Research team

Expertise

Radiology expert, dedicated to chest imaging. Clinical and research focus is imaging of thoracic oncology: lung cancer, early lung cancer and pulmonary nodules, lung cancer screening, mediastinal and pleural tumors. Expertise and interest in clinical application and validation of AI algorithms, mainly in the broad field of chest imaging.

Alliance for multidimensional and multidisciplinary neuroscience (µNEURO). 01/01/2026 - 31/12/2031

Abstract

Owing to their high spatiotemporal resolution and non-invasive nature, (bio)medical imaging technologies have become key to understanding the complex structure and function of the nervous system in health and disease. Recognizing this unique potential, μNEURO has assembled the expertise of eight complementary research teams from three different faculties, capitalizing on advanced neuro-imaging tools across scales and model systems to accelerate high-impact fundamental and clinical neuro-research. Building on the multidisciplinary collaboration that has been successfully established since its inception (2020-2025), μNEURO (2026-2031) now intends to integrate and consolidate the synergy between its members to become an international focal point for true multidimensional neuroscience. Technologically, we envision enriching spatiotemporally resolved multimodal imaging datasets (advanced microscopy, MRI, PET, SPECT, CT) with functional read-outs (fMRI, EEG, MEG, electrophysiology, behaviour and clinical evaluation) and a molecular context (e.g., fluid biomarkers, genetic models, spatial omics) to achieve unprecedented insight into the nervous system and mechanisms of disease. Biologically, μNEURO spans a variety of neurological disorders including neurodegeneration, movement disorders, spinal cord and traumatic brain injury, glioblastoma and peripheral neuropathies, which are investigated in a variety of complementary model systems ranging from healthy control and patient-derived organoids and assembloids to fruit flies, rodents, and humans. With close collaboration between fundamental and preclinical research teams, method developers, and clinical departments at the University Hospital Antwerp (UZA), μNEURO effectively encompasses a fully translational platform for bench-to-bedside research. Now that we have intensified the interaction, in the next phase, μNEURO intends to formalize the integration by securing additional large-scale international research projects, by promoting the interaction between its members and core facilities and by fuelling high-risk-high-gain research within the hub and beyond. This way, μNEURO will foster breakthroughs for the neuroscience community. In addition, by focusing on technological and biological innovations that will streamline the translational pipeline for discovery and validation of novel biomarkers and therapeutic compounds, μNEURO aims to generate a long-term societal impact on the growing burden of rare and common diseases of the nervous system, connecting to key research priorities of the University of Antwerp, Belgium, and Europe.

Researcher(s)

Research team(s)

Project type(s)

  • Research Project

Targeting high risk (ex-)smokers in Flanders (BE) for lung cancer screenng with low-dose CT-scan. 01/10/2022 - 01/10/2026

Abstract

Lung cancer is the leading cause of cancer death, worldwide and in Belgium. Lung cancer screening (LCS) with low-dose Computed Tomography (LDCT) has been shown to reduce lung cancer specific mortality up to 26% in a high-risk population of current and former smokers. Implementation of LDCT LCS is hence in progress in several European countries, including Belgium. Recruitment of the target group, which is not defined by age and sex but by risk of developing lung cancer, is different than other cancer screening programmes and faces several challenges and barriers to overcome. Our study will A/Prospectively investigate the accrual of eligible high-risk participants in 5 different cohorts of at-risk Flemish citizens: 1/approach by their general practitioner, 2/occupational physician, 3/tobaccologist, 4/a letter joined to their next coming invitation for breast or colon cancer screening and 5/approach of hard-to-reach socio-economic minorities through the Centers for Respiratory Health (Flemish Society of Respiratory Health and Tuberculosis-VRGT) B/Gain insight into the views of relevant stakeholders regarding best practices, barriers, and opportunities for successful implementation of a future lung cancer screening programme in Flanders.

Researcher(s)

Research team(s)

Project type(s)

  • Research Project

Optimization of low dose CT for lung cancer screening: finding the best balance between radiation risk and performance in terms of image quality and success rate of computer aided detection.. 01/01/2022 - 31/12/2023

Abstract

Lung cancer causes a large mortality, worldwide and in Flanders, with 3822 cases in 2016. The only technique with proven mortality reduction is lung cancer screening (LKS) with CT scans. In 2020, a mortality reduction of 26% (men) and 41% (women) was measured in the Dutch-Belgian "NELSON" study. The Flemish 'Task Force Lung Cancer Screening' therefore brings together experts to design and subsequently support an LKS program for population groups where this improvement can be continued. All (expected) questions from the responsible Flemish government must be answered. With this project, radiologists and physicists from the Task Force want to find adequate answers to the following questions: what is the radiation risk associated with the CT scans? Can imaging be optimized for even lower X-ray doses? How are computer algorithms for detecting and measuring cancers performing? The greatest challenge is to develop new test methods in a world with rapidly changing technology - e.g. artificial intelligence. Three university groups will combine their expertise with (1) risk assessment, (2) evaluation of clinical image quality and (3) computer-aided detection and characterization of cancer. All new techniques and test criteria will be bundled in a unique test protocol to assess and optimize the quality of each CT protocol or computer algorithm for LKS.

Researcher(s)

Research team(s)

Project type(s)

  • Research Project