Endometrial cancer diagnosis based on predictive computer models within an International Endometrial Tumour Analysis (IETA) collaboration

From 01-12-2013 to 31-05-2017

Description

Endometrial cancer is diagnosed in around 1450 Belgian patients yearly, according to statistics from http://www.kankerregister.be . Nonetheless, no non-invasive technique currently exists for diagnosing this disease. The downside of this is two-fold: on the one hand, healthy women undergo needless invasive diagnostic techniques, while on the other hand, some endometrial cancer patients are diagnosed at a late stage of their disease, with worse prognosis as a result. The aim of this proposal is to develop diagnostic models that, using data obtained in a non-invasive way, allow to diagnose endometrial cancer. These models will be obtained by applying machine-learning techniques to data from the International Endometrial Tumour Analysis (IETA) consortium. This is a large, multi-centre consortium of experts in gynaecological ultrasound and gynaecological oncology studying endometrial cancer. To this end, they have set up a number of prospective studies, that have enrolled over 2000 patients from 20 centres since 2011, using the centrally managed Clinical Data Miner (CDM) web interface for data collection. Diagnostic models will be generated both for symptomatic women and non-symptomatic, post-menopausal women. The first is the main project goal, and can be used in women with abnormal uterine bleeding, while the second is this project's stretch goal, and will be usable as a screening test. A temporal validation study will verify the obtained diagnostic models, and the final models will be published in relevant peer-reviewed journals. Additionally, since the generated models will be computationally complex, the models will be integrated into CDM to allow clinicians to obtain a diagnostic assessment for their patients in a user-friendly way, hiding model complexities. This modified, web-based CDM interface will be made available to the gynaecology community. The potential benefit to Flemish women's health is two-fold. On the one hand, the diagnostic model for symptomatic women will allow to avoid surgery in healthy patients, with reduced risk of morbidity and impaired fertility as a result. On the other hand, if this project's stretch goal is reached, it may allow earlier detection of endometrial cancer through a screening test, potentially saving lives.

Team

• Bart De Moor, Co-promoter
• Dirk Timmerman, Promoter (External)
• Arnaud Installé, Team member
• Inge Thijs, Team member

Financing

Funding: IWT - Agentschap voor Innovatie door Wetenschap en Technologie 

Program/Grant Type: IWT TBM - VLAIO TBM Projects Applied Biomedical Research with a Primary Social finality