TAMURA Manabu
   Department   Graduate School of Medical Science, Graduate School of Medical Science
   Position   Associate Professor
Article types Original article
Language English
Peer review Peer reviewed
Title High-precision intraoperative diagnosis of gliomas: integrating imaging and intraoperative flow cytometry with machine learning
Journal Formal name：Frontiers in Neurology
ISSN code：16642295
Domestic / ForeginForegin
Volume, Issue, Page 16,pp.1647009
Author and coauthor KORIYAMA Shunichi†, MATSUI Yutaka, SHIOYAMA Takahiro, ONODERA Mikoto, TAMURA Manabu, KOBAYASHI Tatsuya, RO Buntou, MASUI Kenta, KOMORI Takashi, MURAGAKI Yoshihiro*, KAWAMATA Takakazu
Publication date 2025/09/09
Summary 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
PMID 40994716