Features of the tumor microenvironment (TME), such as hemoglobin saturation (HbSat), can provide valuable information on early development and progression of tumors. HbSat correlates with high metabolism and precedes the formation of angiogenic tumors; therefore, changes in HbSat profile can be used as a biomarker for early cancer detection. In this project, we develop a methodology to evaluate HbSat for forecasting early tumor development in a mouse model. We built a delta () cumulative feature that includes spatial and temporal distribution of HbSat for classifying tumor/normal areas. Using a two-class (normal and tumor) logistic regression, the feature successfully forecasts tumor areas in two window chamber mice ( and 0.85). To assess the performance of the logistic regression-based classifier utilizing the feature of each region, we conduct a 10-fold cross-validation analysis (AUC of the ). These results show that the TME features based on HbSat can be used to evaluate tumor progression and forecast new occurrences of tumor areas.