Individualized Survival and Treatment Response Predictions in Breast Cancer Patients: Involvements of Phospho-EGFR and Phospho-Her2/Neu Proteins
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2008/01/01
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Description:Our robust prediction system for individual breast cancer patients combines three well-known machinelearning classifiers to provide stable and accurate clinical outcome prediction (N=269). The average performance of the selected classifiers is used as the evaluation criterion in breast cancer outcome predictions. A profile (incorporating histology, lymph node status, tumor grade, tumor stage, ER, PR, Her2/neu, patient's age and smoking status) generated over 95% accuracy in individualized disease-free survival and treatment response predictions. Furthermore, our analysis demonstrated that the measurement of phospho-EGFR and phospho-Her2/neu is more powerful in breast cancer survival prediction than that of total EGFR and total Her2/neu (p < 0.05). The incorporation of hormone receptor status, Her2/neu, patient's age and smoking status into the traditional pathologic markers creates a powerful standard to perform individualized survival and treatment outcome predictions for breast cancer patients. [Description provided by NIOSH]
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ISSN:1874-1894
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Pages in Document:18-31
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Volume:2
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NIOSHTIC Number:nn:20034063
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Citation:Open Clin Cancer J 2008 Jan; 2:18-31
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Contact Point Address:Yong Qian, Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia 26505
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Email:yaq2@cdc.gov
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Federal Fiscal Year:2008
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Peer Reviewed:True
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Source Full Name:The Open Clinical Cancer Journal
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Main Document Checksum:urn:sha-512:c1d47f569ed56286f4b3b364e4291525b141d11a73e063b367cfdf43ea71b129ee0b13c9b324c36dd66197286a1adb7ba53583e17acb35e3e8454016758029b6
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