ArticleBiomedical optics express2022
In-vitro study on the identification of gastrointestinal stromal tumor tissues using laser-induced breakdown spectroscopy with chemometric methods.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 18 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
- On exploring the potential of machine learning assisted nanoparticles enhanced laser induced breakdown spectroscopy as an initial screening tool for early breast cancer detection.Mikrochimica acta · 2025Article
- Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue.Biomedical optics express · 2023Article
- Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning.Scientific reports · 2023Article
- Comparison of whole blood and serum samples of breast cancer based on laser-induced breakdown spectroscopy with machine learning.Biomedical optics express · 2023Article
- Diffuse Reflectance Spectroscopy of the Oral Mucosa: In Vivo Experimental Validation of the Precancerous Lesions Early Detection Possibility.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early-stage detection of tumors helps to improve patient survival rate. In this work, we demonstrate a novel discrimination method to diagnose the gastrointestinal stromal tumor (GIST) and its healthy formalin fixed paraffin embedded (FFPE) tissues by combining chemometric algorithms with laser-induced breakdown spectroscopy (LIBS). Chemometric methods which include partial least square discrimination analysis (PLS-DA), k-nearest neighbor (k-NN) and support vector machine (SVM) were used to build the discrimination models. The comparison of PLS-DA, k-NN and SVM classifiers shows an increase in accuracy from 94.44% to 100%. The comparison of LIBS signal between the healthy and infected tissues shows an enhancement of calcium lines which is a signature of the presence of GIST in the FFPE tissues. Our results may provide a complementary method for the rapid detection of tumors for the successful treatment of patients.
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Registered trials
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