efficiency. Furthermore, an existing oxygen transport simulation algorithm was numerically optimized and benchmarked. The simulated oxygen distributions within the experimental tissue samples revealed increased oxygenation after irradiation, indicating reoxygenation. These results, which also account for the tissue-specific properties of the characterized vascular architectures, may be used to improve simulations for the radiation response of hypoxic tumors.