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Prediction of survival of early stages lung cancer patients based on ER beta cellular expressions and epidemiological data

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Date Issued:
2011
Abstract/Description:
We attempted a mathematical model for expected prognosis of lung cancer patients based ona multivariate analysis of the values of ER-interacting proteins (ERbeta) and a membranebound, glycosylated phosphoprotein MUC1), and patients clinical data recorded at the timeof 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 andMUC1) allows to predict survival of lung cancer patients with about 80% accuracy whileprediction on the basis of clinical data only gives about 70% accuracy. The present work canbe viewed as a pilot study on the subject: since results conrm that ER-interacting proteinsindeed inuence lung cancer patients' survival, more data is currently being collected.
Title: Prediction of survival of early stages lung cancer patients based on ER beta cellular expressions and epidemiological data.
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Name(s): Martinenko, Evgeny, Author
Shivamoggi, Bhimsen, Committee Chair
Chow, Lee, Committee Member
Peale, Robert, Committee Member
Brandenburg, John, Committee Member
, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2011
Publisher: University of Central Florida
Language(s): English
Abstract/Description: We attempted a mathematical model for expected prognosis of lung cancer patients based ona multivariate analysis of the values of ER-interacting proteins (ERbeta) and a membranebound, glycosylated phosphoprotein MUC1), and patients clinical data recorded at the timeof 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 andMUC1) allows to predict survival of lung cancer patients with about 80% accuracy whileprediction on the basis of clinical data only gives about 70% accuracy. The present work canbe viewed as a pilot study on the subject: since results conrm that ER-interacting proteinsindeed inuence lung cancer patients' survival, more data is currently being collected.
Identifier: CFE0004134 (IID), ucf:49120 (fedora)
Note(s): 2011-12-01
M.S.
Sciences, Mathematics
Masters
This record was generated from author submitted information.
Subject(s): regression -- survival -- neural networks
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0004134
Restrictions on Access: public 2011-12-15
Host Institution: UCF

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