Playbook For Cancer Patients

Playbook for cancer patients; Global soccer competitions and hotly contested political primaries, bookies and pundits are scouring every evolving scrap of information and sifting through mountains of data in an effort to predict the outcome of the next game or election. These predictions can change on a dime, however, based on a player’s poor pass or a candidate’s stellar debate performance.

Playbook for cancer patients

Now researchers at the Stanford University School of Medicine; which have take a page from this playbook to generate more accurate prognoses for cancer patients. They’ve done so by designing a computer algorithm; so that can integrate many different types of predictive data including a tumor’s response; hence to treatment and the amount of cancer DNA circulating in a patient’s blood during therapy to generate a single; so dynamic risk assessment at any point in time during a patient’s course of treatment. Such an advance could be deeply meaningful for patients and their doctors.

“When they care for our patients, they are walking on eggshells for a profound period of time; hence while they try to determine whether the cancer is truly gone, or if it is likely to return,” said associate professor of medicine Ash Alizadeh, MD, Ph.D. “And patients are wondering ‘Should be planning to attend my child’s wedding next summer, or should I prioritize making my will?’ They are trying to come up with a better way; so to predict at any point during a patient’s course of treatment what their outcome is likely to be.”

“What we’re doing now is somewhat like trying to predict; so the outcome of a basketball game by tuning in at halftime to check the score, or by watching only the tipoff,” Diehn said, “when in reality they know that there are any number of things; so that could have happen during the first half that we aren’t taking into account. They want to learn if it’s best to look at the latest information available about a patient, the earliest information they gathered; or whether it’s best to aggregate all of this data over many time points.”

Prognostic indicators

“Our standard methods of predicting prognoses in these patients are not that accurate,” Kurtz said. “Using standard baseline variables it becomes almost a crystal ball exercise. If a perfectly accurate test has a score of 1, and a test that assigns patients randomly to one of two groups has a score of 0.5 essentially a coin toss our current methods score at about 0.6. But CIRI’s score was around 0.8. Not perfect, but markedly better than we’ve done in the past.”

The researchers next test CIRI’s performance on data from previously publish panels; hence of people with a common leukemia and another on breast cancer patients. Although the prognostic indicators vary for each disease, they found that, by serially integrating the predictive information over time, CIRI outperform the standard methods. Furthermore, it suggest that it might be useful to identify patients who might need more aggressive intervention within one or two rounds of treatment; hence rather than waiting to see if the disease recurs.

“What I didn’t expect was that aggregating all this information through time may also be predictive,” Alizadeh said. “It might tell us ‘you’re going down the wrong path with this therapy, and this other therapy might be better.’ Now we have a mathematical model that might help us identify subsets of patients who are unlikely to do well with standard treatments.” The researchers are next planning to test CIRI’s predictive capabilities in people recently diagnosed with aggressive lymphoma.