Cardiovascular phenotype-genotype analysis within a CT based lung cancer sreening trial

Objectives

In this project we aim to find clues from magnetic resonance images (MRI) of the normal breast to predict whether these women are likely to develop a non-dormant cancer. Whether a cancer should be considered “dormant” will be established by analyzing the genes inside the cancer tissue that these women developed. The effects of abnormal gene regulations on the biology of the cancers will be analysed and compared with the MRI. In the future we envision that one pre-screening MRI will result in individualized screening programs, tailored to the individual risk of the women, rather than the one-size-fits-all screening programs that are currently employed.

Recent results

We have investigated whether subjects undergoing lung cancer screening who are at risk of a cardiovascular event (CVE) can be automatically identified based on image and subject characteristics. The results demonstrate that these subjects can be identified using automatic image analysis. Combining subject information with image characteristics only slightly improved the performance

Publications

1. B.D. de Vos, P.A. de Jong, J.M. Wolterink, R. Vliegenthart, G.V.F. Wielingen, M.A. Viergever, I. Isgum. Automatic machine learning based prediction of cardiovascular events in lung cancer screening data. In: SPIE Medical Imaging (in press), 2015