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Updated: Jul 27, 2025
Laser-induced Breakdown Spectroscopy: A New Approach for Nanoparticle's Mapping and Quantification in Organ Tissue
Published on: June 18, 2014
Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and
Cristian Sarpe1, Elena Ramela Ciobotea1, Christoph Burghard Morscher1
1Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany.
Abstract:
In the treatment of most newly discovered solid cancerous tumors, surgery remains the first treatment option. An important factor in the success of these operations is the precise identification of oncological safety margins to ensure the complete removal of the tumor without affecting much of the neighboring healthy tissue. Here we report on the possibility of applying femtosecond Laser-Induced Breakdown Spectroscopy (LIBS) combined with Machine Learning algorithms as an alternative discrimination technique to differentiate cancerous tissue. The emission spectra following the ablation on thin fixed liver and breast postoperative samples were recorded with high spatial resolution; adjacent stained sections served as a reference for tissue identification by classical pathological analysis. In a proof of principle test performed on liver tissue, Artificial Neural Networks and Random Forest algorithms were able to differentiate both healthy and tumor tissue with a very high Classification Accuracy of around 0.95. The ability to identify unknown tissue was performed on breast samples from different patients, also providing a high level of discrimination. Our results show that LIBS with femtosecond lasers is a technique with potential to be used in clinical applications for rapid identification of tissue type in the intraoperative surgical field.
Insights
Femtosecond Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning can accurately differentiate cancerous tissue from healthy tissue. This innovative technique shows promise for real-time intraoperative surgical guidance.
Area of Science:
- Biomedical Optics
- Spectroscopy
- Computational Biology
Background:
- Surgery is the primary treatment for solid cancerous tumors.
- Accurate identification of oncological safety margins is crucial for successful tumor removal and patient outcomes.
- Current methods for intraoperative tissue identification can be time-consuming.
Purpose of the Study:
- To investigate the potential of femtosecond Laser-Induced Breakdown Spectroscopy (LIBS) combined with Machine Learning (ML) algorithms.
- To develop an alternative discrimination technique for differentiating cancerous and healthy tissue.
- To assess the feasibility of this technique for intraoperative surgical applications.
Main Methods:
- Femtosecond LIBS was used to ablate thin, fixed liver and breast postoperative samples.
- Emission spectra were recorded with high spatial resolution.
- Artificial Neural Networks and Random Forest algorithms were employed for tissue classification, with classical pathological analysis serving as the reference.
Main Results:
- In liver tissue, ML algorithms achieved a high classification accuracy of approximately 0.95 in differentiating healthy and tumor tissues.
- The technique demonstrated a high level of discrimination when applied to identify unknown breast tissue samples from different patients.
- High spatial resolution spectral data enabled effective tissue differentiation.
Conclusions:
- Femtosecond LIBS coupled with ML algorithms shows significant potential as a rapid, accurate method for intraoperative tissue identification.
- This technology could aid surgeons in precisely determining tumor margins during operations.
- Further development may lead to improved surgical outcomes by ensuring complete tumor resection while preserving healthy tissue.
