Applications of Raman spectroscopy in clinical medicine

Qi, Yaping; Chen, Esther Xinyi; Hu, Dan; Yang, Ying; Wu, Zhenping; Zheng, Ming; Sadi, Mohammad A.; Jiang, Yucheng; Zhang, Kang; Chen, Zi; Chen, Yong P.

doi:10.1002/fft2.335

Cited by 4 publications

(2 citation statements)

References 226 publications

(306 reference statements)

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“…Within RS, PCA proves invaluable for delving into data, spotting outliers, and discerning spectral differences tied to distinct sample attributes or compositions. 118 At its core, PCA transforms a set of potentially correlated variables into a fresh array of uncorrelated variables known as principal components (PCs).…”

Section: Advanced Chemometric Methods Applicable In Raman Spectroscop...mentioning

confidence: 99%

Unveiling brain disorders using liquid biopsy and Raman spectroscopy

Ranasinghe,

Wang,

Huang

2024

Nanoscale

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Section: Advanced Chemometric Methods Applicable In Raman Spectroscop...mentioning

confidence: 99%

Unveiling brain disorders using liquid biopsy and Raman spectroscopy

Ranasinghe,

Wang,

Huang

2024

Nanoscale

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“…Complex information can be extracted from spectral statistics using machine learning techniques. Machine learning models can be applied to identify the characteristics of Raman spectra and classify substances, thereby enhancing the diagnostic capability of diseases ( Qi et al, 2024 ). Gayap, H. T. et al conducted a review of recent studies on the use of deep learning methods for lung cancer detection and diagnosis.…”

Section: Applying Raman Spectroscopy To Screen and Diagnose Early-sta...mentioning

confidence: 99%

The application of Raman spectroscopy for the diagnosis and monitoring of lung tumors

Miao,

Wu,

Qiang

et al. 2024

Front. Bioeng. Biotechnol.

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Raman spectroscopy is an optical technique that uses inelastic light scattering in response to vibrating molecules to produce chemical fingerprints of tissues, cells, and biofluids. Raman spectroscopy strategies produce high levels of chemical specificity without requiring extensive sample preparation, allowing for the use of advanced optical tools such as microscopes, fiber optics, and lasers that operate in the visible and near-infrared spectral range, making them increasingly suitable for a wide range of medical diagnostic applications. Metal nanoparticles and nonlinear optical effects can improve Raman signals, and optimized fiber optic Raman probes can make real-time, in vivo, single-point observations. Furthermore, diagnostic speed and spatial accuracy can be improved through the multimodal integration of Raman measurements and other technologies. Recent studies have significantly contributed to the improvement of diagnostic speed and accuracy, making them suitable for clinical application. Lung cancer is a prevalent type of respiratory malignancy. However, the use of computed tomography for detection and screening frequently reveals numerous smaller lung nodules, which makes the diagnostic process more challenging from a clinical perspective. While the majority of small nodules detected are benign, there are currently no direct methods for identifying which nodules represent very early-stage lung cancer. Positron emission tomography and other auxiliary diagnostic methods for non-surgical biopsy samples from these small nodules yield low detection rates, which might result in significant expenses and the possibility of complications for patients. While certain subsets of patients can undergo curative treatment, other individuals have a less favorable prognosis and need alternative therapeutic interventions. With the emergence of new methods for treating cancer, such as immunotherapies, which can potentially extend patient survival and even lead to a complete cure in certain instances, it is crucial to determine the most suitable biomarkers and metrics for assessing the effectiveness of these novel compounds. This will ensure that significant treatment outcomes are accurately measured. This review provides a comprehensive overview of the prospects of Raman spectroscopy and its applications in the diagnosis and analysis of lung tumors.

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