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

2018 ◽  
Vol 95 (3) ◽  
pp. 302-308 ◽  
Author(s):  
Haifeng Wei ◽  
Linyan Xie ◽  
Qiao Liu ◽  
Changshun Shao ◽  
Ximing Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Light Scattering ◽  
Small Cell ◽  
Label Free ◽  
Small Cell Lung ◽  
Free Cells ◽  
Poorly Differentiated ◽  
Static Cytometry

Machine-learning-based classification of the histological subtype of non-small-cell lung cancer using MRI texture analysis

2021 ◽  
Vol 66 ◽  
pp. 102446
Author(s):  
Ewelina Bębas ◽  
Marta Borowska ◽  
Marcin Derlatka ◽  
Edward Oczeretko ◽  
Marcin Hładuński ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Texture Analysis ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Histological Subtype ◽  
Small Cell Lung

scholarly journals Quantum and classical machine learning for the classification of non-small-cell lung cancer patients

2020 ◽  
Vol 2 (6) ◽  
Author(s):  
Siddhant Jain ◽  
Jalal Ziauddin ◽  
Paul Leonchyk ◽  
Shashibushan Yenkanchi ◽  
Joseph Geraci
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cancer Patients ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancer Patients

scholarly journals Label-Free Detection of Human Serum Using Surface-Enhanced Raman Spectroscopy Based on Highly Branched Gold Nanoparticle Substrates for Discrimination of Non-Small Cell Lung Cancer

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Xiaowei Cao ◽  
Zhenyu Wang ◽  
Liyan Bi ◽  
Jie Zheng
Keyword(s):  
Lung Cancer ◽  
Raman Spectroscopy ◽  
Cell Lung Cancer ◽  
Surface Enhanced Raman Spectroscopy ◽  
Small Cell ◽  
Label Free ◽  
Small Cell Lung ◽  
Surface Enhanced ◽  
Different Types ◽  
Surface Enhanced Raman

Surface-enhanced Raman spectroscopy (SERS) is a good candidate for the development of fast and easy-to-use diagnostic tools, possibly used on serum in screening tests. In this study, a potential label-free serum test based on SERS spectroscopy was developed to analyze human serum for the diagnosis of the non-small cell lung cancer (NSCLC). We firstly synthesized novel highly branched gold nanoparticles (HGNPs) at high yield through a one-step reduction of HAuCl4 with dopamine hydrochloride at 60°C. Then, HGNP substrates with good reproducibility, uniformity, and high SERS effect were fabricated by the electrostatically assisted (3-aminopropyl) triethoxysilane-(APTES-) functionalized silicon wafer surface-sedimentary self-assembly method. Using as-prepared HGNP substrates as a high-performance sensing platform, SERS spectral data of serum obtained from healthy subjects, lung adenocarcinoma patients, lung squamous carcinoma patients, and large cell lung cancer patients were collected. The difference spectra among different types of NSCLC were compared, and analysis result revealed their intrinsic difference in types and contents of nucleic acids, proteins, carbohydrates, amino acids, and lipids. SERS spectra were analyzed by principal component analysis (PCA), which was able to distinguish different types of NSCLC. Considering its time efficiency, being label-free, and sensitivity, SERS based on HGNP substrates is very promising for mass screening NSCLC and plays an important role in the detection and prevention of other diseases.

scholarly journals Classification of Non-Small Cell Lung Cancer Based on Copy Number Alterations

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88300 ◽  
Author(s):  
Bi-Qing Li ◽  
Jin You ◽  
Tao Huang ◽  
Yu-Dong Cai
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Copy Number ◽  
Small Cell ◽  
Copy Number Alterations ◽  
Small Cell Lung

Prognostic factors for survival in advanced non-small-cell lung cancer: univariate and multivariate analyses including recursive partitioning and amalgamation algorithms in 1,052 patients. The European Lung Cancer Working Party.

1995 ◽  
Vol 13 (5) ◽  
pp. 1221-1230 ◽  
Author(s):  
M Paesmans ◽  
J P Sculier ◽  
P Libert ◽  
G Bureau ◽  
G Dabouis ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prognostic Factors ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Cox Regression ◽  
Recursive Partitioning ◽  
Working Party ◽  
Small Cell ◽  
Small Cell Lung

PURPOSE This study attempted to determine the prognostic value for survival of various pretreatment characteristics in patients with nonresectable non-small-cell lung cancer in the context of more than 10 years of experience of a European Cooperative Group. PATIENTS AND METHODS We included in the analysis all eligible patients (N = 1,052) with advanced non-small-cell lung cancer registered onto one of seven trials conducted by the European Lung Cancer Working Party (ELCWP) during one decade. The patients were treated by chemotherapy regimens based on platinum derivatives. We prospectively collected 23 variables and analyzed them by univariate and multivariate methods. RESULTS The global estimated median survival time was 29 weeks, with a 95% confidence interval of 27 to 30 weeks. After univariate analysis, we applied two multivariate statistical techniques. In a Cox regression model, the selected explanatory variables were disease extent, Karnofsky performance status, WBC and neutrophil counts, metastatic involvement of skin, serum calcium level, age, and sex. These results were confirmed by application of recursive partitioning and amalgamation algorithms (RECPAM), which led to classification of the patients into four homogeneous subgroups. CONCLUSION We confirmed by our analysis the role of well-known independent prognostic factors for survival, but also identified the effect of the neutrophil count, rarely studied, with the use of two methods: a classical Cox regression model and a RECPAM analysis. The classification of patients into the four subgroups we obtained needs to be validated in other series.

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