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Prediction of histological grade in breast cancer by combining DCE-MRI and DWI features
Author(s): Wenrui Zhao; Ming Fan; Maosheng Xu; Lihua Li
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Paper Abstract

Breast cancer is one of the most common malignant tumors in women. The purpose of this study was to predict the histological grade of breast cancer using features extracted from dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI). In this study, we collected 144 cases of breast invasive ductal carcinoma, which consists of 76 who were high-grade malignant (Grade 3) and 68 mediate-grade malignant (Grade 2) breast cancers. Preoperative breast DW and DCE-MR examination were performed using a 3T MR scanner. Breast tumor segmentation was performed on all of the image series. After that, image features of texture, statistic, and morphological features of breast tumor were extracted on both the DW and DCE-MR images. The classification model was established on these images respectively, and the classifiers of single-parametric image were fused for prediction. In order to evaluate the classifier performance, the area under the receiver operating characteristic curve (AUC) was calculated in a leave-oneout cross-validation (LOOCV) analysis. The predictive model based on DCE-MRI generated an AUC of 0.829 with the sensitivity and specificity of 0.868 and 0.676 respectively, while that based on DWI generated an AUC of 0.783 with the sensitivity and specificity of 0.842 and 0.676 respectively. After multi-classifier fusion using features both from the DWI and DCE-MRI, the classification performance was increased to AUC of 0.844±0.067 with the sensitivity and specificity of 0.908 and 0.735 respectively. Our results showed that, compared with each single parametric image alone, the performance of the classifier could be improved by combining features of DCE-MRI and DWI.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109541H (15 March 2019); doi: 10.1117/12.2513126
Show Author Affiliations
Wenrui Zhao, Hangzhou Dianzi Univ. (China)
Ming Fan, 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. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

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