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 Two‐Dimensional Light Scattering Anisotropy Cytometry for Label‐Free Classification of Ovarian Cancer Cells via Machine Learning

2019 ◽  
Vol 97 (1) ◽  
pp. 24-30
Author(s):  
Xuantao Su ◽  
Tao Yuan ◽  
Zhiwen Wang ◽  
Kun Song ◽  
Rongrong Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Light Scattering ◽  
Cancer Cells ◽  
Ovarian Cancer Cells ◽  
Two Dimensional ◽  
Label Free ◽  
Scattering Anisotropy ◽  
Free Classification

scholarly journals Label-free classification of colon cancer grading using infrared spectral histopathology

2016 ◽  
Vol 187 ◽  
pp. 105-118 ◽  
Author(s):  
C. Kuepper ◽  
F. Großerueschkamp ◽  
A. Kallenbach-Thieltges ◽  
A. Mosig ◽  
A. Tannapfel ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Label Free ◽  
Infrared Spectral ◽  
Poorly Differentiated ◽  
Well Differentiated ◽  
Free Classification ◽  
Cancer Grading ◽  
Spectral Histopathology ◽  
Good Agreement

In recent years spectral histopathology (SHP) has been established as a label-free method to identify cancer within tissue. Herein, this approach is extended. It is not only used to identify tumour tissue with a sensitivity of 94% and a specificity of 100%, but in addition the tumour grading is determined. Grading is a measure of how much the tumour cells differ from the healthy cells. The grading ranges from G1 (well-differentiated), to G2 (moderately differentiated), G3 (poorly differentiated) and in rare cases to G4 (anaplastic). The grading is prognostic and is needed for the therapeutic decision of the clinician. The presented results show good agreement between the annotation by SHP and by pathologists. A correlation matrix is presented, and the results show that SHP provides prognostic values in colon cancer, which are obtained in a label-free and automated manner. It might become an important automated diagnostic tool at the bedside in precision medicine.

scholarly journals Correction: Huang et al. Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring. Sensors 2021, 21, 512

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8360
Author(s):  
Xiwei Huang ◽  
Hyungkook Jeon ◽  
Jixuan Liu ◽  
Jiangfan Yao ◽  
Maoyu Wei ◽  
...  
Keyword(s):  
Deep Learning ◽  
Label Free ◽  
State Monitoring ◽  
Immune State ◽  
Free Classification

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