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Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer
被引:33
作者:
Li, Xia
[1
]
Kang, Hakmook
[2
]
Arlinghaus, Lori R.
[1
]
Abramson, Richard G.
[1
,3
,4
]
Chakravarthy, A. Bapsi
[3
,5
]
Abramson, Vandana G.
[3
,6
]
Farley, Jaime
[3
,6
]
Sanders, Melinda
[3
,7
]
Yankeelov, Thomas E.
[1
,3
,4
,8
,9
,10
]
机构:
[1] Vanderbilt Univ, Inst Imaging Sci, Nashville, TN 37232 USA
[2] Vanderbilt Univ, Dept Biostat, Nashville, TN 37232 USA
[3] Vanderbilt Univ, Vanderbilt Ingram Canc Ctr, Nashville, TN 37232 USA
[4] Vanderbilt Univ, Dept Radiol & Radiol Sci, Nashville, TN 37232 USA
[5] Vanderbilt Univ, Dept Radiat Oncol, Nashville, TN 37232 USA
[6] Vanderbilt Univ, Dept Med Oncol, Nashville, TN 37232 USA
[7] Vanderbilt Univ, Dept Pathol, Nashville, TN 37232 USA
[8] Vanderbilt Univ, Dept Biomed Engn, Nashville, TN 37232 USA
[9] Vanderbilt Univ, Dept Phys, Nashville, TN 37232 USA
[10] Vanderbilt Univ, Dept Canc Biol, Nashville, TN 37232 USA
关键词:
CONTRAST-ENHANCED MRI;
PROGNOSTIC VALUE;
DIFFUSION;
SURVIVAL;
REGISTRATION;
ALGORITHM;
SIZE;
IDENTIFICATION;
BIOMARKERS;
REGRESSION;
D O I:
10.1593/tlo.13748
中图分类号:
R73 [肿瘤学];
学科分类号:
100214 ;
摘要:
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).
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页码:14 / 22
页数:9
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