Finding interesting patterns in a cancer registry data can provide oncologists and medical experts with a new perspective to improve healthcare for cancer patients. In this paper, we apply a supervised local pattern mining called Exceptional Model Mining. Its aim is to find subgroups in the data that somehow behave differently from the norm. This behaviour is captured by a model and the interestingness of a subgroup is assessed according to a quality measure. In particular, we develop a logistic regression model and propose a few different quality measures based on statistical tests and probability distributions. Additionally, we provide a statistical test, the permutation test, to assess the interestingness of a subgroup from a statistical point of view.