Noninvasive prostate cancer screening based on serum surface-enhanced Raman spectroscopy and support vector machine

Abstract

This study aims to present a noninvasive prostate cancer screening methods using serum surface-enhanced Raman scattering (SERS) and support vector machine (SVM) techniques through peripheral blood sample. SERS measurements are performed using serum samples from 93 prostate cancer patients and 68 healthy volunteers by silver nanoparticles. Three types of kernel functions including linear, polynomial, and Gaussian radial basis function (RBF) are employed to build SVM diagnostic models for classifying measured SERS spectra. For comparably evaluating the performance of SVM classification models, the standard multivariate statistic analysis method of principal component analysis (PCA) is also applied to classify the same datasets. The study results show that for the RBF kernel SVM diagnostic model, the diagnostic accuracy of 98.1% is acquired, which is superior to the results of 91.3% obtained from PCA methods. The receiver operating characteristic curve of diagnostic models further confirm above research results. This study demonstrates that label-free serum SERS analysis technique combined with SVM diagnostic algorithm has great potential for noninvasive prostate cancer screening.

Surface-enhanced Raman spectroscopy of serum accurately detects prostate cancer in patients with prostate-specific antigen levels of 4–10 ng/mL

“…[27][28][29] An investigation into diagnosing PCa in 2015 reported that by analyzing the Raman spectra of serum separated from a peripheral blood sample and applying SVM techniques, PCa could be diagnosed with an accuracy of up to 98.1%. 24 While this study offered extremely promising results, it focused on developing the methodology for using SERS in PCa diagnosis and thus did not study the method in a clinically relevant situation. Thus, this method would become closer to clinical application if it was tested on clinically relevant subjects.…”

“…Similar to our study, there was another research that also used surface-enhanced Raman spectroscopy (SERS) to measure serum spectra for prostate detection and showed great advantage as an invasive method. 24 This work involved normal and PCa participants with support vector machine (SVM) classification and lack of BPH discrimination.…”

“…24,36,37 The tentative assignments of serum SERS spectra bands are listed in Table 2. The cancer group contains higher concentration of amide I and C=C lipid stretch (1,655 cm −1 ) in serum, while lower concentration of glycogen (492 cm ), and guanine in DNA (1,573 cm −1 ).…”

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

“…In many machine learning algorithms, SVM belongs to the family of kernel-based classifiers, and they are very powerful classifiers, as they can perform both linear and non-linear classification simply by changing the "kernel" function utilized [23]. SVM has been widely used in the realm of EEG [24][25][26][27].…”

Surface-enhanced Raman spectroscopy + support vector machine: a new noninvasive method for prostate cancer screening?

“…PSA detection is the important screening means, but the PSA test can lead to overdiagnosis because PSA levels are low in some subjects with prostate cancer, so the US Preventive Services Task Force does not recommend screening for prostate cancer based on PSA at present. Recently, the surfaceenhanced Raman spectroscopy (SERS) in combination with support vector machine (SVM) has been successfully applied in classification of prostate cancer from normal subjects by detecting peripheral blood [2]. We feel that blood SERS will open a new approach for noninvasive screening prostate cancer at the early stages.…”