ホリセ　ユキ   Horise Yuki   堀瀬　友貴    所属   医学研究科 医学研究科 (医学部医学科をご参照ください)    職種   非常勤講師
言語種別	英語
発表タイトル	Integration of intra-operative brain functionalpositions into the standard brain using SPM
会議名	CARS 2019 – 33rd International Congress and Exhibition
学会区分	国際学会及び海外の学会
発表形式	ポスター掲示
講演区分	一般
発表者・共同発表者	OHSHIMA Kazuma, SATO Ikuma, NAMBU Y, FUJINO Yuichi, HORISE Yuki, KUSUDA Kaori, TAMURA Manabu, MURAGAKI Yoshihiro, MASAMUNE Ken
発表年月日	2019/06/20
開催地 （都市, 国名）	Rennes, France
学会抄録	CARS 2019 21(4) 2019 CARS 2019 – 33rd International Congress and Exhibition
概要	Purpose ‘‘Future-predicting glioma surgery’’ has been suggested for glioma resection surgery [1]. Future-predicting glioma surgery can facilitate clinical decision-making by predicting the survival rate and postoperative neurological complications with respect to the set resection area. However, storage of evidenced-based information for realizing future-predicting glioma surgery has not yet been sufficiently done. To enable future-predicting glioma surgery, we need to create datasets for predicting the survival rate and post-operative neurological complications extracted from post-surgery information. In a previous study, digitized brain functional positions and methods for creation of datasets for statistical analysis and a method of integrating intra-operative electrical stimulation positions into the standard brain were suggested [1, 2]. As individual differences exist in intra-operative information among patients, position information is unified in the standard brain coordinate system, and individual differences can be eliminated by standardization. Using the intra-operative information obtained by these studies to integrate them into the standard brain allows the analysis of evidenced-based information. However, the available datasets for statistical analysis are not adequate. In this study, for creating analysis datasets to realize future prediction surgery, brain functional positions with individual differences of each patient were integrated into the standard brain by statistical parametric mapping (SPM) and were visualized. In addition, w