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Transl Oncol. 2014 Feb 1;7(1):14-22. eCollection 2014 Feb.

Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.

Author information

1
Institute of Imaging Science, Vanderbilt University, Nashville, TN.
2
Department of Biostatistics, Vanderbilt University, Nashville, TN.
3
Institute of Imaging Science, Vanderbilt University, Nashville, TN ; Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN ; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN.
4
Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN ; Department of Radiation Oncology, Vanderbilt University, Nashville, TN.
5
Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN ; Department of Medical Oncology, Vanderbilt University, Nashville, TN.
6
Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN ; Department of Pathology, Vanderbilt University, Nashville, TN.
7
Institute of Imaging Science, Vanderbilt University, Nashville, TN ; Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN ; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN ; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN ; Department of Physics, Vanderbilt University, Nashville, TN ; Department of Cancer Biology, Vanderbilt University, Nashville, TN.

Abstract

The purpose of this study is to investigate the ability of multivariate analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parametric maps, obtained early in the course of therapy, to predict which patients will achieve pathologic complete response (pCR) at the time of surgery. Thirty-three patients underwent DCE-MRI (to estimate K (trans), v e, k ep, and v p) and DW-MRI [to estimate the apparent diffusion coefficient (ADC)] at baseline (t 1) and after the first cycle of neoadjuvant chemotherapy (t 2). Four analyses were performed and evaluated using receiver-operating characteristic (ROC) analysis to test their ability to predict pCR. First, a region of interest (ROI) level analysis input the mean K (trans), v e, k ep, v p, and ADC into the logistic model. Second, a voxel-based analysis was performed in which a longitudinal registration algorithm aligned serial parameters to a common space for each patient. The voxels with an increase in k ep, K (trans), and v p or a decrease in ADC or v e were then detected and input into the regression model. In the third analysis, both the ROI and voxel level data were included in the regression model. In the fourth analysis, the ROI and voxel level data were combined with selected clinical data in the regression model. The overfitting-corrected area under the ROC curve (AUC) with 95% confidence intervals (CIs) was then calculated to evaluate the performance of the four analyses. The combination of k ep, ADC ROI, and voxel level data achieved the best AUC (95% CI) of 0.87 (0.77-0.98).

PMID:
24772203
PMCID:
PMC3998687

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