ArticleBMC cancer2015
Label-free blood serum detection by using surface-enhanced Raman spectroscopy and support vector machine for the preoperative diagnosis of parotid gland tumors.
Article in BMC cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Diagnostic accuracy of Raman spectroscopy in oral squamous cell carcinoma.Frontiers in oncology · 2022Pooled it
- Combined SERS and chemometric analyses of blood plasma and bone marrow towards pediatric leukemia biomarkers detection.Mikrochimica acta · 2026Article
- SERS and Machine Learning-Enabled Liquid Biopsy: A Promising Tool for Early Detection and Recurrence Prediction in Acute Leukemia.ACS omega · 2025Article
- Intraoperative rapid assessment of the deep muscle surgical margin of tongue squamous cell carcinoma via Raman spectroscopy.Frontiers in bioengineering and biotechnology · 2024Article
- Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics.Journal of Raman spectroscopy : JRS · 2022Article
- Acoustofluidic multimodal diagnostic system for Alzheimer's disease.Biosensors & bioelectronics · 2022Article
- Raman Spectroscopy: A Potential Diagnostic Tool for Oral Diseases.Frontiers in cellular and infection microbiology · 2022Review
- Identifying functioning and nonfunctioning adrenal tumors based on blood serum surface-enhanced Raman spectroscopy.Analytical and bioanalytical chemistry · 2021Article
- Comparison of Surface-Enhanced Raman Scattering Properties of Serum and Urine for the Detection of Chronic Kidney Disease in Patients.Applied spectroscopy · 2021Article
- Decoding Optical Data with Machine Learning.Laser & photonics reviews · 2021Article
- Big Data in Head and Neck Cancer.Current treatment options in oncology · 2018Review
- Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools.Biomedical optics express · 2018Article
- Nanotechnology: a promising method for oral cancer detection and diagnosis.Journal of nanobiotechnology · 2018Review
- In-vivo optical imaging in head and neck oncology: basic principles, clinical applications and future directions.International journal of oral science · 2018Review
- Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for tumor stages detection and histologic grades classification of oral squamous cell carcinoma.International journal of nanomedicine · 2018Article
- Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for the diagnosis of the oral squamous cell carcinoma.Lipids in health and disease · 2017Article
- Surface-enhanced Raman spectroscopy of serum accurately detects prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL.International journal of nanomedicine · 2017Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt is difficult for the parotid gland neoplasms to make an accurate preoperative diagnosis due to the restriction of biopsy in the parotid gland neoplasms. The aim of this study is to apply the surface-enhanced Raman spectroscopy (SERS) method for the blood serum biochemical detection and use the support vector machine for the analysis in order to develop a simple but accurate blood serum detection for preoperative diagnosis of the parotid gland neoplasms.
methodsThe blood serums were collected from four groups: the patients with pleomorphic adenoma, the patients with Warthin's tumor, the patients with mucoepidermoid carcinoma and the volunteers without parotid gland neoplasms. Au nanoparticles (Au NPs) were mixed with the blood serum as the SERS active nanosensor to enhance the Raman scattering signals produced by the various biochemical materials and high quality SERS spectrum were obtained by using the Raman microscope system. Then the support vector machine was utilized to analyze the differences of the SERS spectrum from the blood serum of different groups and established a diagnostic model to discriminate the different groups.
resultsIt was demonstrated that there were different intensities of SERS peaks assigned to various biochemical changes in the blood serum between the parotid gland tumor groups and normal control group. Compared with the SERS spectra of the normal serums, the intensities of peaks assigned to nucleic acids and proteins increased in the SERS spectra of the parotid gland tumor serums, which manifested the differences of the biochemical metabolites in the serum from the patients with parotid gland tumors. When the leave-one-sample-out method was used, support vector machine (SVM) played an outstanding performance in the classification of the SERS spectra with the high accuracy (84.1 % ~ 88.3 %), sensitivity (82.2 % ~ 97.4 %) and specificity (73.7 % ~ 86.7 %). Though the accuracy, sensitivity and specificity decreased in the leave-one-patient-out cross validation, the mucoepidermoid carcinoma was still easier to diagnose than other tumors. DISCUSSION: The specific molecular differences of parotid gland tumors and normal serums were significantly demonstrated through the comparison between the various SERS spectra.But compared with the serum SERS spectra reported in the other studies, some differences exist between the spectra in this study and the ones reported in the lietratures. These differences may result from the various nano-particles, the different preparation of serum and equipment parameters, and we could need a further research to find an exact explanation.Based on the SERS spectra of the serum samples, SVM have shown a giant potential to diagnose the parotid gland tumors in our preliminary study. However, different cross validaiton methods could effect the accuracy and a further study involing a great number of samples should be needed.
conclusionsThis exploratory research demonstrated the great potential of SERS combined with SVM into a non-invasive clinical diagnostic method for preoperative diagnosis of parotid gland tumors. And the internal relation between the spectra and patients should be established in the further study.
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