Raman spectroscopy as a potential diagnostic tool to analyse biochemical alterations in lung cancer

Zheng, Qingfeng; Li, Junyi; Yang, Lin; Zheng, Bo; Wang, Jiangcai; Lv, Ning; Luo, Jianbin; Martin, Francis; Liu, Dameng; He, Jie

doi:10.1039/c9an02175b

Cited by 35 publications

(35 citation statements)

References 36 publications

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“…A multivariate statistics algorithm with PCA-LDA analysis was used to analyze the identity between the control group and the three drugs treated groups. This algorithm is widely used in biomedical applications as a powerful spectral analysis method [45][46][47]. The normalized data of the whole Raman spectra was input into the SPSS software for factor analysis.…”

Section: Discussionmentioning

confidence: 99%

Study on the chemodrug-induced effect in nasopharyngeal carcinoma cells using laser tweezer Raman spectroscopy

Qiu¹,

Li²,

Li³

et al. 2020

Biomed. Opt. Express

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To explore the effect in nasopharyngeal carcinoma (NPC) cells after treatment with chemodrugs, Raman profiles were characterized by laser tweezer Raman spectroscopy. Two NPC cell lines (CNE2 and C666-1) were treated with gemcitabine, cisplatin, and paclitaxel, respectively. The high-quality Raman spectra of cells without or with treatments were recorded at the single-cell level with label-free laser tweezers Raman spectroscopy (LTRS) and analyzed for the differences of alterations of Raman profiles. Tentative assignments of Raman peaks indicated that the cellular specific biomolecular changes associated with drug treatment include changes in protein structure (e.g. 1655 cm −1), changes in DNA/RNA content and structure (e.g. 830 cm −1), destruction of DNA/RNA base pairs (e.g. 785 cm −1), and reduction in lipids (e.g. 970 cm −1). Besides, both principal components analysis (PCA) combined with linear discriminant analysis (LDA) and the classification and regression trees (CRT) algorithms were employed to further analyze and classify the spectral data between control group and treated group, with the best discriminant accuracy of 96.7% and 90.0% for CNE2 and C666-1 group treated with paclitaxel, respectively. This exploratory work demonstrated that LTRS technology combined with multivariate statistical analysis has promising potential to be a novel analytical strategy at the single-cell level for the evaluation of NPC-related chemotherapeutic drugs.

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Section: Discussionmentioning

confidence: 99%

Study on the chemodrug-induced effect in nasopharyngeal carcinoma cells using laser tweezer Raman spectroscopy

Qiu¹,

Li²,

Li³

et al. 2020

Biomed. Opt. Express

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“…Support vector machine (SVM) is a machine learning method dealing with classification and regression data efficiently based on the implementation of Vapnik's structural risk minimisation principle (Zheng et al ., 2020). The method aims to explore a hyperplane that can segregate the dataset into a discrete predefined number of classes (Xu et al ., 2019).…”

Section: Methodsmentioning

confidence: 99%

Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion

Ren

Liu

Ning

et al. 2020

Int J of Food Sci Tech

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Summary Food fraud causes significant economic losses for the industry and generates distrust between the consumers and traders. Tea is one of the most valued beverages throughout the world, being vulnerable to economically motivated cheat. The objective of the study was to develop the potential of hyperspectral imaging (HSI) allying multivariate analysis and data fusion to identify the authenticity of Keemun black tea quality categories. Data fusion that integrated of texture characteristics based on grey level co‐occurrence matrix and visible and near‐infrared spectral features via competitive adaptive reweighted sampling (CARS) was as the target data for modelling. Support vector machine (SVM) and random forest (RF) were utilised for the classification of tea samples of seven grades. The RF model using fused data gave the best performance with the correct discriminant rate of 92.70% for the prediction set. This study demonstrated that HSI coupled with RF was effective in identifying tea sample rank.

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“…After removing duplicate studies and screening for titles and abstracts, we screened 42 studies that met the inclusion criteria for full-text reading. Finally, there were 12 studies (1,(3)(4)(5)(16)(17)(18)(19)(20)(21)(22)(23) included in the meta-analysis. PRISMA diagram for the study selection is disclosed in Figure 1.…”

Section: Literature Search and Selection Of Studiesmentioning

confidence: 99%

“…The general characteristics of the included studies were presented in the Table 1. Ten studies (1,3,5,16,17,(19)(20)(21)(22)(23) were published in English and two studies (4,18) were Chinese. A total of 960 samples were tested in all the studies and 430 patients were included without McGregor's study (17).…”

Section: Characteristics Quality Assessment and Publication Bias Of The Included Studiesmentioning

confidence: 99%

The accuracy of Raman spectroscopy in the diagnosis of lung cancer: a systematic review and meta-analysis

Chen¹,

Hao²,

Hao³

et al. 2021

Transl Cancer Res TCR

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Background: To investigate the overall performance of Raman Spectroscopy (RS) in the diagnosis of lung cancer.Methods: We systematic searched databases including PubMed, EMBASE, CNKI and Web of science for studies up to May 2020 with no start date limited. Then we extracted data of true positives, true negatives, false positives and false negatives from the included studies to calculate the pooled sensitivity, specificity, accuracy, positive and negative likelihood ratios (LRs), and diagnostic odds ratio (DOR), with 95% confidence intervals to evaluate the diagnostic value of Raman spectroscopy. We plotted the summary receiver operator characteristics (SROC) and the area under the curve (AUC) to evaluate the overall performance of Raman spectroscopy. Quality assessments and Publication bias were evaluated by QUADAS-2 checklist and Stata software version 12.0.Results: Totally, 12 studies were included in our meta-analysis. The pooled diagnostic sensitivity and specificity of Raman Spectroscopy in lung cancer were 0.90 (95% CI, 0.87-0.92, P<0.05) and 0.76 (95% CI, 0.72-0.79, P<0.05). The pooled PLR and NLR were 5.87 (95% CI, 3.45-9.97) and 0.14 (95% CI, 0.10-0.22) respectively. And the AUC of SROC curve was 0.9453. Discussion: Raman spectroscopy had excellent accuracy with high sensitivity and considerable specificity in the diagnosis of lung cancer.

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Raman spectroscopy as a potential diagnostic tool to analyse biochemical alterations in lung cancer

Abstract: Raman spectroscopy can be used as a rapid diagnosis tool in lung cancer to help us understand cancer progression at molecular level and improve clinical practices.

Cited by 35 publications

References 36 publications

Study on the chemodrug-induced effect in nasopharyngeal carcinoma cells using laser tweezer Raman spectroscopy

Study on the chemodrug-induced effect in nasopharyngeal carcinoma cells using laser tweezer Raman spectroscopy

Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion

The accuracy of Raman spectroscopy in the diagnosis of lung cancer: a systematic review and meta-analysis

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