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Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors
Author(s) -
Wu Chengyue,
Pineda Federico,
Hormuth David A.,
Karczmar Gregory S.,
Yankeelov Thomas E.
Publication year - 2019
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.27529
Subject(s) - wilcoxon signed rank test , medicine , voxel , logistic regression , breast cancer , nuclear medicine , dynamic contrast enhanced mri , radiology , magnetic resonance imaging , cancer , mann–whitney u test
Purpose We propose a novel methodology to integrate morphological and functional information of tumor‐associated vessels to assist in the diagnosis of suspicious breast lesions. Theory and Methods Ultrafast, fast, and high spatial resolution DCE‐MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE‐MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, K trans (volume transfer coefficient), and v p (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability. Results A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, K trans , and v p showed significant difference between malignant and benign lesions ( P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91). Conclusion This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.