• Hirofumi Kazama, Mitsuto Hanihara, Kentaro Yoshimura, Tomohiko Iwano, Ryu Saito, Masakazu Ogiwara, Tomoyuki Kawataki, Hideyuki Yoshioka, and Hiroyuki Kinouchi.
• Department of Neurosurgery, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Chuo, Yamanashi, Japan.
• World Neurosurg. 2025 Jan 9; 194: 123577123577.

ObjectiveMalignant glioma progresses rapidly and shows poor prognosis, but clinically applicable blood plasma-based biochemical tumor markers remain lacking. This study aimed to develop a diagnostic system using probe electrospray ionization mass spectrometry (PESI-MS) and a machine-learning logistic regression model to detect plasma changes at various time points in a murine glioma model.MethodsWe used a syngeneic intracranial orthotopic murine model with GL261 glioma cells. Blood plasmas were collected before and 3, 7, and 14 days after intracranial transplantation of glioma cells (tumor group, n = 7) or injection of phosphate-buffered saline (control group, n = 8). Mass spectra from those samples were obtained using PESI-MS and compared between control and tumor groups. We explored changes in mass spectra at the 3 time points (3, 7, and 14 days) after transplantation. The performance of machine-learning logistic regression-based diagnosis algorithm was evaluated to clarify the potential utility for early diagnosis.ResultsSixteen significant mass spectrum peaks were identified between the tumor and control groups. Multiple logistic regression analysis revealed 5 key mass spectra, achieving sensitivity of 0.875 and specificity of 0.943 for tumor discrimination. The area under the receiver operating characteristic curve was 0.981, outperforming analyses of individual spectra.ConclusionsThese results indicate that PESI-MS combined with machine learning-based diagnostics in blood plasma could be a promising approach to accurate detection of malignant glioma.Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

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