カミヤ　コウヘイ   Kamiya Kohei   神谷　昂平    所属   東邦大学  医学部 医学科（大森病院）    職種   講師
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.
掲載誌名	正式名：Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine ISSNコード：13473182
出版社	JPN SOC MAGNETIC RESONANCE MEDICINE
巻・号・頁	18(1),pp.44-52
著者・共著者	Akira Kunimatsu,Natsuko Kunimatsu,Koichiro Yasaka,Hiroyuki Akai,Kouhei Kamiya,Takeyuki Watadani,Harushi Mori,Osamu Abe
発行年月	2019/01
概要	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
DOI	10.2463/mrms.mp.2017-0178
PMID	29769456