[{"innerBlocks":[],"name":"core\/freeform","postId":2362,"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 Emory and Northwestern universities have developed artificial intelligence software that can predict the survival of patients diagnosed with glioma, a deadly form of brain tumor, by analyzing digital images of tissue biopsies.

Being able to predict the course of a patient\u2019s glioma at diagnosis is vital given that it carries a bleak prognosis. Gliomas are classified as either low-grade or high-grade based on their appearance under a microscope. The problem is that microscopic examination is extremely subjective, with different pathologists often providing different interpretations.

However, researchers used deep learning to train software to learn visual patterns associated with patient survival using images of brain tumor tissue samples. What they discovered was that when the software was trained using digital images and genomic data their predictions of how long patients survive beyond diagnosis were more accurate than those of human pathologists.

\u201cOur approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes,\u201d conclude the authors in an article<\/a> published this week in Proceedings of the National Academy of Sciences<\/i>.

Also See<\/b>: Machine learning automatically identifies brain tumors<\/a>

\u201cThe way the (convolutional neural) network works is we show it the images and the patients\u2019 survival\u2014sort of as an input and output pair\u2014and by doing that repeatedly the network learns the relationships between the images and the clinical outcomes of the patients,\u201d says Lee Cooper, the study\u2019s lead author and a professor of biomedical informatics at Emory University School of Medicine.

In addition, researchers demonstrated using visualization techniques that the software learns to recognize many of the same structures and patterns in the tissues that pathologists use when conducting their examinations under a microscope and grading the brain tumors. Public data produced by the National Cancer Institute\u2019s Cancer Genome Atlas project was leveraged to develop and evaluate the algorithm.

\u201cPrognosis depends on both genomics and histology and so it\u2019s important to have a unified framework that can take all the data that comes out of the pathology department and make these predictions,\u201d adds Cooper, who is also a member of the Winship Cancer Institute. \u201cWhat we\u2019re hoping to do is to identify patients who can benefit from more aggressive therapy and also to avoid applying therapy where there will be no benefit but where there are significant side effects.\u201d

Going forward, he says researchers are planning future studies to assess whether the software can be used to improve outcomes for newly diagnosed glioma patients, with an eye towards clinical trials.

\u201cThe eventual goal is to use this software to provide doctors with more accurate and consistent information. We want to identify patients where treatment can extend life,\u201d concludes Cooper. \u201cWhat the pathologists do with a microscope is amazing. That an algorithm can learn a complex skill like this was an unexpected result. This is more evidence that AI will have a profound impact in medicine, and we may experience this sooner than expected.\u201d

Nonetheless, he acknowledges that being able to explain how algorithms work remains a critical barrier to their adoption for clinical use. \u201cThe issue is they\u2019re very complex and so they can\u2019t really be analyzed mathematically typically, so you have to use techniques like visualization to understand what they are doing.\u201d","saveContent":"Researchers at Emory and Northwestern universities have developed artificial intelligence software that can predict the survival of patients diagnosed with glioma, a deadly form of brain tumor, by analyzing digital images of tissue biopsies.

Being able to predict the course of a patient\u2019s glioma at diagnosis is vital given that it carries a bleak prognosis. Gliomas are classified as either low-grade or high-grade based on their appearance under a microscope. The problem is that microscopic examination is extremely subjective, with different pathologists often providing different interpretations.

However, researchers used deep learning to train software to learn visual patterns associated with patient survival using images of brain tumor tissue samples. What they discovered was that when the software was trained using digital images and genomic data their predictions of how long patients survive beyond diagnosis were more accurate than those of human pathologists.

\u201cOur approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes,\u201d conclude the authors in an article<\/a> published this week in Proceedings of the National Academy of Sciences<\/i>.

Also See<\/b>: Machine learning automatically identifies brain tumors<\/a>

\u201cThe way the (convolutional neural) network works is we show it the images and the patients\u2019 survival\u2014sort of as an input and output pair\u2014and by doing that repeatedly the network learns the relationships between the images and the clinical outcomes of the patients,\u201d says Lee Cooper, the study\u2019s lead author and a professor of biomedical informatics at Emory University School of Medicine.

In addition, researchers demonstrated using visualization techniques that the software learns to recognize many of the same structures and patterns in the tissues that pathologists use when conducting their examinations under a microscope and grading the brain tumors. Public data produced by the National Cancer Institute\u2019s Cancer Genome Atlas project was leveraged to develop and evaluate the algorithm.

\u201cPrognosis depends on both genomics and histology and so it\u2019s important to have a unified framework that can take all the data that comes out of the pathology department and make these predictions,\u201d adds Cooper, who is also a member of the Winship Cancer Institute. \u201cWhat we\u2019re hoping to do is to identify patients who can benefit from more aggressive therapy and also to avoid applying therapy where there will be no benefit but where there are significant side effects.\u201d

Going forward, he says researchers are planning future studies to assess whether the software can be used to improve outcomes for newly diagnosed glioma patients, with an eye towards clinical trials.

\u201cThe eventual goal is to use this software to provide doctors with more accurate and consistent information. We want to identify patients where treatment can extend life,\u201d concludes Cooper. \u201cWhat the pathologists do with a microscope is amazing. That an algorithm can learn a complex skill like this was an unexpected result. This is more evidence that AI will have a profound impact in medicine, and we may experience this sooner than expected.\u201d

Nonetheless, he acknowledges that being able to explain how algorithms work remains a critical barrier to their adoption for clinical use. \u201cThe issue is they\u2019re very complex and so they can\u2019t really be analyzed mathematically typically, so you have to use techniques like visualization to understand what they are doing.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

AI software predicts outcomes for patients with brain tumors

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