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Proceedings Paper

Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data
Author(s): Joseph B. DeGrandchamp; Jennifer G. Whisenant; Lori R. Arlinghaus; V. G. Abramson; Thomas E. Yankeelov; Julio Cárdenas-Rodríguez
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Paper Abstract

The pharmacokinetic parameters derived from dynamic contrast enhanced (DCE) MRI have shown promise as biomarkers for tumor response to therapy. However, standard methods of analyzing DCE MRI data (Tofts model) require high temporal resolution, high signal-to-noise ratio (SNR), and the Arterial Input Function (AIF). Such models produce reliable biomarkers of response only when a therapy has a large effect on the parameters. We recently reported a method that solves the limitations, the Linear Reference Region Model (LRRM). Similar to other reference region models, the LRRM needs no AIF. Additionally, the LRRM is more accurate and precise than standard methods at low SNR and slow temporal resolution, suggesting LRRM-derived biomarkers could be better predictors. Here, the LRRM, Non-linear Reference Region Model (NRRM), Linear Tofts model (LTM), and Non-linear Tofts Model (NLTM) were used to estimate the RKtrans between muscle and tumor (or the Ktrans for Tofts) and the tumor kep,TOI for 39 breast cancer patients who received neoadjuvant chemotherapy (NAC). These parameters and the receptor statuses of each patient were used to construct cross-validated predictive models to classify patients as complete pathological responders (pCR) or non-complete pathological responders (non-pCR) to NAC. Model performance was evaluated using area under the ROC curve (AUC). The AUC for receptor status alone was 0.62, while the best performance using predictors from the LRRM, NRRM, LTM, and NLTM were AUCs of 0.79, 0.55, 0.60, and 0.59 respectively. This suggests that the LRRM can be used to predict response to NAC in breast cancer.

Paper Details

Date Published: 29 March 2016
PDF: 10 pages
Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978811 (29 March 2016); doi: 10.1117/12.2217008
Show Author Affiliations
Joseph B. DeGrandchamp, Univ. of Arizona (United States)
Jennifer G. Whisenant, Vanderbilt Univ. Institute of Imaging Science (United States)
Lori R. Arlinghaus, Vanderbilt Univ. Institute of Imaging Science (United States)
V. G. Abramson, Vanderbilt Univ. School of Medicine (United States)
Thomas E. Yankeelov, Univ. of Texas at Austin (United States)
Julio Cárdenas-Rodríguez, Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 9788:
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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