Automatic Classification of Label‐Free Cells from Small Cell Lung Cancer and Poorly Differentiated Lung Adenocarcinoma with 2D Light Scattering Static Cytometry and Machine Learning. Issue 3 (3rd December 2018)
- Record Type:
- Journal Article
- Title:
- Automatic Classification of Label‐Free Cells from Small Cell Lung Cancer and Poorly Differentiated Lung Adenocarcinoma with 2D Light Scattering Static Cytometry and Machine Learning. Issue 3 (3rd December 2018)
- Main Title:
- Automatic Classification of Label‐Free Cells from Small Cell Lung Cancer and Poorly Differentiated Lung Adenocarcinoma with 2D Light Scattering Static Cytometry and Machine Learning
- Authors:
- Wei, Haifeng
Xie, Linyan
Liu, Qiao
Shao, Changshun
Wang, Ximing
Su, Xuantao - Abstract:
- Abstract: Small cell lung cancer (SCLC) needs to be classified from poorly differentiated lung adenocarcinoma (PDLAC) for appropriate treatment of lung cancer patients. Currently, the classification is achieved by experienced clinicians, radiologists and pathologists based on subjective and qualitative analysis of imaging, cytological and immunohistochemical (IHC) features. Label‐free classification of lung cancer cell lines is developed here by using two‐dimensional (2D) light scattering static cytometric technique. Measurements of scattered light at forward scattering (FSC) and side scattering (SSC) by using conventional cytometry show that SCLC cells are overlapped with PDLAC cells. However, our 2D light scattering static cytometer reveals remarkable differences between the 2D light scattering patterns of SCLC cell lines (H209 and H69) and PDLAC cell line (SK‐LU‐1). By adopting support vector machine (SVM) classifier with leave‐one‐out cross‐validation (LOO‐CV), SCLC and PDLAC cells are automatically classified with an accuracy of 99.87%. Our label‐free 2D light scattering static cytometer may serve as a new, accurate, and easy‐to‐use method for the automatic classification of SCLC and PDLAC cells. © 2018 International Society for Advancement of Cytometry
- Is Part Of:
- Cytometry. Volume 95:Issue 3(2019)
- Journal:
- Cytometry
- Issue:
- Volume 95:Issue 3(2019)
- Issue Display:
- Volume 95, Issue 3 (2019)
- Year:
- 2019
- Volume:
- 95
- Issue:
- 3
- Issue Sort Value:
- 2019-0095-0003-0000
- Page Start:
- 302
- Page End:
- 308
- Publication Date:
- 2018-12-03
- Subjects:
- 2D light scattering -- label‐free -- static cytometry -- lung cancer -- machine learning
Flow cytometry -- Periodicals
Imaging systems in biology -- Periodicals
Imaging systems in medicine -- Periodicals
Diagnostic imaging -- Periodicals
571.605 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1552-4930 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/cyto.a.23671 ↗
- Languages:
- English
- ISSNs:
- 1552-4922
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3506.855100
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 12868.xml