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

• Published in: Magnetic Resonance in Medical Sciences 2019, Vol.18(1), pp.44-52
• Main Authors: Kunimatsu, Akira, Kunimatsu, Natsuko, Yasaka, Koichiro, Akai, Hiroyuki, Kamiya, Kouhei, Watadani, Takeyuki, Mori, Harushi, Abe, Osamu
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Brain; Brain cancer; Brain tumors; Central nervous system; Classification; Classifiers; Computation; Confidence intervals; Diagnostic systems; Glioblastoma; Image classification; Image contrast; Image enhancement; Kernels; Learning algorithms; Lymphoma; Machine learning; Magnetic resonance imaging; Major Paper; Medical imaging; Nervous system; Neuroimaging; primary central nervous system lymphoma; support vector machine; Support vector machines; Texture; Training
• ISSN: 1347-3182; 1880-2206
• DOI: 10.2463/mrms.mp.2017-0178

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.