Application of Surface-Enhanced Raman Spectroscopy in the Screening of Pulmonary Adenocarcinoma Nodules

Peng, Bowen; Yan, Huan; Lin, Runrui; Guo, Yin

doi:10.1155/2022/4368928

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…The model ultimately achieved an accuracy of 93.33% in differentiating among these three groups of samples. The results highlight the potential of using nanoparticle-based SERS techniques in combination with SVM to assist in clinical diagnostic efforts and screening for LUAD ( Peng et al, 2022 ). Hollow gold nanospheres (HGNs) have substantial single-particle enhancement effects, with hotspots localized on the pinholes found inside their structure ( Figure 3 ).…”

Section: Raman Spectroscopy As a Tool To Screen Diagnose And Treat Ea...mentioning

confidence: 94%

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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Section: Raman Spectroscopy As a Tool To Screen Diagnose And Treat Ea...mentioning

confidence: 94%

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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show abstract

“…This is not only highly accurate, which assists in early screening, but also has the advantage of being non-invasive and causing no harm to the patient. [80] Furthermore, both the PCA-RF algorithm and the PCA-kNN algorithm developed by Elec et al have good classification results for serum samples of kidney cancer. [81] Apart from blood samples, the method applies to the analysis of a variety of other body fluid samples such as tears, urine, saliva, etc.…”

Section: Identification Of the Diseased And Healthy Populationmentioning

confidence: 99%

“…employed the SVM method to analyze the SERS spectra of serum samples and showed good screening for lung adenocarcinoma nodules with an accuracy of 93.33 %. This is not only highly accurate, which assists in early screening, but also has the advantage of being non‐invasive and causing no harm to the patient [80] . Furthermore, both the PCA‐RF algorithm and the PCA‐kNN algorithm developed by Elec et al.…”

Section: The Application Of Sers Combined With Machine Learning In Me...mentioning

confidence: 99%

SERS‐Based Biosensors Combined with Machine Learning for Medical Application**

et al. 2023

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Surface‐enhanced Raman spectroscopy (SERS) has shown strength in non‐invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi‐quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.

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Application of serum Raman spectroscopy combined with classification model for rapid breast cancer screening

Lin,

Peng,

et al. 2023

Front. Oncol.

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IntroductionThis study aimed to evaluate the feasibility of using general Raman spectroscopy as a method to screen for breast cancer. The objective was to develop a machine learning model that utilizes Raman spectroscopy to detect serum samples from breast cancer patients, benign cases, and healthy subjects, with puncture biopsy as the gold standard for comparison. The goal was to explore the value of Raman spectroscopy in the differential diagnosis of breast cancer, benign lesions, and healthy individuals.MethodsIn this study, blood serum samples were collected from a total of 333 participants. Among them, there were 129 cases of tumors (pathologically diagnosed as breast cancer and labeled as cancer), 91 cases of benign lesions (pathologically diagnosed as benign and labeled as benign), and 113 cases of healthy controls (labeled as normal). Raman spectra of the serum samples from each group were collected. To classify the normal, benign, and cancer sample groups, principal component analysis (PCA) combined with support vector machine (SVM) was used. The SVM model was evaluated using a cross-validation method. ResultsThe results of the study revealed significant differences in the mean Raman spectra of the serum samples between the normal and tumor/benign groups. Although the mean Raman spectra showed slight variations between the cancer and benign groups, the SVM model achieved a remarkable prediction accuracy of up to 98% for classifying cancer, benign, and normal groups. DiscussionIn conclusion, this exploratory study has demonstrated the tremendous potential of general Raman spectroscopy as a clinical adjunctive diagnostic and rapid screening tool for breast cancer.

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Application of Surface-Enhanced Raman Spectroscopy in the Screening of Pulmonary Adenocarcinoma Nodules

Cited by 3 publications

References 42 publications

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

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

SERS‐Based Biosensors Combined with Machine Learning for Medical Application**

Application of serum Raman spectroscopy combined with classification model for rapid breast cancer screening

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