With the ageing society, there is an urgent need to develop new preventive and new therapeutic strategies for common age-related diseases. Tailored prevention requires an understanding of the earliest stages of disease in combination with the identification of persons who are at risk. Tailored therapy relies on accurate assessment of diagnostic signs and a subsequent precision of the individual prognosis. As most age-related diseases are caused by a complex interplay between genetic predisposition, life style, and the environment, all these factors need to be taken into account in a combined approach.
Population studies that collect imaging and genetic data provide a unique opportunity here. They have the potential to trace back to the earliest visual signs of disease at the point in time when they differentiate from normal variations. Population studies also enable the identification of people at risk. And, they enable the precise differentiation of diagnosis into individualized prognosis. In short, population imaging studies enable us to see disease develop before our eyes. Genetic, environmental, and lifestyle data provide the census to discriminate the abnormal from the variations of the normal. This new way of medical research will have a considerable impact on the practice of medicine at large.
The goal of ImaGene is threefold: to develop and to validate tools which
1) detect disease as early and as precise as possible
2) identify persons benefiting from preventive strategies or therapeutic intervention
3)enable the tailoring of treatment to the individual patient
To achieve these goals, novel technology is required. The sheer size, large variability, and heterogeneity of
data acquired within population studies imply that the current practices of semi-supervised visual exploration
do not apply here. The following major innovations in technology are targeted in this program:
1) new medical image processing technology to identify the first visible signs of disease
2) automated unknown pattern search correlated with later signs in large, heterogeneous data sets
3) integrated imaging and genetic variational analysis for cause-consequence analysis
The ImaGene programme unites the efforts of experts in a multitude of fields to reach its goals. It requires expertise in medical image analysis (for the automated analysis of the human body), computer science experts (for large scale processing and searching without prior hypotheses), genetics (for genotyping and genetic analyses), epidemiology (to measure environmental and lifestyle factors in a population-based setting), bioinformatics (to record correlation with signs of the normal and the abnormal), and industry (for valorization). It connects to a worldwide unique dataset of population studies opening the door to improve the quality of life of many people, and by early intervention to reduce the burden on the healthcare system with its urgently needed economic and societal impacts.
Project 1: Search for the yet unknown, projectleader: Prof.dr.ir. A.W.M. Smeulders (UvA / CWI), with participants: UvA, CWI, Euvision
Project 2: Visual Analysis in Population Imaging Research, projectleader: Dr. C.P. Botha (TU Delft / LUMC), with participants: TU Delft EWI, LUMC Image Processing / Radiology, Erasmus MC, Medis, Synerscope, Treparel
Project 3: Genes in Space, projectleader: Prof.dr.ir. B.P.F. Lelieveldt (LUMC / TU Delft), with participants: TU Delft EWI, LUMC Image Processing / Radiology / Molecular Genetics, Erasmus MC, Percuros, Skyline Diagnostics, Treparel
Project 4: Advanced diffusion tensor MRI based phenotyping for imaging genetic studies, projectleader: Dr. F.M. Vos (TU Delft / AMC), with participants: Erasmus MC Epidemiology, TU Delft TNW, Erasmus MC BIGR, Biotronics 3D, Philips Healthcare
Project 5: Imaging Genetics to unravel the causes of Alzheimer’s Disease, projectleader: Dr. M.A. Ikram (Erasmus MC), with participants: Erasmus MC Epidemiology, Erasmus MC BIGR, Erasmus MC Radiology, GE Healthcare, IBM, Quantib
Project 6: High-field (7T) MR Imaging genetics for early prediction of neurocognitive impairment in diabetes, projectleader: Prof.dr. E. Formisano (Maastricht University Medical Center), with participants: UMCU Image Sciences, UMCU Radiology, NKI Medical Oncology, NKI Bioinformatics, Guerbet, Philips Research
Project 7: Computer-aided risk assessment of breast cancer using gene-correlated dynamic contrast-enhanced MRI, project leader: Dr. K.G.A. Gilhuijs (UMC Utrecht), with participants: UMCU Image Sciences Institute, Radiology, NKI Medical Oncology, NKI Bioinformatics, Guerbet, Philips Research
Project 8: Cardiovascular phenotype-genotype analysis within a CT based lung cancer screening trial, projectleader: Dr. I. Išgum (UMC Utrecht), with participants: UMCU Image Sciences Institute, UMCU Radiology, UMCU Medical Genetics, Medis, PIE Medical Imaging, 3mensio Medical Imaging