Three-dimensional oxygen maps of tumors in real time - Analysis in the context of active tumor vasculature.

BACKGROUND AND OBJECTIVE: Characterizing the tumor microenvironment (TME) requires integrating multiple physiological features, including oxygenation, vascularity, and redox status. While EPR oxygen imaging (EPROI) provides spatial pO₂ maps, conventional metrics such as median pO₂ or hypoxic fraction (HF) may not fully capture vascular characteristics. This study aimed to identify EPROI-derived parameters that best reflect tumor vasculature by correlating oxygenation metrics with Doppler ultrasound vascular imaging and redox kinetics. METHODS: Mel270 uveal melanoma tumors were implanted in the intrascapular fat pad of SCID mice, providing a highly vascularized niche. Non-invasive imaging included ultrasound (anatomical and Doppler) and EPROI for pO₂ mapping. EPR spectroscopy of the nitroxide redox probe was used to estimate tissue redox status. Parameters extracted included median pO₂, HF20, VC40, VC60, vascular fraction (PV), and kinetic descriptors (α, β, mean amplitude). Correlation analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression, with Leave-One-Out Cross-Validation (LOOCV) and Partial Least Squares Regression (PLSR), as well as principal component analysis (PCA) were performed to identify the most informative metrics. RESULTS: VC40 and VC60, representing the upper tail of the pO₂ histogram, showed moderate correlations with vascularity (PV), median pO₂, and redox kinetics (β), outperforming HF20. Both LASSO and PCA confirmed VC40/VC60 as the most robust single parameters for vascular characterization, as well as the best predictors of median pO2, with a secondary contribution from mean amplitude and PV. As expected, median pO₂ correlated negatively with metastasis, while HF20 correlated negatively with PV in PCA analysis. Tumor size did not correlate with oxygenation or vascularity. CONCLUSIONS: Advanced evaluation of the tumor microenvironment requires complementary parameters like VC40/VC60 and HF20/pO₂ to capture its full complexity, integrating oxygenation, vascularity, and redox data. Future work should adopt advanced histogram-based and machine learning methods, as widely used in MRI, to fully exploit spatial oxygen and vascular network imaging data.