Analysing the effect of change in mammography screening sequences

In the AIforScreening project we have tested different ways of utilizing time sequence information in mammography screening, including published methods and home-grown ones. The simplest ones include regression modelling, where we apply a single-image breast cancer risk model at a sequence of images of a given breast and use linear regression to onstruct a modified score. This method givs a very modest improvement of the order 0.001 on the AUC scale, which was statistically significant only for the inferior holistic model. The more advanced methods try include co-registration of the current and previous images and various ways of merging the model’s features to produce improved risk scores, utilizing various so-called Siamese net models. Over-all, the results were negative, as none of the advanced methods gave improvements above the linear model. This is contrary to published results, and we speculate that this may be due to the fact that our model has a high performance to begin with, leaving less room for improvement. The linear model places positive weight on the previous risk scores, which go against the intuition that an increase in risk score over time should increase the likelihood of cancer. Apparently, the ’direct effect’ that an elevated risk score is associated with future cancer is stronger.