ArticleBiomedical optics express2020
Pathological identification of brain tumors based on the characteristics of molecular fragments generated by laser ablation combined with a spiking neural network.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 16 citations in OpenAlex.
- From Fundamentals of Laser-Induced Breakdown Spectroscopy to Recent Advancements in Cancer Detection and Calcified Tissues Analysis: An Overview (2015-2025).Molecules (Basel, Switzerland) · 2025Review
- Signal enhancement in spark-assisted laser-induced breakdown spectroscopy for discrimination of glioblastoma and oligodendroglioma lesions.Biomedical optics express · 2023Article
- Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue.Biomedical optics express · 2023Article
- Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis.Biomedical optics express · 2021Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quick and accurate diagnosis helps shorten intraoperative waiting time and make a correct plan for the brain tumor resection. The common cryostat section method costs more than 10 minutes and the diagnostic accuracy depends on the sliced and frozen process and the experience of the pathologist. We propose the use of molecular fragment spectra (MFS) in laser-induced breakdown spectroscopy (LIBS) to identify different brain tumors. Formation mechanisms of MFS detected from brain tumors could be generalized into 3 categories, for instance, combination, reorganization and break. Four kinds of brain tumors (glioma, meningioma, hemangiopericytoma, and craniopharyngioma) from different patients were used as investigated samples. The spiking neural network (SNN) classifier was proposed to combine with the MFS (MFS-SNN) for the identification of brain tumors. SNN performed better than conventional machine learning methods for the analysis of similar and limited MFS information. With the ratio data type, the identification accuracy achieved 88.62% in 2 seconds.
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Registered trials
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