Cancer risk analysis in families with hereditary nonpolyposis colorectal cancer

被引:4
|
作者
Kokuer, Munevver [1 ]
Naguib, Raouf N. G.
Jancovic, Peter
Younghusband, H. Banfield
Green, Roger C.
机构
[1] Coventry Univ, Fac Engn & Comp, BIOCORE, Coventry CV1 5FB, W Midlands, England
[2] Carleton Univ, Ottawa, ON K1S 5B6, Canada
[3] De La Belle Univ, Manila, Philippines
[4] Univ Birmingham, Sch Elect Elect & Comp Engn, Birmingham B15 2TT, W Midlands, England
[5] Mem Univ Newfoundland, Fac Med, St John, NF A1C 5S7, Canada
关键词
artificial neural networks (ANNs); cancer risk assessment; hereditary nonpolyposis colorectal cancer (HNPCC); logistic regression (LR); pedigree analysis; principal component analysis (PCA); self-organizing maps (SOM);
D O I
10.1109/TITB.2006.872054
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Colorectal cancer (CRC) is one of the most common fatal cancers in developed countries and represents a significant public-health issue. About 3%-5% of patients with CRC have hereditary nonpolyposis colorectal cancer (HNPCC). Cancer morbidity and mortality can be reduced if early and intensive screening is pursued. However, despite advances in screening, population-wide genetic screening for HNPCC is not currently considered feasible due to its complexity and expense. If the risk of a family having HNPCC can be identified/assessed, then only the high-risk fraction of the population would undergo intensive screening. This identification is currently performed by a genetic counselor/physician who makes the decision based on some predefined criteria. Here, we report on a system to identify. the risk of a family having HNPCC based on its history. We compare artificial neural networks and statistical approaches for assessing the risk of a family having HNPCC and discuss the experimental results obtained by these two approaches.
引用
收藏
页码:581 / 587
页数:7
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