Nomograms for everyone

June 19, 2008

The University of Texas Southwestern Medical Center has come up with a mathematical modeling system that can predict the probability of a patient with renal cancer being cancer-free 12 years after initial surgical treatment. This nomogram considers sex, the presence or absence of symptoms, radiological data, and the size of the mass.

The University of Texas Southwestern Medical Center has come up with a mathematical modeling system that can predict the probability of a patient with renal cancer being cancer-free 12 years after initial surgical treatment. This nomogram considers sex, the presence or absence of symptoms, radiological data, and the size of the mass.

For example, an asymptomatic woman with a 3-cm renal mass has a 96% chance of being free of cancer 12 years after surgery alone. A man the same age, however, who complains of flank pain and is diagnosed with a 4-cm mass and enlarged lymph nodes has less than a 40% chance.

The difference in these two cases is the incidental finding of cancer using CT.

Historically, renal cancer has had a notoriously poor prognosis, according to Dr. Ganesh Raj, a developer of the nomogram. But that has changed. With the widening use of CT and other imaging techniques, more and more cases are being diagnosed incidentally.

"A patient will have a CT scan to evaluate unrelated symptoms and be told he or she has a mass in the kidney," he said.

Raj and colleagues designed the nomogram for use in counseling sessions following diagnosis so that patients could have a clearer understanding of their likely outcomes with surgery. But the concept can be applied beyond just renal cancer and patient counseling.

The key factor in the nomogram is the incidental finding of renal cancer, typically through the unrelated use of CT. The underlying point, however, is that modern imaging provides early diagnosis. The idea behind mammography is the diagnosis of asymptomatic women. In fact, all imaging modalities are geared toward the earliest possible diagnosis of patients, although symptoms often bring these patients to their physicians.

The National Cancer Institute and myriad individual researchers have been gathering data about cancer survival for decades. If mathematical models similar to the nomogram could be developed for types of cancer other than renal, they could be applied retrospectively to historical data to determine their validity as well as the impact of medical imaging on patient healthcare.

It is hard to imagine a better focus for vendor-funded research studies. The extension of the nomogram beyond renal cancer could help many more patients and physicians as well as the imaging community as a whole.