High-precision intraoperative diagnosis of gliomas: integrating imaging and intraoperative flow cytometry with machine learning

作者：Shunichi Koriyama, Yutaka Matsui, Takahiro Shioyama, Mikoto Onodera, Manabu Tamura, Tatsuya Kobayashi, Buntou Ro, Kenta Masui, Takashi Komori, Yoshihiro Muragaki, Takakazu Kawamata · 发表于：Frontiers in Neurology · 年份：2025 · DOI：10.3389/fneur.2025.1647009 · 被引用次数：2 · 研究领域：Glioma Diagnosis and Treatment、Cell Image Analysis Techniques、Brain Tumor Detection and Classification

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: C-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.