Patient-Centered Decision-Making for Management of Small Renal Tumors

NIH RePORTER · NIH · K07 · $180,576 · view on reporter.nih.gov ↗

Abstract

 DESCRIPTION (provided by applicant): 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 treatmen...

Key facts

NIH application ID
9970424
Project number
5K07CA197134-05
Recipient
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
Principal Investigator
Stella Kang
Activity code
K07
Funding institute
NIH
Fiscal year
2020
Award amount
$180,576
Award type
5
Project period
2016-08-01 → 2022-07-31