Stokes shift spectroscopy and machine learning for label-free human prostate cancer detection

The Stokes shift spectra (S3) of human cancerous and normal prostate tissues were collected label free at a selected wavelength interval of 40 nm to investigate the efficacy of the approach based on three key molecules-tryptophan, collagen, and reduced nicotinamide adenine dinucleotide (NADH)-as can...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Optics letters Ročník 48; číslo 4; s. 936
Hlavní autori: Pu, Yang, Wu, Binlin, Mo, Haiding, Alfano, Robert R
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 15.02.2023
Predmet:
Algorithms
Collagen - chemistry
Humans
Male
Prostate - chemistry
Prostatic Neoplasms - diagnosis
Spectrometry, Fluorescence
Support Vector Machine
ISSN:1539-4794, 1539-4794
On-line prístup:Zistit podrobnosti o prístupe
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The Stokes shift spectra (S3) of human cancerous and normal prostate tissues were collected label free at a selected wavelength interval of 40 nm to investigate the efficacy of the approach based on three key molecules-tryptophan, collagen, and reduced nicotinamide adenine dinucleotide (NADH)-as cancer biomarkers. S3 combines both fluorescence and absorption spectra in one scan. The S3 spectra were analyzed using machine learning (ML) algorithms, including principal component analysis (PCA), nonnegative matrix factorization (NMF), and support vector machines (SVMs). The components retrieved from the S3 spectra were considered principal biomarkers. The differences in the weights of the components between the two types of tissues were found to be significant. Sensitivity, specificity, and accuracy were calculated to evaluate the performance of SVM classification. This research demonstrates that S3 spectroscopy is effective for detecting the changes in the relative concentrations of the endogenous fluorophores in tissues due to the development of cancer label free.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1539-4794
1539-4794
DOI:10.1364/OL.483076