[{"innerBlocks":[],"name":"core\/freeform","postId":729,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"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.

\u201cSimilar to \u2018win probability\u2019 models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient\u2019s course,\u201d states a study<\/a> published last week in the journal Cell<\/i>. \u201cApplying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models.\u201d

In addition, the study\u2019s authors say they \u201cdemonstrate CIRI\u2019s 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.\u201d

Also See<\/b>: Deep learning uses CT images to predict cancer treatment response<\/a>

\u201cOur standard methods of predicting prognoses in these patients are not that accurate,\u201d says instructor of medicine David Kurtz, MD, an author of the study. \u201cUsing 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\u2014 essentially a coin toss\u2014our current methods score at about 0.6. But CIRI\u2019s score was around 0.8. Not perfect, but markedly better than we\u2019ve done in the past.\u201d

CIRI integrates different types of predictive data, including a tumor\u2019s response to treatment and the amount of cancer DNA circulating in a patient\u2019s blood during therapy.

\u201cWhat I didn't expect was that aggregating all this information through time may also be predictive,\u201d says senior author Ash Alizadeh, MD, a Stanford Health Care oncologist and associate professor of medicine. \u201cIt might tell us \u2018you\u2019re going down the wrong path with this therapy, and this other therapy might be better.\u2019 Now we have a mathematical model that might help us identify subsets of patients who are unlikely to do well with standard treatments.\u201d","saveContent":"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.

\u201cSimilar to \u2018win probability\u2019 models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient\u2019s course,\u201d states a study<\/a> published last week in the journal Cell<\/i>. \u201cApplying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models.\u201d

In addition, the study\u2019s authors say they \u201cdemonstrate CIRI\u2019s 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.\u201d

Also See<\/b>: Deep learning uses CT images to predict cancer treatment response<\/a>

\u201cOur standard methods of predicting prognoses in these patients are not that accurate,\u201d says instructor of medicine David Kurtz, MD, an author of the study. \u201cUsing 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\u2014 essentially a coin toss\u2014our current methods score at about 0.6. But CIRI\u2019s score was around 0.8. Not perfect, but markedly better than we\u2019ve done in the past.\u201d

CIRI integrates different types of predictive data, including a tumor\u2019s response to treatment and the amount of cancer DNA circulating in a patient\u2019s blood during therapy.

\u201cWhat I didn't expect was that aggregating all this information through time may also be predictive,\u201d says senior author Ash Alizadeh, MD, a Stanford Health Care oncologist and associate professor of medicine. \u201cIt might tell us \u2018you\u2019re going down the wrong path with this therapy, and this other therapy might be better.\u2019 Now we have a mathematical model that might help us identify subsets of patients who are unlikely to do well with standard treatments.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

Algorithm helps docs see cancer patients’ prognoses

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