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Therapeutic prognosis of prostate cancer using surface‐enhanced Raman scattering of patient urine and multivariate statistical analysis
Author(s) -
Ma Yiwei,
Chi Jingmao,
Zheng Zhaoyu,
Attygalle Athula,
Kim Isaac Yi,
Du Henry
Publication year - 2021
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.202000275
Subject(s) - raman spectroscopy , linear discriminant analysis , raman scattering , surface enhanced raman spectroscopy , prostate cancer , multivariate analysis , multivariate statistics , partial least squares regression , urine , materials science , analytical chemistry (journal) , artificial intelligence , cancer , chemistry , computer science , medicine , machine learning , chromatography , optics , physics
Surface‐enhanced Raman scattering (SERS) is highly sensitive and label‐free analytical technique based on Raman spectroscopy aided by field‐multiplying plasmonic nanostructures. We report the use of SERS measurements of patient urine in conjunction with biostatistical algorithms to assess the treatment response of prostate cancer (PCa) in 12 recurrent (Re) and 63 nonrecurrent (NRe) patient cohorts. Multiple Raman spectra are collected from each urine sample using monodisperse silver nanoparticles (AgNPs) for Raman signal enhancement. Genetic algorithms‐partial least squares‐linear discriminant analysis (GA‐PLS‐LDA) was employed to analyze the Raman spectra. Comprehensive GA‐PLS‐LDA analyses of these Raman spectral features ( p = 3.50 × 10 −16 ) yield an accuracy of 86.6%, sensitivity of 86.0%, and specificity 87.1% in differentiating the Re and NRe cohorts. Our study suggests that SERS combined with multivariate GA‐PLS‐LDA algorithm can potentially be used to detect and monitor the risk of PCa relapse and to aid with decision‐making for optimal intermediate secondary therapy to recurred patients.