Clinically localised prostate cancer is microsatellite stable.

Objectives: To determine the frequency of microsatellite instability (MSI) change with mono-, di- and tetranucleotide markers in clinically localized prostate cancer, and to correlate those markers with clinical and pathological variables.

Materials And Methods: Two forms of MSI have been described in human cancer: MSI typical of hereditary nonpolyposis colon cancer, defined with mono- and dinucleotide repeat MS; and a second variety of MSI is best seen at selective tetranucleotide repeats, i.e.

Promoter hypermethylation is associated with tumor location, stage, and subsequent progression in transitional cell carcinoma.

Purpose: Transitional cell carcinoma (TCC) is a pan-urothelial disease characterized by multiplicity. Although little is known about the molecular events in upper-tract TCC, similar carcinogenic mechanisms are thought to occur throughout the urinary tract. However, we have previously shown that distinct patterns of microsatellite instability occur in upper and lower urinary tract TCC, suggesting biologic differences between these tumors.

Distinct patterns of microsatellite instability are seen in tumours of the urinary tract.

To date, two forms of microsatellite instability (MSI) have been described in human cancer. MSI typical of hereditary nonpolyposis colon cancer (HNPCC), is due to deficient DNA mismatch repair (MMR) and is defined with mono- and dinucleotide repeat microsatellites. A second variety of instability is best seen at selective tetranucleotide repeats (EMAST; elevated microsatellite alterations at select tetranucleotides).

Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.

Purpose: New techniques for the prediction of tumor behavior are needed, because statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. Whereas artificial neural networks (ANN), the best-studied form of AI, have been used successfully, its hidden networks remain an obstacle to its acceptance.