ArticleCommunications medicine2024
Towards machine learning-based quantitative hyperspectral image guidance for brain tumor resection.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Hyperspectral imaging for tumor resection guidance in surgery: a systematic review of preclinical and clinical studies.Journal of biomedical optics · 2025Pooled it
- Artificial intelligence models in the surgical planning of low-grade gliomas: a systematic review.Frontiers in oncology · 2025Pooled it
- Article
- Precise delineation of glioma margins in hyperspectral images using cross-channel spectral feature fusion.Biomedical optics express · 2026Article
- Multimodal HSI-MRI system for tumor delineation in neurosurgical guidance.International journal of computer assisted radiology and surgery · 2026Article
- Machine Learning-Based Fluorescence Assessment for Augmented Imaging and Decision Support in Glioblastoma Resections.Cancers · 2026Article
- Image-Guided Brain Tumor Surgery.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- A narrative review on artificial intelligence in neurosurgery: ethical challenges and implementation considerations.Annals of medicine and surgery (2012) · 2025Review
- GLIMMER: an interim subgroup analysis from an ongoing prospective study evaluating hyperspectral imaging for MGMT promoter methylation in gliomas.Journal of neuro-oncology · 2025Observational
- Engineering Nanomaterials for Raman Bioimaging and Theranostics: Advances, Challenges, and Future Directions.ACS omega · 2025Review
- Review
- A Compact Fluorescence System for Tumor Detection: Performance and Integration Potential.Biosensors · 2025Article
- Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications.Journal of biomedical optics · 2025Review
- Utility and Safety of 5-ALA Guided Surgery in Pediatric Brain Tumors: A Systematic Review.Cancers · 2024Review
- Spectral library and method for sparse unmixing of hyperspectral images in fluorescence guided resection of brain tumors.Biomedical optics express · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComplete resection of malignant gliomas is hampered by the difficulty in distinguishing tumor cells at the infiltration zone. Fluorescence guidance with 5-ALA assists in reaching this goal. Using hyperspectral imaging, previous work characterized five fluorophores' emission spectra in most human brain tumors.
methodsIn this paper, the effectiveness of these five spectra was explored for different tumor and tissue classification tasks in 184 patients (891 hyperspectral measurements) harboring low- (n = 30) and high-grade gliomas (n = 115), non-glial primary brain tumors (n = 19), radiation necrosis (n = 2), miscellaneous (n = 10) and metastases (n = 8). Four machine-learning models were trained to classify tumor type, grade, glioma margins, and IDH mutation.
resultsUsing random forests and multilayer perceptrons, the classifiers achieve average test accuracies of 84-87%, 96.1%, 86%, and 91% respectively. All five fluorophore abundances vary between tumor margin types and tumor grades (p < 0.01). For tissue type, at least four of the five fluorophore abundances are significantly different (p < 0.01) between all classes.
conclusionsThese results demonstrate the fluorophores' differing abundances in different tissue classes and the value of the five fluorophores as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.