Prediction of histological grade in breast cancer by combining DCE-MRI and DWI features

Wenrui Zhao; Ming Fan; Maosheng Xu; Lihua Li

doi:10.1117/12.2513126

15 March 2019 Prediction of histological grade in breast cancer by combining DCE-MRI and DWI features

Wenrui Zhao, Ming Fan, Maosheng Xu, Lihua Li

Proceedings Volume 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications; 109541H (2019) https://doi.org/10.1117/12.2513126
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Wenrui Zhao, Ming Fan, Maosheng Xu, and Lihua Li "Prediction of histological grade in breast cancer by combining DCE-MRI and DWI features", Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109541H (15 March 2019); https://doi.org/10.1117/12.2513126

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Diffusion weighted imaging

Breast cancer

Magnetic resonance imaging

Breast

Image segmentation

Tumors

Image enhancement

Show All Keywords

Keywords/Phrases

Search In:

Publication Years