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