Due to the new demonstrations of Laser-induced breakdown spectroscopy (LIBS) applicability in a surprisingly wide variety of applications, the use of LIBS as a medical diagnostic tool is steadily gaining momentum. Especially in different cancer diseases, LIBS has the potential to become a fast and valuable analytical tool. We addressed LIBS equipment and quantitative analytical procedures, and signal enhancement techniques for improving element detection. For detailed aspects of applications, we reviewed the recent progress of LIBS in different cancer diseases diagnoses by using different tissues and medical fluid as samples. To fulfill the high demands in the medical industry and overcome the severe tissue sample problem, it is proposed that the chemometric and signal amplification techniques for quantitative analysis should be employed, and robust and effective LIBS devices should be developed. This overview of the different cancers by LIBS is meant to summarize the research performed to date and suggest some suitable advanced chemometrics techniques and effective LIBS devices, if successfully implemented, would be significantly beneficial to the medical field in the future.
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.
Discrimination and identification of melanoma (a kind of skin cancer) by using laser-induced breakdown spectroscopy (LIBS) combined with chemometrics methods are reported. The human melanoma and normal tissues are used in the form of formalin-fixed paraffin-embedded (FFPE) blocks as samples. The results demonstrated higher LIBS signal intensities of phosphorus (P), potassium (K), sodium (Na), magnesium (Mg), and calcium (Ca) in melanoma FFPE samples while lower signal intensities in normal FFPE tissue samples. Chemometric methods, artificial neural network (ANN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and partial least square discriminant analysis (PLS-DA) are used to build the classification models. Different preprocessing methods, standard normal variate (SNV), mean-centering, normalization by total area, and autoscaling, were compared. A good performance of the model (sensitivity, specificity, and accuracy) for melanoma and normal FFPE tissues has been achieved by the ANN and PLS-DA models (all were 100%). The results revealed that LIBS combined with chemometric methods for detection and discrimination of human malignancies is a reliable, accurate, and precise technique.
Limited by the lack of training spectral data in different kinds of tissues, the diagnostic accuracy of laser-induced breakdown spectroscopy (LIBS) is hard to reach the desired level with normal supervised learning identification methods. In this paper, we proposed to apply the predictive data clustering methods with supervised learning methods together to identify tissue information accurately. The meanshift clustering method is introduced to compare with three other clustering methods which have been used in LIBS field. We proposed the cluster precision (CP) score as a new criterion to work with Calinski-Harabasz (CH) score together for the evaluation of the clustering effect. The influences of principal component analysis (PCA) on all four kinds of clustering methods are also analyzed. PCA-meanshift shows the best clustering effect based on the comprehensive evaluation combined CH and CP scores. Based on the spatial location and feature similarity information provided by the predictive clustering, the PCA-Meanshift can improve diagnosis accuracy from less than 95% to 100% for all classifiers including support vector machine (SVM), k nearest neighbor (k-NN), soft independent modeling of class analogy (Simca) and random forests (RF) models.
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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