Andra V. Krauze, M.D., DABR
- Center for Cancer Research
- National Cancer Institute
- Building 10, Room B2-3637
- Bethesda, MD 20892
- 240-760-6205
- 301-480-5591
- andra.krauze@nih.gov
RESEARCH SUMMARY
I am a radiation oncologist focused on harnessing computational analysis and artificial intelligence (AI) through the aggregation of de-siloed, large-scale oncologic data to generate adaptive, interpretable frameworks and algorithms for clinical data and biospecimen linkage and annotation. The goal of my research is to identify clinically actionable biomarkers that incorporate imaging and non-invasively obtained omic data to understand critical pathways driving tumor resistance and identify potentially clinically actionable biomarkers. The ultimate goal is to personalize management and patient outcomes by utilizing longitudinal proteomic, metabolomic, imaging, and radiation therapy dosimetry data.
Areas of Expertise
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Andra V. Krauze, M.D., DABR
Research
1. Using advanced imaging to analyze central nervous system malignancies to inform personalized radiation planning: a pilot study
We plan to use MRI, functional imaging and novel, emerging imaging methods currently employed in malignant central nervous system tumors to: 1) investigate relationship of radiation therapy dose/fractionation, treatment volumes and patterns of failure as well as normal tissue changes, and 2) help identify secondary surrogates with transferable roles in neurocognitive assessment and late complications of radiation therapy.
2. Advancement of a radiogenomic and multiparametric diffusion-weighted MRI model for high-grade glioma
Our goal is to develop and advance machine learning algorithms to assist diagnosis and to investigate biological behavior, treatment response, patterns of failure and biological optimization of radiation therapy targeting. The focus will be to identify radiographic “signatures” for tumor progression in patients with gliomas.
3. Automated outcomes analysis using a clinical data pipeline towards eventual AI driven personalized oncologic management using spatial and non-spatial data for central nervous system and other malignancies
Based on a proof of concept pipeline generated in previous work, ongoing efforts will aim to incorporate spatial and radiation dosimetry data and more sophisticated statistical tool sets into existing and emerging work flows.
4. Molecular characterization and management of patients with a diagnosis of glioma – generation of prognostic algorithms to optimize management and outcomes and to examine ongoing patterns of practice
This involves examination of areas of controversy in the clinic and exploration of differences of availability and timely access to molecular characterization nationally and internationally. To acquire a better understanding of current practices and how they are captured in large-scale data sets, current research involves ongoing data acquisition to identify patterns observed in North America and internationally.
5. Re-irradiation as treatment option for patients with recurrent high-grade glioma
Protocol ongoing: “Dose Escalation Trial of Re-irradiation in Good Prognosis Recurrent Glioblastoma”
Open to accrual as of March 2016. PI: Dr. Andra Krauze (transferred to Dr. K. Camphausen post July 2017). NCT02709226. Data has been collected and analysis is ongoing. This remains a controversial area in the approach to management in patients with recurrent glioma.
Publications
Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel
RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma
Operational Ontology for Radiation Oncology (OORO) – A Professional Society Based, Multi-Stakeholder, Consensus Driven Informatics Standard Supporting Clinical and Research use of “Real -World” Data from Patients Treated for Cancer.
Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics
Machine learning based survival prediction in Glioma using large-scale registry data
Biography
Andra V. Krauze, M.D., DABR
Dr. Krauze completed an undergraduate honors degree in biochemistry at the University of Calgary, followed by medical school and a radiation oncology residency at the University of Alberta. She then completed a clinical and research fellowship in central nervous system and genitourinary tumors at NCI and subsequently continued as staff clinician in the Radiation Oncology Branch.
At NCI, she has been involved with the Neuro-Oncology Branch, multiple multidisciplinary clinics, implementation of the high-dose rate brachytherapy program at NCI, NIH, and opening of a re-irradiation protocol for recurrent high-grade glioma. She has twice been awarded the NCI clinical educator award in radiation oncology.
In 2017, she relocated to British Columbia (BC), Canada where she held a clinical role and conducted academic research on computational approaches to spatial and non-spatial data employing BC Cancer Registry data across several tumor sites. She has considerable experience both as a clinician treating complex and rare malignancies and as an academic researcher and teacher.
In British Columbia she gathered and analyzed a wide range of large-scale data sets and developed clinical data analysis pipelines for computational analysis. She helped implement the stereotactic radiosurgery program, while advancing clinically driven research and teaching a new generation of learners at the University of British Columbia Faculty of Medicine and Dentistry.
In 2018 she was awarded a clinical research position at BC Cancer, part of Canada’s Provincial Health Services Authority, to develop an innovative multilayered research program centered on big data analysis in oncology. She received a grant from the Porte-Hungerford Neuro-Oncology Fund to develop an analysis platform for MRI in patients with central nervous system tumors.
Her ongoing research is aimed at leveraging and optimizing computational approaches towards personalizing treatment and improving outcomes for patients with cancer.
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