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

Idrees, Bushra Sana; Wang, Qianqian; Khan, Muhammad Nouman; Teng, Geer; Cui, Xutai; Xiangli, Wenting; Wei, Kai

doi:10.1364/boe.442489

Cited by 8 publications

(7 citation statements)

References 32 publications

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“…The slices with lateral sizes of around 2 × 1.5 cm were placed on a microscopy slide. In LIBS analysis on very thin samples, an important issue is the choice of substrate 28 , which can present strong spectral lines of impurities 22 . For this reason, we have chosen high purity quartz glass (Plano GmbH) substrates, where the only emission lines present in the measured spectral range are those of silicon (Si), as it can be seen in the insertion in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…In the LIBS process, the material is ionized and plasma is produced, which upon cooling will emit radiation specific to the chemical elements existing in the material. Many studies that try to identify different types of malignant tissue are carried out using nanosecond lasers [12][13][14][15][16][17][18][19][20][21][22] , producing high temperature plasma with significant thermal damage to the sample and a decrease in spatial resolution 23 . In previous studies, we have shown that femtosecond (fs) pulses can be used for precision in situ/ in vivo LIBS analysis of biological tissue 23 and technical samples 24 , allowing a spatial resolution on the order of microns and below 25 .…”

Section: Identification Of Tumor Tissue In Thin Pathological Samples ...mentioning

confidence: 99%

“…The type of tissue from which the recorded spectrum originates (used in training and evaluation of ML algorithms) could be identified by direct comparison with the results of the pathological analysis on adjacent cuts. To our knowledge, this type of analysis on samples of micrometric thickness was performed only in one recent study on gastrointestinal tumor tissue by using ns-laser pulses 22 , but the spectra presented in this publication have a strong spectral contribution from the substrate, with many emission lines overlapping with the ones coming from the biological tissue. Using ultrashort laser pulses and a high-purity quartz substrate, we performed ablation with a high spatial resolution and low collateral damage, and the recorded spectra contain only contributions from the sample.…”

Section: Identification Of Tumor Tissue In Thin Pathological Samples ...mentioning

confidence: 99%

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

Sarpe-Tudoran¹,

Ciobotea²,

Morscher³

et al. 2023

Sci Rep

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Identification Of Tumor Tissue In Thin Pathological Samples ...mentioning

confidence: 99%

Section: Identification Of Tumor Tissue In Thin Pathological Samples ...mentioning

confidence: 99%

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

Sarpe-Tudoran¹,

Ciobotea²,

Morscher³

et al. 2023

Sci Rep

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“…The concentration of Ca is more important than other elements for also the identification of many other malignancies. Excess of Ca lines with high intensities in Gastrointestinal stromal tumor (GIST) tissues in comparison to its healthy tissues is related to the presence of tumor [ 32 ]. It may also show great distinguishing ability in breast cancer detection.…”

Section: Data Processing Procedures and Methodsmentioning

confidence: 99%

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

IDREES¹,

Teng²,

Israr³

et al. 2023

Biomed. Opt. Express

Self Cite

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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.

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“…Several researchers have shown the effectiveness of machine learning methods and automatic classification of spectral data in detecting cancerous lesions [31,37,46]. For example, in [31], the effectiveness of Raman spectroscopy in detecting cancerous lesions of the oral cavity was studied.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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

Cited by 8 publications

References 32 publications

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

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

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

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

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