Label‐free serum detection based on Raman spectroscopy for the diagnosis and classification of glioma

2020 ◽  
Vol 51 (10) ◽  
pp. 1977-1985
Author(s):  
Chenxi Zhang ◽  
Ying Han ◽  
Bo Sun ◽  
Wenli Zhang ◽  
Shujun Liu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Label Free ◽  
Diagnosis And Classification ◽  
Free Serum ◽  
Serum Detection

scholarly journals Towards automated cancer screening: Label-free classification of fixed cell samples using wavelength modulated Raman spectroscopy

2018 ◽  
Vol 11 (4) ◽  
pp. e201700244 ◽  
Author(s):  
Lana Woolford ◽  
Mingzhou Chen ◽  
Kishan Dholakia ◽  
C. Simon Herrington
Keyword(s):  
Raman Spectroscopy ◽  
Cancer Screening ◽  
Label Free ◽  
Fixed Cell ◽  
Free Classification

scholarly journals Potential Application of Raman Spectroscopy for Real-time Diagnosis and Classification of Colorectal Cancer

2018 ◽  
Vol 30 (3) ◽  
pp. 381-389 ◽  
Author(s):  
Ryuichi SEKINE ◽  
Sumito SATO ◽  
Jun-ichi TANAKA ◽  
Hirotada KAGOSHIMA ◽  
Takeshi AOKI ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Raman Spectroscopy ◽  
Real Time ◽  
Potential Application ◽  
Diagnosis And Classification ◽  
Time Diagnosis

Label-Free Classification of a Nasopharyngeal Carcinoma Tissue Test at Different Stages Based on Raman Spectroscopy

2017 ◽  
Vol 100 (2) ◽  
pp. 429-433 ◽  
Author(s):  
Mingyu Liu ◽  
Jinyong Lin ◽  
Sufang Qiu ◽  
Weilin Wu ◽  
Gaoqiang Liu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Nasopharyngeal Carcinoma ◽  
Raman Spectra ◽  
Early Stage ◽  
Support Vector ◽  
Chemical Compositions ◽  
Label Free ◽  
Quick Method ◽  
Clinical Method

Abstract Raman spectroscopy (RS) of nasopharyngeal carcinoma (NPC) tissue provides substantial biomolecular information and various biomedicine features for tissue at different stages of cancer development. This study suggested an automatic and quick method for the classification of Raman spectra at different stages of NPC by multivariate statistical analysis. During RS measurement, Raman spectra were acquired from all NPC tissues in two groups of samples: 30 early-stage NPC patients (stages I and II) and 46 advanced-stage NPC patients (stages III and IV). In addition, a tentative diagnostic algorithm comprising principal components analysis and support vector machine was used to effectively classify multivariate data from the Raman spectra to yield sensitivities (70%; 21 of 30 samples) and specificities (91%; 42 of 46 samples) by the leave-one-out cross-validation method. Meaningful chemical compositions in the classification process were then deduced by analyzing the classified mathematical model. This beneficial work provides a great potential clinical method for the automatic classification of NPC stages and the speculation of the chemical compositions for NPC staging.

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