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DCE-MRI based analysis of intratumor heterogeneity by decomposing method for prediction of HER2 status in breast cancer
Author(s): Peng Zhang; Ming Fan; Yuanzhe Li; Maosheng Xu; Lihua Li
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Paper Abstract

Human epidermal growth factor receptor-2 (HER2) plays an important role in treatment strategy and prognosis determination in breast cancers. However, breast cancers are characterized by considerable heterogeneity both between and within tumors, which is a key impediment to accurately determine HER2 status for radiomic analysis. To this end, tumor heterogeneity was evaluated by unsupervised decomposition method on breast magnetic resonance imaging (MRI), in which three tumor subregions were generated terms as Input, Fast and Slow. This tumor decomposition was performed by a convex analysis of mixtures (CAM) method, which was designed according to analysis of contrast-enhancement patterns. The study retrospectively investigated 181 patients who underwent dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) examination. Among them, 124 were HER2-negative and 57 were HER2-positive status. Imaging features of texture and histogram were computed in each subregion. Multivariate logistic regression classifiers were trained and validated with leave-one-out cross-validation (LOOCV) method. An area under a receiver operating characteristic curve (AUC) was calculated to assess performance of the classifier. The classifier based on features from Fast subregion obtained an AUC of 0.802 ± 0.067 and was significantly (P = 0.0113) outperformed the classifier based on features from the whole tumors. When the predicted values from the respective classifiers were fused by weighted average, the AUC significantly increased to 0.820 ± 0.063 (P = 0.0011). The results indicate that analysis of intratumor heterogeneity through decomposing method of DCE-MRI has the potential to serve as a marker for predicting HER2 status.

Paper Details

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500H (13 March 2019); doi: 10.1117/12.2513102
Show Author Affiliations
Peng Zhang, Hangzhou Dianzi Univ. (China)
Ming Fan, Hangzhou Dianzi Univ. (China)
Yuanzhe Li, Hangzhou Dianzi Univ. (China)
Maosheng Xu, Zhejiang Provincial Hospital of Traditional Chinese Medicine (China)
Lihua Li, Hangzhou Dianzi Univ. (China)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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