Multispectral quantification of tissue types in a RIF-1 tumor model with histological validation. Part I

Accurate assessments of therapeutic efficacy are confounded by intra- and intertumor heterogeneity. To address this issue we employed multispectral (MS) analysis using the apparent diffusion coefficient (ADC), T(2), proton density (M(0)), and k-means (KM) clustering algorithm to identify multiple compartments within both viable and necrotic tissue in a radiation-induced fibrosarcoma (RIF-1) tumor model receiving single-dose (1000 cGy) radiotherapy. Optimization of the KM method was achieved through histological validation by hematoxylin-eosin (H& and E) staining and hypoxia-inducible factor-1alpha (HIF-1alpha) immunohistochemistry. The optimum KM method was determined to be a two-feature (ADC, T(2)) and four-cluster (two clusters each of viable tissue and necrosis) segmentation. KM volume estimates for both viable (r = 0.94, P < 0.01) and necrotic (r = 0.69, P = 0.07) tissue were highly correlated with their H&E counterparts. HIF-1alpha immunohistochemistry showed that the intensity of HIF-1alpha expression tended to be concentrated in perinecrotic regions, supporting the subdivision of the viable tissue into well-oxygenated and hypoxic regions. Since both necrosis and hypoxia have been implicated in poor treatment response and reduced patient survival, the ability to quantify the degree of necrosis and the severity of hypoxia with this method may aid in the planning and modification of treatment regimens.