Premium
Integration of diffusion‐weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy
Author(s) -
Atuegwu Nkiruka C.,
Arlinghaus Lori R.,
Li Xia,
Welch E. Brian,
Chakravarthy Bapsi A.,
Gore John C.,
Yankeelov Thomas E.
Publication year - 2011
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.23203
Subject(s) - effective diffusion coefficient , magnetic resonance imaging , voxel , pearson product moment correlation coefficient , diffusion mri , nuclear medicine , diffusion , medicine , correlation coefficient , breast cancer , chemotherapy , nuclear magnetic resonance , radiology , mathematics , statistics , cancer , physics , thermodynamics
Diffusion‐weighted magnetic resonance imaging data obtained early in the course of therapy can be used to estimate tumor proliferation rates, and the estimated rates can be used to predict tumor cellularity at the conclusion of therapy. Six patients underwent diffusion‐weighted magnetic resonance imaging immediately before, after one cycle, and after all cycles of neoadjuvant chemotherapy. Apparent diffusion coefficient values were calculated for each voxel and for a whole tumor region of interest. Proliferation rates were estimated using the apparent diffusion coefficient data from the first two time points and then used with the logistic model of tumor growth to predict cellularity after therapy. The predicted number of tumor cells was then correlated to the corresponding experimental data. Pearson's correlation coefficient for the region of interest analysis yielded 0.95 ( P = 0.004), and, after applying a 3 × 3 mean filter to the apparent diffusion coefficient data, the voxel‐by‐voxel analysis yielded a Pearson correlation coefficient of 0.70 ± 0.10 ( P < 0.05). Magn Reson Med, 2011. © 2011 Wiley Periodicals, Inc.