Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 581-587 |
| Number of pages | 7 |
| Journal | IEEE Transactions on Information Technology in Biomedicine |
| Volume | 10 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Jul 2006 |
Keywords
- principal component analysis (PCA)
- pedigree analysis
- logistic regression (LR)
- cancer risk assessment
- hereditary nonpolyposis colorectal cancer (HNPCC)
- artificial neural networks (ANNs)
- self-organizing maps (SOM)