Label-free breast cancer detection using fiber probe-based Raman spectrochemical biomarker-dominated profiles extracted from a mixture analysis algorithm

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.

Automated summary

This study introduces a label-free, simple, and high-efficiency breast cancer detection platform with multimodal biomarker analytic algorithms, demonstrating the potential for real-time diagnosis of breast cancer during surgery.