The current treatment paradigm for small renal tumors (? 4 cm) has resulted in worsened overall survival despite earlier detection and aggressive treatment with surgical resection. This lack of improved outcomes may be due to the indolence of most renal tumors and harms of surgery in this generally elderly population. Although the majority of these small renal masses are malignant, a small minority of tumors metastasize and approximately 20% are benign. Less aggressive treatment alternatives must be more widely adopted into decision-making to prevent unnecessary surgeries in patients with indolent or benign tumors, or risk factors for poor post-surgical outcomes. As an abdominal radiologist and health outcomes researcher, I have obtained my MD and am pursuing a Master of Science concentrated in comparative effectiveness research methods. I am PI on a radiology outcomes research grant to evaluate recently developed functional magnetic resonance imaging (MRI) techniques to characterize renal masses and aid in treatment selection. The Departments of Population Health and Radiology within New York University Medical Center offer the mentoring and resources to position me for successful health services research in renal mass management as an independent investigator. A team of experts in medical decision-making, decision aids, radiology, oncology and urology will guide my project and career development. I will complete coursework in advanced decision-analytic modeling, decision aid development, and survey research to build the skillset required for my project, as well as my career. In the proposed work, I will summarize performance characteristics of diagnostic imaging tests for renal tumor characterization. I will construct a decision-analytic model to assess downstream oncologic and post- treatment outcomes of tumor imaging features, incorporating patient comorbidities (e.g. chronic kidney disease) that may impact long-term survival. Tested treatment strategies include the current standard of care partial nephrectomy, as well as less invasive percutaneous ablation and watchful waiting. I will then embed the decision model in an interactive decision aid to improve patients' knowledge of small renal tumors, communicate personalized harms/benefits, and elicit patient preferences in treatment selection. I hypothesize that 1) MRI will perform with higher specificity than CT for detection of benign renal tumors and indolent malignancies; 2) incorporation of small renal tumor histology and anatomy, and patient comorbidities (including renal function) in treatment selection improves life expectancy compared with standard management; and 3) watchful waiting and ablation are non-inferior strategies for survival compared with the current standard of surgery in most patients who are not currently offered these strategies. These contributions will provide a timely and novel tool to determine the most effective treatment and increase patient-centered decision-making for management of small renal tumors.

Public Health Relevance

Kidney tumors are most often diagnosed as early stage, incidental lesions on imaging tests performed for unrelated reasons. The vast majority of patients undergo surgical resection, although metastasis rarely occurs during watchful waiting, high rates of underlying chronic kidney disease may worsen surgical outcomes, and overall survival for early stage renal cancer has not improved despite aggressive treatment. Therefore, we will determine the optimal management strategies for patients with small kidney tumors, and create tools to communicate personalized harms and benefits of treatment options and promote shared decision-making.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Academic/Teacher Award (ATA) (K07)
Project #
5K07CA197134-03
Application #
9522029
Study Section
Subcommittee I - Transistion to Independence (NCI)
Program Officer
Perkins, Susan N
Project Start
2016-08-01
Project End
2021-07-31
Budget Start
2018-08-01
Budget End
2019-07-31
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
New York University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
121911077
City
New York
State
NY
Country
United States
Zip Code
10016
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Kang, Stella K; Jiang, Miao; Duszak Jr, Richard et al. (2018) Use of Breast Cancer Screening and Its Association with Later Use of Preventive Services among Medicare Beneficiaries. Radiology 288:660-668
Kang, Stella K; Heacock, Laura; Doshi, Ankur M et al. (2017) Comparative performance of non-contrast MRI with HASTE vs. contrast-enhanced MRI/3D-MRCP for possible choledocholithiasis in hospitalized patients. Abdom Radiol (NY) 42:1650-1658