Laser tweezer Raman spectroscopy combined with deep neural networks for identification of liver cancer cells

shuyun, Weng; Lin, Feng; Pan, Changbin; Zhang, Qiyi; Tao, Hong; Fan, Min; Xu, Luyun; Kong, Kien Voon; Chen, Yuanmei; Lin, Duo; Feng, Shouhua

doi:10.1016/j.talanta.2023.124753

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…Also, H. Yan et al obtained excellent classification accuracy (97.2%), sensitivity (99.1%), and specificity (95.4%) in the discrimination of tongue squamous cell carcinoma from adjacent non-tumorous tissues with Raman spectroscopy and a CNN [17]. As for cell samples, W. Shuyun et al reported an accuracy mean of 99.2 ± 5.1%, a sensitivity mean of 99.2 ± 5.1%, and a specificity mean of 99.8 ± 1.0% for the classification of different kinds of liver cancer cell lines by means of laser tweezer Raman spectroscopy combined with a deep neural network [18].…”

Section: Introductionmentioning

confidence: 99%

Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2024

Photonics

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Colorectal cancer is very widespread in developed countries. Its diagnosis partly depends on pathologists’ experience and their laboratories’ instrumentation, producing uncertainty in diagnosis. The use of spectroscopic techniques sensitive to the cellular biochemical environment could aid in achieving a reliable diagnosis. So, we used Raman micro-spectroscopy, combined with a spectral analysis by means of machine learning methods, to build classification models, which allow colon cancer to be diagnosed in cell samples, in order to support such methods as complementary tools for achieving a reliable identification of colon cancer. The Raman spectra were analyzed in the 980–1800 cm−1 range by focusing the laser beam onto the nuclei and the cytoplasm regions of single FHC and CaCo-2 cells (modelling healthy and cancerous samples, respectively) grown onto glass coverslips. The comparison of the Raman intensity of several spectral peaks and the Principal Component Analysis highlighted small biochemical differences between healthy and cancerous cells mainly due to the larger relative lipid content in the former cells with respect to the latter ones and to the larger relative amount of nucleic acid components in cancerous cells compared with healthy ones. We considered four classification algorithms (logistic regression, support vector machine, k nearest neighbors, and a neural network) to associate unknown Raman spectra with the cell type to which they belong. The built machine learning methods achieved median values of classification accuracy ranging from 95.5% to 97.1%, sensitivity values ranging from 95.5% to 100%, and specificity values ranging from 93.9% to 97.1%. The same median values of the classification parameters, which were estimated for a testing set including unknown spectra, ranged between 93.1% and 100% for accuracy and between 92.9% and 100% for sensitivity and specificity. A comparison of the four methods pointed out that k nearest neighbors and neural networks better perform the classification of nucleus and cytoplasm spectra, respectively. These findings are a further step towards the perspective of clinical translation of the Raman technique assisted by multivariate analysis as a support method to the standard cytological and immunohistochemical methods for diagnostic purposes.

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Section: Introductionmentioning

confidence: 99%

Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2024

Photonics

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“…8 Laser tweezer Raman spectroscopy (LTRS), which combines Raman spectroscopy and laser tweezers, is emerging as a powerful non-invasive tool for cellular analyses, including red blood cell detection, diagnosis of cancer, and detection of cell apoptosis. 3,9–14…”

Section: Introductionmentioning

confidence: 99%

Detection of nasopharyngeal cancer cells using the laser tweezer Raman spectroscopy technology

Chen,

Lin,

Zhang

et al. 2023

Anal. Methods

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“…The use of advanced signal processing schemes, using machine learning and artificial intelligence, in the extensive context of photonic systems, is rapidly expanding, where these methods are enabling a more efficient extraction of information from the optical signals. Consequently, more powerful sensing systems can be implemented, for instance, resulting in so-called, intelligent systems [4,[8][9][10][11][12][13]. While such approaches have a very strong potential, their reliability is very much dependent on the quality and dimension of the training sets, and usually drops considerably when facing 'real life' samples.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, while recent developments in this area have created new opportunities for intelligent devices for biomedical applications [4,10,11,14], practical systems still face problems for a reliable and seamless transition to technological applications. One of the most important factors is the reproducibility of the results, which may vary due to external conditions or operating procedures.…”

Section: Introductionmentioning

confidence: 99%

Autonomous and intelligent optical tweezers for improving the reliability and throughput of single particle analysis

Teixeira,

Moreira,

Oliveira

et al. 2023

Meas. Sci. Technol.

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Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.

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Laser tweezer Raman spectroscopy combined with deep neural networks for identification of liver cancer cells

Cited by 7 publications

References 36 publications

Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

Detection of nasopharyngeal cancer cells using the laser tweezer Raman spectroscopy technology

Autonomous and intelligent optical tweezers for improving the reliability and throughput of single particle analysis

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