ノナカ　マサヒロ   NONAKA MASAHIRO   埜中　正博    所属   関西医科大学  脳神経外科学講座    職種   教授
論文種別	原著(症例報告除く)
言語種別	英語
査読の有無	査読あり
表題	Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas.
掲載誌名	正式名：Scientific reports 略　 称：Sci Rep ISSNコード：2045232220452322
掲載区分	国外
巻・号・頁	8(1),pp.11773
著者・共著者	Arita Hideyuki, Kinoshita Manabu, Kawaguchi Atsushi, Takahashi Masamichi, Narita Yoshitaka, Terakawa Yuzo, Tsuyuguchi Naohiro, Okita Yoshiko, Nonaka Masahiro, Moriuchi Shusuke, Takagaki Masatoshi, Fujimoto Yasunori, Fukai Junya, Izumoto Shuichi, Ishibashi Kenichi, Nakajima Yoshikazu, Shofuda Tomoko, Kanematsu Daisuke, Yoshioka Ema, Kodama Yoshinori, Mano Masayuki, Mori Kanji, Ichimura Koichi, Kanemura Yonehiro
発行年月	2018/08
概要	Molecular biological characterization of tumors has become a pivotal procedure for glioma patient care. The aim of this study is to build conventional MRI-based radiomics model to predict genetic alterations within grade II/III gliomas attempting to implement lesion location information in the model to improve diagnostic accuracy. One-hundred and ninety-nine grade II/III gliomas patients were enrolled. Three molecular subtypes were identified: IDH1/2-mutant, IDH1/2-mutant with TERT promoter mutation, and IDH-wild type. A total of109 radiomics features from 169 MRI datasets and location information from 199 datasets were extracted. Prediction modeling for genetic alteration was trained via LASSO regression for 111 datasets and validated by the remaining 58 datasets. IDH mutation was detected with an accuracy of 0.82 for the training set and 0.83 for the validation set without lesion location information. Diagnostic accuracy improved to 0.85 for the training set and 0.87 for the validation set when lesion location information was implemented. Diagnostic accuracy for predicting 3 molecular subtypes of grade II/III gliomas was 0.74 for the training set and 0.56 for the validation set with lesion location information implemented. Conventional MRI-based radiomics is one of the most promising strategies that may lead to a non-invasive diagnostic technique for molecular characterization of grade II/III gliomas.
DOI	10.1038/s41598-018-30273-4
PMID	30082856