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The researchers developed a computer algorithm that could accurately predict cancer outcomes in patients.
Cancer is a group of complex diseases characterized by abnormal growth of cells. One of the biggest challenges in caring for cancer patients is the inability to accurately predict long-term health outcomes. Specifically, it is difficult for physicians to know how long patients will live and how they can respond to chemotherapy. Patients are suffering because of these uncertainties because they are unsure of their future. Currently, physicians predict patient outcomes by badessing the patient's initial symptoms, determining the cell type from which the cancer is derived, and the size and location of the tumor. However, these approaches are not fully effective and provide only a glimpse of the progression of the tumor. They provide no information on the tumor's response to the treatments and on the state of improvement or deterioration of the patients over time. It is therefore important that researchers seek effective new strategies to accurately predict the health outcomes of cancer patients.
Researchers at Stanford University School of Medicine recently published a study on Cell, where they demonstrated the effectiveness of CIRI (Continuous Individualized Risk Index) in predicting cancer outcomes. CIRI is a computer algorithm developed by the authors of the study. It is based on the same techniques used by statisticians for decades to forecast sports matches and elections. In short, the algorithm works by integrating a large amount of data, including tumor responsiveness to treatment and the amount of cancer DNA in the patient's blood, to generate a single, continuous risk badessment.
The researchers collected data on 2,500 patients with diffuse large B-cell lymphoma, the most common type of blood cancer in the United States. The data was then introduced into the computer algorithm so that he could recognize and identify patterns / combinations that he would then use to determine the results for the patient. They found that, compared to standard methods, CIRI was much better. However, its predictive power is not quite perfect and needs to be further improved. They also tested the CIRI to predict the results of other cancers, including leukemia and bad cancer. Again, they found that while the predictive power was not perfect and the level of precision varied from one cancer to another, CIRI was much better at predicting health outcomes than conventional techniques. .
These results could have important consequences for the way doctors manage cancer patients. Specifically, the technology could allow patients to have a better idea of what to expect during the course of their disease. Although CIRI can not change health outcomes, it can at least allow individuals to plan their future appropriately. The researchers believe that the technology can also be used to constantly monitor the progress of patients' disease, allowing doctors to quickly determine whether a patient's chemotherapy treatment improves or deteriorates their health. With this information in hand, doctors will be able to make minor adjustments to ensure they are always receiving the best care.
Written by Haisam Shah, BSc
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Reference: D. Kurtz, M.Sfahani, M.Scherer, F., J. Soo, M.M.C., Liu, C.L., … & Westin, J.R. (2019). Dynamic risk profile using serial tumor biomarkers for personalized prediction of results. Cell.
Image of Gerd Altmann from Pixabay
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