Abstract

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