ムラガキ　ヨシヒロ   MURAGAKI Yoshihiro   村垣　善浩    所属   医学部 医学科（東京女子医科大学病院）    職種   客員教授
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	High-precision intraoperative diagnosis of gliomas: integrating imaging and intraoperative flow cytometry with machine learning
掲載誌名	正式名：Frontiers in Neurology ISSNコード：16642295
掲載区分	国外
巻・号・頁	16,pp.**-**
著者・共著者	KORIYAMA Shunichi†, MATSUI Yutaka, SHIOYAMA Takahiro, ONODERA Mikoto, TAMURA Manabu, KOBAYASHI Tatsuya, RO Buntou, MASUI Kenta, KOMORI Takashi, MURAGAKI Yoshihiro, KAWAMATA Takakazu
発行年月	2025/09
概要	Introduction: Accurate intraoperative identification of glioma molecular subtypes, such as isocitrate dehydrogenase mutation and 1p/19q co-deletion, is essential for precise diagnosis, prognostication, and determining the extent of tumor resection—balancing maximal tumor removal with preservation of neurological function. Methods: We developed a machine learning model that integrates preoperative imaging features [magnetic resonance imaging, computed tomography, and 11C-methionine positron emission tomography (PET)] and intraoperative flow cytometry (iFC) data to predict molecular subtypes of glioma in real-time. Results: Analyzing 288 cases of diffuse gliomas, this model achieved an overall accuracy of 76.0%, with a macro-average ROC-AUC of 0.88 and a micro-average ROC-AUC of 0.89. Key predictive factors included the tumor-to-normal uptake ratio on PET, malignancy index from iFC, and patient age, all of which showed significant differences between correctly and incorrectly classified cases. We also developed a prototype application that visualizes the prediction results intraoperatively, thereby supporting real-time surgical decision-making. Conclusion: This integrated approach enhances the precision of intraoperative molecular diagnosis and has the potential to optimize surgical strategies for glioma treatment.
DOI	10.3389/fneur.2025.1647009