Machine-learning based classification of glioblastoma using dynamic susceptibility enhanced MR image

Jiwoong Jason Jeong; Bing Ji; Yang Lei; Liya Wang; Tian Liu; Arif Ali; Walter J. Curran; Hui Mao; Xiaofeng Yang

doi:10.1117/12.2512557

15 March 2019 Machine-learning based classification of glioblastoma using dynamic susceptibility enhanced MR image

Jiwoong Jason Jeong, Bing Ji, Yang Lei, Liya Wang, Tian Liu, Arif Ali, Walter J. Curran, Hui Mao, Xiaofeng Yang

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1095329 (2019) https://doi.org/10.1117/12.2512557
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

A classification method that integrates delta-radiomic features, DSC MRI, and random forest approach on the glioblastoma classification task is proposed. 25 patients, 13 high and 12 low-grade gliomas, who underwent the standard brain tumor MRI protocol, including DSC MRI, were included. Tumor regions on all DSC MRI images were registered to and contoured in T2-weighted fluid-attenuated-inversion-recovery (FLAIR) images. These contours and its contralateral regions of the normal tissue were used to extract delta-radiomic features before applying feature selection. The most informative and non-redundant features were selected to train a random forest to differentiate high-grade (HG) and low-grade (LG) gliomas. These were then fed into a leaveone- out cross-validation random forest to classify these tumors for grading. Finally, a majority-voting method was applied to reduce binarization bias and to combine the results of various feature lists. Analysis of the predictions showed that the reported method consistently predicted the tumor grade of 24 out of 25 patients correctly (0.96). Finally, the mean prediction accuracy was 0.95±0.10 for HG and 0.85±0.25 for LG. The area under the receiver operating characteristic curve (AUC) was 0.94. This study shows that delta-radiomic features derived from DSC MRI data can be used to characterize and determine the tumor grades. The radiomic features from DSC MRI may be used to elucidate the underlying tumor biology and response to therapy.

Citation Download Citation

Jiwoong Jason Jeong, Bing Ji, Yang Lei, Liya Wang, Tian Liu, Arif Ali, Walter J. Curran, Hui Mao, and Xiaofeng Yang "Machine-learning based classification of glioblastoma using dynamic susceptibility enhanced MR image ", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1095329 (15 March 2019); https://doi.org/10.1117/12.2512557

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
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Magnetic resonance imaging

Tumors

Tissues

Feature selection

Image classification

Image enhancement

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years