20.500.12556/RUL-164641 Towards reliable hyperspectral imaging biomarkers of CT26 murine tumor model The non-invasive monitoring of tumor growth can offer invaluable diagnostic insights and enhance our understanding of tumors and their microenvironment. Integrating hyperspectral imaging (HSI) with three-dimensional optical profilometry (3D OP) makes contactless and non-invasive tumor diagnosis possible by utilizing the inherent tissue contrast provided by visible (VIS) and near-infrared (NIR) light. Consequently, valuable information regarding tumors and healthy tissues can be extracted from the acquired hyperspectral images. Until now, very few methods have been used to monitor tumor models in vivo daily and non-invasively. In this research, we conducted a 14-day study monitoring BALB/c mice with subcutaneously grown CT26 murine colon carcinomas in vivo, commencing on the day of tumor cell injection. We extracted physiological properties such as total hemoglobin (THB) and tissue oxygenation (StO$_2$) using the inverse adding-doubling (IAD) algorithm and manually segmented the tissues. We then selected the ten most relevant features describing tumors using the Max-Relevance Min-Redundancy (MRMR) algorithm and utilized 30 classic and advanced machine learning (ML) algorithms to discriminate tumors from healthy tissues. Finally, we tested the robustness of feature selection and model performance by smoothing tissue parameter maps extracted by IAD with a variable kernel and omitting selected training data. We could discriminate CT26 tumor models from surrounding healthy tissues with an area under the curve (AUC) of up to 1 for models based on the gradient boosting method, linear discriminant analysis, and random forests. Our findings help pave the way for precise and robust imaging biomarkers that could aid tumor diagnosis and advance clinical practice. biomarkers hyperspectral imaging machine learning tumors murine models biomarkerji hiperspektralno slikanje strojno učenje tumorji true false true Angleški jezik Slovenski jezik Članek v reviji 2024-11-06 10:40:31 2024-11-06 10:40:32 2025-06-06 04:42:42 0000-00-00 00:00:00 2024 0 0 17 str. iss. 21, art. e39816 Vol. 10 Nov. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 616-073 2405-8440 10.1016/j.heliyon.2024.e39816 213858051 1-s2.0-S2405844024158471-main.pdf 1-s2.0-S2405844024158471-main.pdf 1 983DB95DAFF1E54F7BBBCADA61CBCE22 749814cb488aa572acf5682414c7fff1cd1726f2c666e4b12ab300e9380fc5c7 69ebe01c-41e8-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=211087 https://www.sciencedirect.com/science/article/pii/S2405844024158471 0 521cdade-41e8-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=211085 Fakulteta za matematiko in fiziko Medicinska fakulteta Zdravstvena fakulteta 0 0 0