Keywords
Biochemical markers, Lungs -- Cancer -- Mathematical models, Lungs -- Cancer -- Patients, Neural networks (Computer science), Regression analysis
Abstract
We attempted a mathematical model for expected prognosis of lung cancer patients based on a multivariate analysis of the values of ER-interacting proteins (ERbeta) and a membrane bound, glycosylated phosphoprotein MUC1), and patients clinical data recorded at the time of initial surgery. We demonstrate that, even with the limited sample size available to use, combination of clinical and biochemical data (in particular, associated with ERbeta and MUC1) allows to predict survival of lung cancer patients with about 80% accuracy while prediction on the basis of clinical data only gives about 70% accuracy. The present work can be viewed as a pilot study on the subject: since results confirm that ER-interacting proteins indeed influence lung cancer patients’ survival, more data is currently being collected.
Notes
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Graduation Date
2011
Semester
Fall
Advisor
Pensky, Marianna
Degree
Master of Science (M.S.)
College
College of Sciences
Department
Mathematics
Degree Program
Mathematical Science
Format
application/pdf
Identifier
CFE0004134
URL
http://purl.fcla.edu/fcla/etd/CFE0004134
Language
English
Release Date
December 2011
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
Subjects
Dissertations, Academic -- Sciences, Sciences -- Dissertations, Academic
STARS Citation
Martinenko, Evgeny, "Prediction Of Survival Of Early Stages Lung Cancer Patients Based On Er Beta Cellular Expressions And Epidemiological Data" (2011). Electronic Theses and Dissertations. 1767.
https://stars.library.ucf.edu/etd/1767