Surgery is one of the oldest treatments for cancer. Even as research and technology have progressed, surgery has remained an excellent choice of treatment.
Kidney cancer, whose attention we are called to especially during the month of March, is often treated with a surgical process called a partial nephrectomy. A partial nephrectomy means that the tumor is removed whilst sparing the kidney. A partial nephrectomy is a complex surgery that can only be done at an early stage. As most cases of kidney cancer are identified at early stages, the surgical procedure is the most common form of treatment. Unfortunately, some patients find that a nephrectomy may severely threaten their health.
Individuals diagnosed with heart disease, kidney disease or other comorbidities may not be viable patients for the surgical procedure. Such diseases could greatly negatively impact the life expectancy of a patient who undergoes a nephrectomy. Being that small kidney tumors may be benign, patients that are not suitable candidates may avoid surgery by undergoing active surveillance. Active surveillance allows medical professionals to monitor the tumor. Periodic CT scans make it possible for medical professionals to track the growth of the tumor and delay surgery for at-risk patients.
While a worthwhile effort, active surveillance is vastly underutilized. This is due, in part, to the lack of guidelines illustrating when to act if surgery is in fact needed. Medical professionals are often not equipped with proper decision-making tools when it comes to weighing the risk of comorbidities against surgery.
At the beginning of this year, however, researchers developed a model that may bring more value to the decision-making process. New York University’s Stella Kang, M.D., and her teammates there, Memorial Sloan Kettering Cancer Center and Massachusetts General Hospital identified the need to effectively weigh the risks. They developed computer-based simulations to measure the impact of various methods of treatment for kidney cancer patients.
Each of the simulations involved a patient with a small kidney tumor. The simulations accounted for variables such as severity of kidney disease and other comorbidities. After one million simulations, Dr. Kang and her research team discovered that partial nephrectomies were rarely the most effective way to extend the life expectancy in patients with chronic kidney disease. Personalized strategies, including active surveillance, extended life expectancy by more than two years when compared with surgery.
The results from Dr. Kang’s study may greatly impact the decision-making process in clinical settings. Simulations using the patient’s data could aid in identifying the correct treatment for an individual. While the simulation does not suggest one specific course of action, it does allow doctors and patients to examine the risks of treatment options. In addition to benefiting individuals battling kidney cancer, these results could impact additional clinical areas. It is a great demonstration of the clinical significance in simulations. Simulations are not limited in time and expense unlike most clinical trial studies. They also offer individualized outcomes which cannot be provided by studies.