I think AdamO's answer is great, but I think it's also worth emphasizing out that this adaptive sample size design is how many (maybe even most? I've done theoretical work during internships at pharm companies, but can't say I've ever planned a real study...) clinical trials are run.
That is to say, if a sequential design is used, initial patients are recruited and treated. Part way through the study, the currently collected data gets analyzed. Three possible actions can occur at this point: the data may show a statistically significant result and the study will be stopped because efficacy has been demonstrated, the data many statistically significantly show that there is no strong effect (for example, the upper end of the confidence interval is below some clinically significant threshold) and the study is stopped due to futility or the data is not yet conclusive (i.e. both a clinically significant effect and a clinically insignificant effect are contained in the confidence interval) in which more data will be collected. So you can see that in this case, the sample size is not fixed.
An important note about this: you can't just run a standard test each time you "check" your data, otherwise you are doing multiple comparisons! Because the test statistics at different times should be positively correlated, it's not as big an issue as standard multiple comparison issues, but it still should be addressed for proper inference. Clinical trials, being regulated by the FDA, must state a plan for how they will address this (as @AdamO points out, SeqTrial provides software for this). However, often times academic researchers, not being regulated by the FDA, will continue to collect data until they find significance without adjusting for the fact that they are doing several comparisons. It's not the biggest abuse of statistical practice in research, but it still is an abuse.