Quantification of tumor tissue populations by multispectral analysis

Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that combines MR diffusion imaging with multispectral (MS) analysis to quantify tumor tissue populations. K-means (KM) clustering of the apparent diffusion coefficient (ADC), T-2, and proton density (M-o) was employed to estimate the volumes of viable tumor tissue, necrosis, and neighboring subcutaneous adipose tissue in a human colorectal tumor xenograft mouse model. In a second set of experiments, the temporal evolution of the MS tissue classes in response to therapeutic intervention Apo2L/TRAIL and CPT-11 was observed. The multiple parameters played complementary roles in identifying the various tissues. The ADC was the dominant parameter for identifying regions of necrosis, whereas T-2 identified two necrotic subpopulations, and M-o contributed to the differentiation of viable tumor from subcutaneous adipose tissue. MS viable tumor estimates (mean volume = 275 +/- 147 mm(3)) were highly correlated (r = 0.81, P < 0.01) with histological estimates (1117 +/- 51 mm). In the treatment study, MS viable tumor volume (at day 10) was 77 +/- 67 mm(3) for the Apo2L/TRAIL+CPT-11 group, and was significantly reduced relative to the control group (292 +/- 127 mm(3), p < 0.01). This method shows promise as a means of detecting an early therapeutic response in vivo. (C) 2004 Wiley-Liss, Inc.