Label-free breast cancer detection using fiber probe-based Raman spectrochemical biomarker-dominated profiles extracted from a mixture analysis algorithm
| Autor: | Soogeun Kim, Jae-Ho Shin, Samjin Choi, Ayoung Bang, Jeong Yoon Song, Wansun Kim |
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| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
General Chemical Engineering Breast Neoplasms 02 engineering and technology Spectrum Analysis Raman 01 natural sciences Least squares Analytical Chemistry 010309 optics symbols.namesake Breast cancer 0103 physical sciences medicine Humans Label free Principal Component Analysis Chemistry General Engineering 021001 nanoscience & nanotechnology medicine.disease Linear discriminant analysis Principal component analysis symbols Biomarker (medicine) Female 0210 nano-technology Raman spectroscopy Algorithm Algorithms Biomarkers |
| Zdroj: | Analytical methods : advancing methods and applications. 13(29) |
| ISSN: | 1759-9679 |
| Popis: | We report the development of a label-free, simple, and high efficiency breast cancer detection platform with multimodal biomarker analytic algorithms on a portable 785 nm Raman setup with an endoscopic Raman-lensed fiber optic probe. We propose a multimodal biomarker extraction algorithm (PCMA) implemented by combining a multivariate statistics principal component analysis (PCA) algorithm and a multivariate curve resolution-alternating least squares (MCR-ALS) computational model for extraction of the biomarker information hidden in Raman spectrochemical data. We show that the six Raman spectrochemical peaks at 1009, 1270, 1305/1443, 1658, and 1750 cm-1 assigned to phenylalanine, amide III in proteins, CH2 deformation in lipids, amide I in proteins, and carbonyl, respectively, can be used as a biomarker for breast cancer diagnosis using the biomarker-dominated PCMA spectrochemical spectra of breast tissues. From 20 human breast tissues, the PCMA-linear discriminant analysis (PCMA-LDA) identification method achieved high classification performance with a sensitivity and specificity >99% along with an improvement of approximately 4.5% compared to the performance without the PCMA mixture analysis algorithm. Our label-free breast cancer detection method has the potential for clinical application to diagnose breast cancer in real-time during surgery. |
| Databáze: | OpenAIRE |
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