Grant Details
Grant Number: |
5R01CA284057-02 Interpret this number |
Primary Investigator: |
Lobo, Jennifer |
Organization: |
University Of Virginia |
Project Title: |
Optimizing Treatment Decision Making for Patients with Localized Renal Mass |
Fiscal Year: |
2024 |
Abstract
Project Summary
Kidney cancer, or renal cell carcinoma (RCC), is one of the 10 most common cancers in
the US. In 2022, over 79,000 people will be diagnosed with RCC. Localized renal masses (LRM,
tumors confined to the kidney) make up two-thirds of all RCC. Patients found to have a LRM face
many choices on what to do, especially since it is not uncommon for these LRM to be benign.
There are currently four ways to manage LRM – active surveillance (watching the mass but not
treating it), thermal ablation (heating the mass by putting a needle in it from the outside), partial
nephrectomy (surgery to cut the mass out but leave the rest of the kidney in place), and radical
nephrectomy (surgery to remove the entire kidney). Patients and doctors are often confused as
to which option to pursue since there have been few previous studies to guide them. It is currently
recommended that the kidney be preserved in patients who may have future problems with their
kidney function. However, it is hard to identify these patients, especially when they have other
health issues. Current recommendations are based on “expert opinion” and it is typically left up
to the doctor to decide what is best for the patient in terms of risk to the kidney and whether the
cancer threatens the life of the patient over other medical conditions the patient may have.
The primary goal of our study is to identify specific management plans that can be
individualized for a patient with a LRM. This will make it easier for the doctor and patient to decide
on the best way to manage the mass. By using a tool built with real-world information, the “trade-
off” with each option (like kidney function) will be clearer to the patient, thereby helping them make
the best decision. We will create an internet-based database shared between three different major
academic hospitals. We will collect information about the health of the patient, how good their
kidney function is, and the details of the LRM. We will assess how often biopsies are done prior
to treatment of the LRM and what treatments patients tend to pursue. We will develop a set of
rules for management of a LRM using a Markov decision process model and real-world patient
information. We will then use this model to identify treatment decisions that are in line with patient-
driven goals, including what is best for their quality of life, and approaches that minimize health
care cost. Our work will enable patients and their doctors to participate in a shared-decision
making process. We will create a web-based tool and assess the feasibility of community
urologists helping with gathering patient information and collaboration to optimize patient
management (i.e., community versus academic setting).
Publications
Cost-Effectiveness Analysis of the Clear Cell Likelihood Score Against Renal Mass Biopsy for Evaluating Small Renal Masses.
Authors: Chen K.Y.
, Lange M.J.
, Qiu J.X.
, Lambert D.
, Mithqal A.
, Krupski T.L.
, Schenkman N.S.
, Lobo J.M.
.
Source: Urology, 2024 Jun; 188, p. 111-117.
EPub date: 2024-04-20.
PMID: 38648945
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