Research overview
Research in the Mieczkowski.Lab focuses on the development of computational methods for medical diagnostics and monitoring based on minimally invasive data.
Across projects, we integrate molecular profiling, medical imaging and clinical information to better characterize disease processes, physiological stress, and treatment response. A central goal of the lab is to translate complex biomedical data into tools that can support clinically meaningful decisions.
Our research is conducted within a broader clinical, and institutional ecosystem
Minimally invasive molecular diagnostics
A major research direction of the lab is the development of minimally invasive diagnostic and monitoring strategies based on blood- and urine-derived molecular data. We work with circulating DNA and other molecular profiles to infer tissue status, disease dynamics and treatment response without the need for invasive procedures.
Our research combines experimental workflows with advanced computational analysis to detect subtle molecular changes and to enable longitudinal monitoring over time. Particular emphasis is placed on reproducibility, robustness, and clinical interpretability of derived biomarkers.
These approaches are primarily applied in oncology and longitudinal health monitoring in demanding clinical and operational settings, where repeated, low-burden sampling is essential for early detection of disease progression, residual disease, or treatment-related effects.
Radiation and extreme exposure research
At Mieczkowski.Lab, we investigate molecular and health-related responses to ionizing radiation and other extreme stressors that impact human physiology. This research aims to identify biological signatures that reflect exposure, dose, and long-term health risk. We combine controlled experimental models with data-driven analysis of molecular and epigenetic profiles to study both immediate and persistent effects of radiation and environmental stress.
The work addresses questions relevant to clinical medicine, radiation protection and health monitoring in high-risk or extreme environments.
This research line supports the development of biological indicators that may complement physical dosimetry and contribute to improved risk assessment and medical decision-making after exposure.
Medical imaging and data integration
A complementary research area focuses on computational analysis of medical imaging data, including CT, MRI, PET, and other clinically relevant modalities. We develop methods to extract quantitative imaging features that are not accessible through standard visual assessment.
Imaging-derived information is integrated with molecular profiles and clinical data to achieve a more comprehensive characterization of disease processes.
This multimodal approach enables improved diagnostic stratification, monitoring of treatment response, and development of predictive models relevant to clinical practice.
Close collaboration with clinical teams allows these methods to be developed and evaluated in real-world medical settings.
