In-vitro study on the identification of gastrointestinal stromal tumor tissues using laser-induced breakdown spectroscopy with chemometric methods.

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.

Evaluation of human melanoma and normal formalin paraffin-fixed samples using Raman and LIBS fused data.

In this research, we developed a novel method of quantitative analysis to increase the detection potential for screening and classification of skin cancer (melanoma). We fused two distinct optical approaches, an atomic spectroscopic detection technique laser-induced breakdown spectroscopy (LIBS) and a vibrational molecular spectroscopic technique known as Raman spectroscopy. Melanoma is a kind of skin cancer, also known as malignant melanoma, that developed in melanocytes cells, which produced melanin. Classification of melanoma cancerous tissues is a fundamental problem in biomedicine. For early melanoma cancer diagnosis and treatment, precise and accurate categorizing is critically essential. Laser-based spectroscopic approaches can be used as an operating instrument for simultaneous tissue ablation and ablated tissue elemental and molecular analysis. For this purpose, melanoma and normal paraffin-embedded tissues are used as a sample for LIBS and Raman measurement. We studied the data provided by laser-based spectroscopic methods using different machine learning classification techniques of extreme learning machine (ELM), partial least square discriminant analysis (PLS-DA), and K nearest neighbors (kNN). For visualization of melanoma and normal data, principal component analysis (PCA) is also used. Three different ways are used to process the data, LIBS measurement, Raman measurement, and combine data measurement (merged/fused data), and then compared the results. ELM classification model achieved the highest accuracy (100%) for combined data as well as for Raman and LIBS data, respectively. According to the experimental results, we can assume that Raman spectroscopy and LIBS combine can significantly improve the identification and classification accuracy of melanoma and normal specimens.

Acceleration of inverse design process on adaptive mesh.

In inverse design, the design and background areas can be represented by different spatial resolutions; thus, adaptive meshes are more efficient than structured meshes. In this study, a second-order interpolation scheme is introduced to realize an inverse design process on an adaptive mesh. Experiment results show that the proposed scheme yields a 1.79-fold acceleration over that achieved using a structured mesh, aiding design time reduction or design area expansion. As the design area can be divided into multiple areas with different spatial resolutions, in future work, adaptive meshes can be combined with machine learning algorithms to further improve the inverse-design-process efficiency.

Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis.

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.

Primary study of identification of parathyroid gland based on laser-induced breakdown spectroscopy.

The identification and preservation of parathyroid glands (PGs) is a major issue in thyroidectomy. The PG is particularly difficult to distinguish from the surrounding tissues. Accidental damage or removal of the PG may result in temporary or permanent postoperative hypoparathyroidism and hypocalcemia. In this study, a novel method for identification of the PG was proposed based on laser-induced breakdown spectroscopy (LIBS) for the first time. LIBS spectra were collected from the smear samples of PG and non-parathyroid gland (NPG) tissues (thyroid and neck lymph node) of rabbits. The emission lines (related to K, Na, Ca, N, O, CN, C2, etc.) observed in LIBS spectra were ranked and selected based on the important weight calculated by random forest (RF). Three machine learning algorithms were used as classifiers to distinguish PGs from NPGs. The artificial neural network classifier provided the best classification performance. The results demonstrated that LIBS can be adopted to discriminate between smear samples of PG and NPG, and it has a potential in intra-operative identification of PGs.

Pathological identification of brain tumors based on the characteristics of molecular fragments generated by laser ablation combined with a spiking neural network.

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.

Discrimination of hazardous bacteria with combination laser-induced breakdown spectroscopy and statistical methods.

Real-time biohazard detectors must be developed to facilitate the rapid implementation of appropriate protective measures against foodborne pathogens. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for the real-time detection of hazardous bacteria (HB) in the field. However, distinguishing among various HBs that exhibit similar C, N, O, H, or trace metal atomic emissions complicates HB detection by LIBS. This paper proposes the use of LIBS and chemometric tools to discriminate Staphylococcus aureus, Bacillus cereus, and Escherichia coli on slide substrates. Principal component analysis (PCA) and the genetic algorithm (GA) were used to select features and reduce the size of spectral data. Several models based on the artificial neural network (ANN) and the support vector machine (SVM) were built using the feature lines as input data. The proposed PCA-GA-ANN and PCA-GA-SVM discrimination approaches exhibited correct classification rates of 97.5% and 100%, respectively.

Importance evaluation of spectral lines in Laser-induced breakdown spectroscopy for classification of pathogenic bacteria.

The correct classification of pathogenic bacteria is significant for clinical diagnosis and treatment. Compared with the use of whole spectral data, using feature lines as the inputs of the classification model can improve the correct classification rate (CCR) and reduce the analyzing time. In order to select feature lines, we need to investigate the contribution to the CCR of each spectral line. In this paper, two algorithms, important weights based on principal component analysis (IW-PCA) and random forests (RF), were proposed to evaluate the importance of spectra lines. The laser-induced plasma spectra (LIBS) of six common clinical pathogenic bacteria species were measured and a support vector machine (SVM) classifier was used to classify the LIBS of bacteria species. In the proposed IW-PCA algorithm, the product of the loading of each line and the variance of the corresponding principal component were calculated. The maximum product of each line calculated from the first three PCs was used to represent the line's importance weight. In the RF algorithm, the Gini index reduction value of each line was considered as the line's importance weight. The experimental results demonstrated that the lines with high importance were more suitable for classification and can be chosen as feature lines. The optimal number of feature lines used in the SVM classifier can be determined by comparing the CCRs with a different number of feature lines. Importance weights evaluated by RF are more suitable for extracting feature lines using LIBS combined with an SVM classification mechanism than those evaluated by IW-PCA. Furthermore, the two methods mutually verified the importance of selected lines and the lines evaluated important by both IW-PCA and RF contributed more to the CCR.