Algorithm helps docs see cancer patients’ prognoses
Researchers at the Stanford University School of Medicine have developed a computer algorithm that provides more accurate prognoses for cancer patients by integrating different types of predictive data.
Researchers at the Stanford University School of Medicine have developed a computer algorithm that provides more accurate prognoses for cancer patients by integrating different types of predictive data.
The Continuous Individualized Risk Index (CIRI) is able to assess how well a cancer patient is faring at any point during their treatment by determining outcome probabilities for individual patients by leveraging risk predictors acquired over time.
“Similar to ‘win probability’ models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient’s course,” states a study published last week in the journal Cell. “Applying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models.”
In addition, the study’s authors say they “demonstrate CIRI’s broader utility in analogous models of chronic lymphocytic leukemia and breast adenocarcinoma and perform a proof-of-concept analysis demonstrating how CIRI could be used to develop predictive biomarkers for therapy selection.”
Also See: Deep learning uses CT images to predict cancer treatment response
“Our standard methods of predicting prognoses in these patients are not that accurate,” says instructor of medicine David Kurtz, MD, an author of the study. “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.”
CIRI integrates different types of predictive data, including a tumor’s response to treatment and the amount of cancer DNA circulating in a patient’s blood during therapy.
“What I didn't expect was that aggregating all this information through time may also be predictive,” says senior author Ash Alizadeh, MD, a Stanford Health Care oncologist and associate professor of medicine. “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 Continuous Individualized Risk Index (CIRI) is able to assess how well a cancer patient is faring at any point during their treatment by determining outcome probabilities for individual patients by leveraging risk predictors acquired over time.
“Similar to ‘win probability’ models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient’s course,” states a study published last week in the journal Cell. “Applying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models.”
In addition, the study’s authors say they “demonstrate CIRI’s broader utility in analogous models of chronic lymphocytic leukemia and breast adenocarcinoma and perform a proof-of-concept analysis demonstrating how CIRI could be used to develop predictive biomarkers for therapy selection.”
Also See: Deep learning uses CT images to predict cancer treatment response“Our standard methods of predicting prognoses in these patients are not that accurate,” says instructor of medicine David Kurtz, MD, an author of the study. “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.”
CIRI integrates different types of predictive data, including a tumor’s response to treatment and the amount of cancer DNA circulating in a patient’s blood during therapy.
“What I didn't expect was that aggregating all this information through time may also be predictive,” says senior author Ash Alizadeh, MD, a Stanford Health Care oncologist and associate professor of medicine. “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.”
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