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Personalized Treatment May Extend Life Expectancy for Patients With Chronic Kidney Disease and Small Renal Tumors

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Key Points

  • Based on 1 million simulations, partial nephrectomy yielded the longest life expectancy in patients of all ages with normal renal function.
  • However, in patients with chronic kidney disease, personalized strategies like active surveillance extended life expectancy over routine nephrectomy.
  • In several simulated subgroups with moderate chronic kidney disease, personalized treatment decisions extended life expectancy by more than 2 years compared with a standard surgical approach.

Personalized treatment plans may extend life expectancy for patients with early-stage kidney cancer who also have risk factors for worsening kidney disease, according to a new study published by Kang et al in Radiology.

Kidney tumors are often discovered at an early stage and are frequently treated with partial nephrectomy. However, some patients, including those with chronic kidney disease, are poor candidates for surgery.

“There may be clear-cut risks with an operation in these patients,” said study lead author Stella K. Kang, MD, MS, Assistant Professor of Radiology and Population Health at NYU Langone Health. “Patients may have significant heart disease, other comorbidities, or a limited life expectancy for some other reason.”

In addition, many small renal tumors are either benign or very slow-growing malignancies, making active surveillance of the tumor—typically through periodic computed tomography scans to watch for changes in tumor size—a viable option in patients who are poor candidates for surgery.

Despite its potential, active surveillance may be underutilized due to a lack of consensus guidelines and decision-support tools, according to Dr. Kang.

“We need a better way of weighing the risks so that more patients can be considered for nonsurgical management,” she said.

Computer-based simulations represent a promising tool in this process of risk stratification.

Study Methods and Findings

For the study, Dr. Kang and colleagues constructed a simulation to assess the impact of different treatment approaches in patients with small renal tumors. The model accounted for important variables like the severity of the kidney disease and competing risks of mortality.

Based on 1 million simulations, partial nephrectomy yielded the longest life expectancy in patients of all ages with normal renal function. However, in patients with chronic kidney disease, personalized strategies like active surveillance extended life expectancy over routine nephrectomy.

In several simulated subgroups with moderate chronic kidney disease, personalized treatment decisions extended life expectancy by more than 2 years compared with a standard surgical approach.

The model also found that the use of magnetic resonance imaging to predict papillary renal cell could potentially improve long-term health outcomes by steering some patients toward active surveillance, and that the effectiveness of biopsy may be improved by incorporating tumor subtype information into the decision-making process.

The researchers hope the model will bring more value to the clinical decision-making process. Information from the model could provide guidance on the benefits and risks of active surveillance and help identify the right treatments for the right patients, according to Dr. Kang.

“There is probably a larger proportion of patients with small renal tumors who merit a fuller discussion of options than is currently recognized,” she said. “While the model does not prescribe one specific treatment, it does provide a set of estimates so that patients and providers can get more information on the viable options.”

“Computer disease models can be useful for studying diseases and potential interventions and guiding prospective studies, helping us understand where the research is needed the most,” Dr. Kang concluded.

Disclosure: The study authors’ full disclosures can be found at pubs.rsna.org.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.


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