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Magn Reson Med Sci. 2019 Jan 10;18(1):44-52. doi: 10.2463/mrms.mp.2017-0178. Epub 2018 May 16.

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

Author information

1
Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.
2
Department of Radiology, The University of Tokyo Hospital.
3
Department of Radiology, International University of Health and Welfare, Mita Hospital.
4
Department of Radiology, Faculty of Medicine, The University of Tokyo.
5
Department of Radiology, Graduate School of Medicine, The University of Tokyo.

Abstract

PURPOSE:

Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T1-weighted images.

METHODS:

This retrospective study on preoperative brain tumor MRI included 76 consecutives, initially treated patients with glioblastoma (n = 55) or PCNSL (n = 21) from one institution, consisting of independent training group (n = 60: 44 glioblastomas and 16 PCNSLs) and test group (n = 16: 11 glioblastomas and 5 PCNSLs) sequentially separated by time periods. A total set of 67 texture features was computed on routine contrast-enhanced T1-weighted images of the training group, and the top four most discriminating features were selected as input variables to train support vector machine classifiers. These features were then evaluated on the test group with subsequent image classification.

RESULTS:

The area under the receiver operating characteristic curves on the training data was calculated at 0.99 (95% confidence interval [CI]: 0.96-1.00) for the classifier with a Gaussian kernel and 0.87 (95% CI: 0.77-0.95) for the classifier with a linear kernel. On the test data, both of the classifiers showed prediction accuracy of 75% (12/16) of the test images.

CONCLUSIONS:

Although further improvement is needed, our preliminary results suggest that machine learning-based image classification may provide complementary diagnostic information on routine brain MRI.

KEYWORDS:

classification; glioblastoma; magnetic resonance imaging; primary central nervous system lymphoma; support vector machine

PMID:
29769456
PMCID:
PMC6326763
DOI:
10.2463/mrms.mp.2017-0178
[Indexed for MEDLINE]
Free PMC Article

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