The goal of this NIDDK Mentored Research Scientist Development Award is to provide an organized scientific and educational environment for Dr. Timothy Kline to begin his transition into an independent research career focused on developing novel imaging technologies and image analysis techniques for abdominal organ pathologies. This proposal outlines a five-year training plan at Mayo Clinic under the primary mentorship of Dr. Bradley Erickson and a Mentoring Team comprised of accomplished researchers in the fields of: biology, nephrology, genetics, radiology, informatics; medical physics, biostatistics, image processing, and physiology. The focus of this proposal is to improve both research studies and disease prognosis for autosomal dominant polycystic kidney disease (ADPKD) patients through biomedical imaging techniques. It is well understood that imaging is essential for ADPKD diagnosis, monitoring, and outcome prediction. Clinical studies utilize total kidney volume (TKV) (as measured by MRI as an image-based biomarker) to follow the progression of ADPKD, as larger TKVs have been shown to correlate with worse prognosis in both human and animal-model studies. However, there are challenges with using TKV as a marker of disease progression. For one, it is a simplification of the disease state and does not inform on microscopic disease processes that are involved with piecemeal destruction of healthy renal tissue. In addition, measurements of TKVs are time consuming, costly, and poorly standardized. The introduction of automated approaches for measuring TKV will: greatly improve measurement throughput, significantly reduce costs associated with performing research studies, allow accurate and reproducible measurements to be obtained both within and across institutions; facilitate the search for new imaging biomarkers.
The specific aims of this project are to: (i) develop and validate automated tools to characterize renal structure, such as TKV and cystic burden; (ii) explore new imaging biomarkers by image texture feature analysis and pattern recognition techniques; and (iii) develop a new technique to measure renal blood flow. This research will be facilitated by Mayo Clinic's outstanding clinical and research environment dedicated to improving patient care, as well as the Mayo Clinic Translational PKD Center, which focuses on translating basic science research into improvements in the management and treatment of ADPKD patients. Dr. Kline's background in imaging technologies and image processing makes him particularly suited to perform this research. In addition to the above aims, Dr. Kline will: 1) develop a strong knowledge base in both nephrology and radiology by attending relevant rounds, seminars, and national conferences; 2) enhance his knowledge of medical imaging, biology, physiology, genetics, and programming through coursework and mentoring; 3) attend workshops focused on grant and publication writing; and 4) submit a highly competitive R01 application expanding upon the findings from this research proposal. This proposal will lead to vast improvements to current analysis workflows, as well as an improved understanding of the prognostic power of new imaging biomarkers of ADPKD. Obtaining this K Award will greatly facilitate Dr. Kline's transition into a prosperous independent research career.
Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common monogenic disorders and is a leading cause of end-stage renal disease. Total kidney volume (TKV) has become the main image-based biomarker for following ADPKD progression. However, there are challenges with using TKV as a marker of disease progression. For one, it is a simplification of the disease state and does not inform on microscopic disease processes that are involved with piecemeal destruction of healthy renal tissue. In addition, measurements of TKVs are time consuming and costly. This project will develop automated tools to increase measurement throughput, and explore new image-based biomarkers that will significantly add to the assessment of patient prognosis, and will have the ability to more quickly judge the effectiveness of interventions.
