Interpreting PET Scans by Structured Patient Data: A Data Mining Case Study in Dementia Research

Abstract

One of the goals of medical research in the area of dementia is to correlate images of the brain with other variables, for instance, demographic information or outcomes of clinical tests. The usual approach is to select a subset of patients based on such variables and analyze the images associated with those patients. In this paper, we apply data mining techniques to take the opposite approach: We start with the images and explain the differences and commonalities in terms of the other variables. In the first step, we cluster PET scans of patients to form groups sharing similar features in brain metabolism. To the best of our knowledge, it is the first time ever that clustering is applied to whole PET scans. In the second step, we explain the clusters by relating them to non-image variables. To do so, we employ RSD, an algorithm for relational subgroup discovery, with the cluster membership of patients as target variable. Our results enable interesting interpretations of differences in brain metabolism in terms of demographic and clinical variables. The approach was implemented and tested on an exceptionally large pre-existing data collection of patients with different types of dementia. It comprises 10 GB of image data from 454 PET scans, and 42 variables from psychological and demographical data organized in 11 relations of a relational database. We believe that explaining medical images in terms of other variables (patient records, demographic information, etc.) is a challenging new and rewarding area for data mining research.