ArticleNeuro-oncology advances
Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Illuminating glioma surgery: A meta-analysis of Raman spectroscopy for intraoperative decision-making.Neurosurgical review · 2025Pooled it
- Metabolic Imaging as Future Technology and Innovation in Brain-Tumour Surgery: A Systematic Review.Current oncology (Toronto, Ont.) · 2025Pooled it
- Shining a light on brain tumors: a review of Raman spectroscopy in the operating room.Journal of biomedical optics · 2026Review
- Intraoperative Spectroscopic and Mass Spectrometric Assessment of Glioma Margins: A Systematic Review and Meta-Analysis.Cancers · 2026Review
- Image-Guided Brain Tumor Surgery.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- A Comprehensive Review of Margin Identification Methods in Soft Tissue Sarcoma.Current oncology (Toronto, Ont.) · 2025Review
- Review
- Towards Optical Biopsy in Glioma Surgery.International journal of molecular sciences · 2025Review
- Intraoperative use of high-speed Raman spectroscopy during soft tissue sarcoma resection.Scientific reports · 2025Article
- Review
- Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.Neuro-oncology · 2024Article
- Rapid visualization of PD-L1 expression level in glioblastoma immune microenvironment via machine learning cascade-based Raman histopathology.Journal of advanced research · 2024Article
- Current Applications of Raman Spectroscopy in Intraoperative Neurosurgery.Biomedicines · 2024Review
- Spatial oncology: Translating contextual biology to the clinic.Cancer cell · 2024Review
- Real-time glioblastoma tumor microenvironment assessment by SpiderMass for improved patient management.Cell reports. Medicine · 2024Article
- Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy.Brain sciences · 2024Article
- Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms.Molecules (Basel, Switzerland) · 2024Article
- Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation.BMC cancer · 2024Article
- Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics.Journal of personalized medicine · 2023Article
- Intraoperative Imaging and Optical Visualization Techniques for Brain Tumor Resection: A Narrative Review.Cancers · 2023Review
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9 authors.
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Abstract
backgroundThe molecular genetic classification of gliomas, particularly the identification of isocitrate dehydrogenase (IDH) mutations, is critical for clinical and surgical decision-making. Raman spectroscopy probes the unique molecular vibrations of a sample to accurately characterize its molecular composition. No sample processing is required allowing for rapid analysis of tissue. The aim of this study was to evaluate the ability of Raman spectroscopy to rapidly identify the common molecular genetic subtypes of diffuse glioma in the neurosurgical setting using fresh biopsy tissue. In addition, classification models were built using cryosections, formalin-fixed paraffin-embedded (FFPE) sections and LN-18 (IDH-mutated and wild-type parental cell) glioma cell lines.
methodsFresh tissue, straight from neurosurgical theatres, underwent Raman analysis and classification into astrocytoma, IDH-wild-type; astrocytoma, IDH-mutant; or oligodendroglioma. The genetic subtype was confirmed on a parallel section using immunohistochemistry and targeted genetic sequencing.
resultsFresh tissue samples from 62 patients were collected (36 astrocytoma, IDH-wild-type; 21 astrocytoma, IDH-mutated; 5 oligodendroglioma). A principal component analysis fed linear discriminant analysis classification model demonstrated 79%-94% sensitivity and 90%-100% specificity for predicting the 3 glioma genetic subtypes. For the prediction of IDH mutation alone, the model gave 91% sensitivity and 95% specificity. Seventy-nine cryosections, 120 FFPE samples, and LN18 cells were also successfully classified. Meantime for Raman data collection was 9.5 min in the fresh tissue samples, with the process from intraoperative biopsy to genetic classification taking under 15 min.
conclusionThese data demonstrate that Raman spectroscopy can be used for the rapid, intraoperative, classification of gliomas into common genetic subtypes.
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