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Grant Details

Grant Number: 1R01CA284057-01 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: 2023


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).



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