SUMMARY
The research of Deanna H. Pafundi, Ph.D., includes investigating new positron emission tomography tracers and advanced diffusion and perfusion magnetic resonance imaging sequencing. She uses these techniques to more accurately define the extent of tumor infiltration compared with the standard imaging practices. These techniques also noninvasively characterize the radioresistant and highly aggressive regions within a tumor. This active research area has been translated into many clinical trials across all three Mayo Clinic campuses over the last decade, focusing on brain tumors and sarcomas.
Focus areas
- Imaging biomarkers. Dr. Pafundi investigates imaging biomarkers for radiation therapy treatment planning, surgical planning and treatment response assessment. She correlates new positron emission tomography tracers and advanced magnetic resonance imaging metrics with the pathological features and immunohistochemical expressions of spatially correlated biopsy samples.
- Machine learning. Dr. Pafundi develops machine learning models and extracting radiomics features from multiple imaging modalities to predict patient outcomes and support the advancement of cancer therapy strategies through individualized medicine.
- Artificial intelligence strategies. Dr. Pafundi implements artificial intelligence strategies to automate radiation oncology treatment planning processes for improved workflow efficiencies and outcomes-based radiation planning.
Significance to patient care
Personalized medicine to improve patient outcomes is the future of cancer care. One example of personalized medicine in radiation therapy is to make treatment plans based on each patient's special tumor biology. Dr. Pafundi strives to find the most aggressive parts of the tumor, the radiation resistant areas within the tumor and see the full area of the tumor.
Use of positron emission tomography tracers and advanced magnetic resonance imaging scans that target the biological processes within a tumor allows clinicians to customize a patient's treatment plan. This treatment plan is tailored by location and the type of radiation to use such as carbon, proton and traditional high energy X-rays. Dr. Pafundi also uses quantitative details within the pictures, like a digital fingerprint, with artificial intelligence to predict how a patient may react to treatment. Therefore, the imaging may be used to not only tailor the original treatment plan but also to change a plan during treatment for better patient outcomes.
Professional highlights
- Program director, Radiation Oncology Medical Physics Residency, Mayo Clinic School of Health Sciences, Mayo Clinic in Florida, 2023-present.
- American Association of Physicists in Medicine:
- Chair, Medicine Working Group for Imaging in Treatment Planning, 2017-2022.
- Invited expert reviewer, Task group 284, magnetic resonance imaging simulation in radiotherapy: considerations for clinical implementation, optimization, and quality assurance, 2019.
- Invited expert reviewer, Task group 294, magnetic resonance biomarkers in radiation oncology, 2019.
- Invited expert reviewer, Task group 132, use of image registration and fusion algorithms and techniques in radiotherapy, 2013.
- Teacher of the Year Award, Mayo Clinic, 2019.
- Moreton Fellow, recognition of outstanding research contributions, Department of Radiation Oncology, Mayo Clinic, 2011.