breast dce-mri kinetic heterogeneity tumor markers: preliminary associations with neoadjuvant chemotherapy response

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ID: 163925
2015
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Ranked #14 of 52 articles by views in advances in acoustics and vibration

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Abstract
The ability to predict response to neoadjuvant chemotherapy for women diagnosed with breast cancer, either before or early on in treatment, is critical to judicious patient selection and tailoring the treatment regimen. In this paper, we investigate the role of contrast agent kinetic heterogeneity features derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting treatment response. We propose a set of kinetic statistic descriptors and present preliminary results showing the discriminatory capacity of the proposed descriptors for predicting complete and non-complete responders as assessed from pre-treatment imaging exams. The study population consisted of 15 participants: 8 complete responders and 7 non-complete responders. Using the proposed kinetic features, we trained a leave-one-out logistic regression classifier that performs with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.84 under the ROC. We compare the predictive value of our features against commonly used MRI features including kinetics of the characteristic kinetic curve (CKC), maximum peak enhancement (MPE), hotspot signal enhancement ratio (SER), and longest tumor diameter that give lower AUCs of 0.71, 0.66, 0.64, and 0.54, respectively. Our proposed kinetic statistics thus outperform the conventional kinetic descriptors as well as the classifier using a combination of all the conventional descriptors (i.e., CKC, MPE, SER, and longest diameter), which gives an AUC of 0.74. These findings suggest that heterogeneity-based DCE-MRI kinetic statistics could serve as potential imaging biomarkers for tumor characterization and could be used to improve candidate patient selection even before the start of the neoadjuvant treatment.
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ashraf2015translationalbreast Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Ahmed Ashraf;Bilwaj Gaonkar;Carolyn Mies;Angela DeMichele;Mark Rosen;Christos Davatzikos;Despina Kontos
Journal advances in acoustics and vibration
Year 2015
DOI
10.1016/j.tranon.2015.03.005
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