Radiomics and deep learning of diffusion-weighted MRI in the diagnosis of breast cancer

Qiyuan Hu; Heather M. Whitney; Alexandra Edwards; John Papaioannou; Maryellen L. Giger

doi:10.1117/12.2512626

13 March 2019 Radiomics and deep learning of diffusion-weighted MRI in the diagnosis of breast cancer

Qiyuan Hu, Heather M. Whitney, Alexandra Edwards, John Papaioannou, Maryellen L. Giger

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109504A (2019) https://doi.org/10.1117/12.2512626
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

The incorporation of diffusion-weighted imaging (DWI) in breast magnetic resonance imaging (MRI) has shown potential in improving the accuracy of breast cancer diagnosis. Since DWI measures possibly complementary biological properties to dynamic contrast-enhanced (DCE) MRI parameters, DWI computer-aided diagnosis (CADx) can potentially improve the performance of current CADx systems in distinguishing between benign and malignant breast lesions. This study was performed on a database of 397 diffusion-weighted breast MR images (69 benign and 328 malignant). Lesions were automatically segmented using a fuzzy C-means method. The apparent diffusion coefficient (ADC)-based radiomic features were extracted and used to train a classifier. Another classifier was trained on convolutional neural network (CNN)-based features extracted by a pre-trained VGG19 network. The outputs from these two classifiers were fused by averaging the posterior probability of malignancy for each case to construct a fusion classifier. The performance evaluation for the three proposed classifiers was performed with five-fold cross-validation. The area under the receiver operating characteristic curve (AUC) was 0.68 (se = 0.04) for the ADC-based classifier, 0.74 (se = 0.03) for the CNN-based classifier, and 0.76 (se = 0.03) for the fusion classifier. The fusion classifier performed significantly better than the ADC-based classifier (݌ = 0.013). The CNN-based classifier failed to show statistically significant performance difference from the ADC-based classifier or the fusion classifier. The findings demonstrate promising performance of the proposed classifiers and the potential for DWI CADx as well as for the development of multiparametric CADx that incorporates information from both DWI and DCE-MRI in breast lesion classification.

Citation Download Citation

Qiyuan Hu, Heather M. Whitney, Alexandra Edwards, John Papaioannou, and Maryellen L. Giger "Radiomics and deep learning of diffusion-weighted MRI in the diagnosis of breast cancer", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109504A (13 March 2019); https://doi.org/10.1117/12.2512626

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