ArticleHead & neck oncology2009
Raman spectroscopy and advanced mathematical modelling in the discrimination of human thyroid cell lines.
Article in Head & neck oncology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Raman Spectroscopy as a Potential Adjunct of Thyroid Nodule Evaluation: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Current concepts and future of noninvasive procedures for diagnosing oral squamous cell carcinoma--a systematic review.Head & face medicine · 2015Pooled it
- Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states.Scientific reports · 2025Article
- The Expressions of Keratins and P63 in Primary Squamous Cell Carcinoma of the Thyroid Gland: An Application of Raman Spectroscopy.OncoTargets and therapy · 2020Article
- Filter-Membrane-Based Ultrafiltration Coupled with Surface-Enhanced Raman Spectroscopy for Potential Differentiation of Benign and Malignant Thyroid Tumors from Blood Plasma.International journal of nanomedicine · 2020Article
- Hyperspectral Raman microscopy can accurately differentiate single cells of different human thyroid nodules.Biomedical optics express · 2019Article
- Label-free detection of hydrogen peroxide-induced oxidative stress in human retinal pigment epithelium cells via laser tweezers Raman spectroscopy.Biomedical optics express · 2019Article
- Raman spectroscopy for accurately characterizing biomolecular changes in androgen-independent prostate cancer cells.Journal of biophotonics · 2018Article
- Proof-of-concept Raman spectroscopy study aimed to differentiate thyroid follicular patterned lesions.Scientific reports · 2017Article
- Raman spectral post-processing for oral tissue discrimination - a step for an automatized diagnostic system.Biomedical optics express · 2017Article
- In situ cellular level Raman spectroscopy of the thyroid.Biomedical optics express · 2017Article
- Quantitative ratiometric discrimination between noncancerous and cancerous prostate cells based on neuropilin-1 overexpression.Proceedings of the National Academy of Sciences of the United States of America · 2011Article
- The future of medical diagnostics: review paper.Head & neck oncology · 2011Article
- Raman spectroscopy in head and neck cancer.Head & neck oncology · 2010Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Raman spectroscopy could offer non-invasive, rapid and an objective nature to cancer diagnostics. However, much work in this field has focused on resolving differences between cancerous and non-cancerous tissues, and lacks the reproducibility and interpretation to be put into clinical practice. Much work is needed on basic cellular differences between malignancy and normal. This would allow the establishment of a clinically relevant cellular based model to translate to tissue classification. Raman spectroscopy provides a very detailed biochemical analysis of the target material and to 'unlock' this potential requires sophisticated mathematical modelling such as neural networks as an adjunct to data interpretation. Commercially obtained cancerous and non-cancerous cells, cultured in the laboratory were used in Raman spectral measurements. Data trends were visualised through PCA and then subjected to neural network analysis based on self-organising maps; consisting of m maps, where m is the number of classes to be recognised. Each map approximates the statistical distribution of a given class. The neural network analysis provided a 95% accuracy for identification of the cancerous cell line and 92% accuracy for normal cell line. In this preliminary study we have demonstrated th ability to distinguish between "normal" and cancerous commercial cell lines. This encourages future work to establish the reasons underpinning these spectral differences and to move forward to more complex systems involving tissues. We have also shown that the use of sophisticated mathematical modelling allows a high degree of discrimination of 'raw' spectral data.
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