Label-free breast cancer detection using fiber probe-based Raman spectrochemical biomarker-dominated profiles extracted from a mixture analysis algorithm. Issue 29 (29th June 2021)

• Record Type: Journal Article
• Authors: Kim, Soogeun; Kim, Wansun; Bang, Ayoung; Song, Jeong-Yoon; Shin, Jae-Ho; Choi, Samjin
• Is Part Of: Analytical methods. Volume 13:Issue 29(2021)
• Page Start: 3249
• Page End: 3255
• Publication Date: 2021-06-29
• Subjects: Chemistry, Analytic -- Periodicals; Analytical biochemistry -- Periodicals; Chemical laboratories -- Standards -- Periodicals; 543.1905
• DOI: 10.1039/d1ay00491c
• Languages: English
• ISSNs: 1759-9660

Abstract

Abstract : PCMA-LDA breast cancer detection method based on biomarker-dominated analysis is expected to provide basal information to implement multi-modal framework platforms to directly diagnose breast cancer during surgery. Abstract : 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.