ArticlePhotomedicine and laser surgery2012
Discrimination of basal cell carcinoma and melanoma from normal skin biopsies in vitro through Raman spectroscopy and principal component analysis.
Article in Photomedicine and laser surgery, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.
- Pooled it
- Raman Spectroscopic Signatures of Hepatic Carcinoma: Progress and Future Prospect.International journal of molecular sciences · 2026Review
- Raman spectral band imaging for the diagnostics and classification of canine and feline cutaneous tumors.The veterinary quarterly · 2025Article
- AI-assisted identification of nonmelanoma skin cancer structures based on combined line-field confocal optical coherence tomography and confocal Raman microspectroscopy.Journal of biomedical optics · 2025Article
- From Vibrations to Visions: Raman Spectroscopy's Impact on Skin Cancer Diagnostics.Journal of clinical medicine · 2023Review
- Direct imaging of intracellular RNA, DNA, and liquid-liquid phase separated membraneless organelles with Raman microspectroscopy.Communications biology · 2022Article
- Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy.Biomedicines · 2022Article
- Review
- Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity.PloS one · 2022Article
- Is Computer-Assisted Tissue Image Analysis the Future in Minimally Invasive Surgery? A Review on the Current Status of Its Applications.Journal of clinical medicine · 2021Review
- Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing.Cancers · 2021Review
- Melanins as Sustainable Resources for Advanced Biotechnological Applications.Global challenges (Hoboken, NJ) · 2021Review
- Optical clearing agent increases effectiveness of photodynamic therapy in a mouse model of cutaneous melanoma: an analysis by Raman microspectroscopy.Biomedical optics express · 2020Article
- Machine learning for composition analysis of ssDNA using chemical enhancement in SERS.Biomedical optics express · 2020Article
- Normal-subtracted preprocessing of Raman spectra aiming to discriminate skin actinic keratosis and neoplasias from benign lesions and normal skin tissues.Lasers in medical science · 2020Article
- Assessment of Raman Spectroscopy for Reducing Unnecessary Biopsies for Melanoma Screening.Molecules (Basel, Switzerland) · 2020Article
- Emerging imaging technologies in dermatology: Part II: Applications and limitations.Journal of the American Academy of Dermatology · 2019Review
- Emerging imaging technologies in dermatology: Part I: Basic principles.Journal of the American Academy of Dermatology · 2019Review
- Raman Spectroscopy and Imaging for Cancer Diagnosis.Journal of healthcare engineering · 2018Review
- Classification of cancer cell lines using matrix-assisted laser desorption/ionization time‑of‑flight mass spectrometry and statistical analysis.International journal of molecular medicine · 2017Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveRaman spectroscopy has been employed to discriminate between malignant (basal cell carcinoma [BCC] and melanoma [MEL]) and normal (N) skin tissues in vitro, aimed at developing a method for cancer diagnosis. BACKGROUND DATA: Raman spectroscopy is an analytical tool that could be used to diagnose skin cancer rapidly and noninvasively.
methodsSkin biopsy fragments of ≈ 2 mm(2) from excisional surgeries were scanned through a Raman spectrometer (830 nm excitation wavelength, 50 to 200 mW of power, and 20 sec exposure time) coupled to a fiber optic Raman probe. Principal component analysis (PCA) and Euclidean distance were employed to develop a discrimination model to classify samples according to histopathology. In this model, we used a set of 145 spectra from N (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues.
resultsWe demonstrated that principal components (PCs) 1 to 4 accounted for 95.4% of all spectral variation. These PCs have been spectrally correlated to the biochemicals present in tissues, such as proteins, lipids, and melanin. The scores of PC2 and PC3 revealed statistically significant differences among N, BCC, and MEL (ANOVA, p<0.05) and were used in the discrimination model. A total of 28 out of 30 spectra were correctly diagnosed as N, 93 out of 96 as BCC, and 13 out of 19 as MEL, with an overall accuracy of 92.4%.
conclusionsThis discrimination model based on PCA and Euclidean distance could differentiate N from malignant (BCC and MEL) with high sensitivity and specificity.
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Registered trials
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