Cancer represents one of the major public health challenges of modern society, making the development of novel diagnostic methods essential for the early detection and monitoring of the disease. Among the promising non-invasive approaches is hyperspectral imaging (HSI), which enables the acquisition of both spatial and spectral information from biological tissues, providing insights into their physiological and morphological properties.
This master's thesis investigates the potential of HSI and tissue indices for monitoring the development of different murine tumors. The analysis was conducted on five murine tumor models: B16-F10, CT2, MC38, MOC1 and MOC2. Data acquisition was performed using a custom developed multimodal imaging system that combines HSI and optical profilometry.
Thirteen tissue indices related to hemoglobin content, melanin content, tissue water content, perfusion, and oxygenation were analyzed. In addition to the tissue indices, first-order radiomic features, including mean, minimum, maximum, variance, skewness, kurtosis, entropy and energy, were extracted. The analysis was performed on tumor, peritumoral, and healthy tissue, with aim of identifying combinations of tissue indices and radiomic features that enable effective differentiation between tissue types and facilitate the monitoring of tumor progression over time.
The results demonstrated that several tissue indices, when combined with radiomic features, have significant potential for distinguishing between different tissues. In particular, indices associated with erythema, melanin content, and tissue oxygenation showed statistically significant differences among the analyzed tissue regions across multiple tumor models. These findings confirm the applicability of HSI as a non-invasive method for monitoring tumor development and provide a foundation for the further development of advanced diagnostic approaches in experimental and clinical oncology.
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