Quantification of tumor tissue populations by multispectral analysis

Abstract 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 0 ) 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 0 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 (117 ± 51 mm 3 ). 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. Magn Reson Med 51:542–551, 2004. © 2004 Wiley‐Liss, Inc.