SU-E-J-208: Synthetic Datasets for Adaptive Radiotherapy Simulations in Lung Cancer Using Principal Component Analysis (PCA)

2013 ◽  
Vol 40 (6Part10) ◽  
pp. 199-199
Author(s):  
C Dial ◽  
G Hugo ◽  
J Siebers
Keyword(s):  
Lung Cancer ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Adaptive Radiotherapy ◽  
Synthetic Datasets

Correlation and Principal Component Analysis of Clinical, Bioelectrical and Functional Variables in Newly Diagnosed Lung Cancer Adult Patients

2021 ◽  
Author(s):  
Justa Carmen Columbié Regüeiferos ◽  
Taira Teresa Batista Luna ◽  
Soraida Cándida Acosta Brooks ◽  
Maylet Planas Rodriguez ◽  
Larisa Zamora Matamoros ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Adult Patients ◽  
Newly Diagnosed

scholarly journals Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

2019 ◽  
Author(s):  
Koki Tsuyuzaki ◽  
Hiroyuki Sato ◽  
Kenta Sato ◽  
Itoshi Nikaido
Keyword(s):  
Principal Component Analysis ◽  
Single Cell ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Rna Seq ◽  
Large Memory ◽  
Synthetic Datasets ◽  
Selection Of ◽  
Memory Efficient

AbstractPrincipal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but large-scale scRNA-seq datasets require long computational times and a large memory capacity.In this work, we review 21 fast and memory-efficient PCA implementations (10 algorithms) and evaluate their application using 4 real and 18 synthetic datasets. Our benchmarking showed that some PCA algorithms are faster, more memory efficient, and more accurate than others. In consideration of the differences in the computational environments of users and developers, we have also developed guidelines to assist with selection of appropriate PCA implementations.

Development of an optimized multi-biomarker panel for the detection of lung cancer based on principal component analysis and artificial neural network modeling

2012 ◽  
Vol 39 (12) ◽  
pp. 10851-10856 ◽  
Author(s):  
José Miguel Flores-Fernández ◽  
Enrique J. Herrera-López ◽  
Francisco Sánchez-Llamas ◽  
Antonio Rojas-Calvillo ◽  
Paula Anel Cabrera-Galeana ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Network Modeling ◽  
Principal Component ◽  
Component Analysis ◽  
Neural Network Modeling ◽  
Artificial Neural Network Modeling ◽  
Biomarker Panel

scholarly journals PO-0948 Predicting lung function post-RT in lung cancer using multivariate and principal component analysis

2019 ◽  
Vol 133 ◽  
pp. S512-S513
Author(s):  
Y. Dong ◽  
M. Tawhai ◽  
C. Veiga ◽  
T. Doel ◽  
D. Landau ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Principal Component Analysis ◽  
Lung Function ◽  
Principal Component ◽  
Component Analysis

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

Sign in / Sign up

Export Citation Format

Share Document