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

Modulate the laser phase to improve the ns-LIBS spectrum signal based on orbital angular momentum

Aiming to enhance the ns-LIBS signal, in this work, we introduced orbital angular momentum to modulate the laser phase of the Gaussian beam into the vortex beam. Under similar incident laser energy, the vortex beam promoted more uniform ablation and more ablation mass compared to the Gaussian beam, leading to elevated temperature and electron density in the laser-induced plasma. Consequently, the intensity of the ns-LIBS signal was improved. The enhancement effects based on the laser phase modulation were investigated on both metallic and non-metallic samples. The results showed that laser phase modulation resulted in a maximum 1.26-times increase in the peak intensities and a maximum 1.25-times increase in the signal-to-background ratio (SBR) of the Cu spectral lines of pure copper for a laser energy of 10 mJ. The peak intensities of Si atomic spectral lines were enhanced by 1.58-1.94 times using the vortex beam. Throughout the plasma evolution process, the plasma induced by the vortex beam exhibited prolonged duration and a longer continuous background, accompanied by a noticeable reduction in the relative standard deviation (RSD). The experimental results demonstrated that modulation the laser phase based on orbital angular momentum is a promising approach to enhancing the ns-LIBS signal.

Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue

The glioma boundary is difficult to identify during surgery due to the infiltrative characteristics of tumor cells. In order to ensure a full resection rate and increase the postoperative survival of patients, it is often necessary to make an expansion range resection, which may have harmful effects on the quality of the patient's survival. A full-Stokes laser-induced breakdown spectroscopy (FSLIBS) theory with a corresponding system is proposed to combine the elemental composition information and polarization information for glioma boundary detection. To verify the elemental content of brain tissues and provide an analytical basis, inductively coupled plasma mass spectrometry (ICP-MS) and LIBS are also applied to analyze the healthy, boundary, and glioma tissues. Totally, 42 fresh tissue samples are analyzed, and the Ca, Na, K elemental lines and CN, C₂ molecular fragmental bands are proved to take an important role in the different tissue identification. The FSLIBS provides complete polarization information and elemental information than conventional LIBS elemental analysis. The Stokes parameter spectra can significantly reduce the under-fitting phenomenon of artificial intelligence identification models. Meanwhile, the FSLIBS spectral features within glioma samples are relatively more stable than boundary and healthy tissues. Other tissues may be affected obviously by individual differences in lesion positions and patients. In the future, the FSLIBS may be used for the precise identification of glioma boundaries based on polarization and elemental characterizing ability.

Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning

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.

Comparison of whole blood and serum samples of breast cancer based on laser-induced breakdown spectroscopy with machine learning

To identify cancer from non-cancer is one of the most challenging issues nowadays in the early diagnosis of cancer. The primary issue of early detection is to choose a suitable type of sample collection to diagnose cancer. A comparison of whole blood and serum samples of breast cancer was studied using laser-induced breakdown spectroscopy (LIBS) with machine learning methods. For LIBS spectra measurement, blood samples were dropped on a substrate of boric acid. For the discrimination of breast cancer and non-cancer samples, eight machine learning models were applied to LIBS spectral data, including decision tree, discrimination analysis, logistic regression, naïve byes, support vector machine, k-nearest neighbor, ensemble and neural networks classifiers. Discrimination between whole blood samples showed that narrow neural networks and trilayer neural networks both provided 91.7% highest prediction accuracy and serum samples showed that all the decision tree models provided 89.7% highest prediction accuracy. However, using whole blood as sample achieved the strong emission lines of spectra, better discrimination results of PCA and maximum prediction accuracy of machine learning models as compared to using serum samples. These merits concluded that whole blood samples could be a good option for the rapid detection of breast cancer. This preliminary research may provide the complementary method for early detection of breast cancer.

Diffuse Reflectance Spectroscopy of the Oral Mucosa: In Vivo Experimental Validation of the Precancerous Lesions Early Detection Possibility

This article is devoted to the experimental validation of the possibility of early detection of precancerous lesions in the oral mucosa in vivo using diffuse reflectance spectroscopy in the wavelength range from 360 to 1000 nm. During the study, a sample of 119 patients with precancerous lesions has been collected and analyzed. As a result of the analysis, the most informative wavelength ranges were determined, in which the maximum differences in the backscattering spectra of lesions and intact tissues were observed, methods for automatic classification of backscattering spectra of the oral mucosa were studied, sensitivity and specificity values, achievable using diffuse reflectance spectroscopy for detecting hyperkeratosis on the tongue ventrolateral mucosa surface and buccal mucosa, were evaluated. As a result of preliminary experimental studies in vivo, the possibility of automatic detection of precancerous lesions of the oral mucosa surface using diffuse reflectance spectroscopy in the wavelength range from 500 to 900 nm with an accuracy of at least 75 percent has been shown.