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Proceedings Paper

Radiomics for ultrafast dynamic contrast-enhanced breast MRI in the diagnosis of breast cancer: a pilot study
Author(s): Karen Drukker; Rachel Anderson; Alexandra Edwards; John Papaioannou; Fred Pineda; Hiroyuke Abe; Gregory Karzcmar; Maryellen L. Giger
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Paper Abstract

Radiomics for dynamic contrast-enhanced (DCE) breast MRI have shown promise in the diagnosis of breast cancer as applied to conventional DCE-MRI protocols. Here, we investigate the potential of using such radiomic features in the diagnosis of breast cancer applied on ultrafast breast MRI in which images are acquired every few seconds. The dataset consisted of 64 lesions (33 malignant and 31 benign) imaged with both ‘conventional’ and ultrafast DCE-MRI. After automated lesion segmentation in each image sequence, we calculated 38 radiomic features categorized as describing size, shape, margin, enhancement-texture, kinetics, and enhancement variance kinetics. For each feature, we calculated the 95% confidence interval of the area under the ROC curve (AUC) to determine whether the performance of each feature in the task of distinguishing between malignant and benign lesions was better than random guessing. Subsequently, we assessed performance of radiomic signatures in 10-fold cross-validation repeated 10 times using a support vector machine with as input all the features as well as features by category. We found that many of the features remained useful (AUC>0.5) for the ultrafast protocol, with the exception of some features, e.g., those designed for latephase kinetics such as the washout rate. For ultrafast MRI, the radiomics enhancement-texture signature achieved the best performance, which was comparable to that of the kinetics signature for ‘conventional’ DCE-MRI, both achieving AUC values of 0.71. Radiomic developed for ‘conventional’ DCE-MRI shows promise for translation to the ultrafast protocol, where enhancement texture appears to play a dominant role.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753U (27 February 2018); doi: 10.1117/12.2293644
Show Author Affiliations
Karen Drukker, The Univ. of Chicago (United States)
Rachel Anderson, The Univ. of Chicago (United States)
Alexandra Edwards, The Univ. of Chicago (United States)
John Papaioannou, The Univ. of Chicago (United States)
Fred Pineda, The Univ. of Chicago (United States)
Hiroyuke Abe, The Univ. of Chicago (United States)
Gregory Karzcmar, The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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