Technology predicts tumor response to cancer treatment


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A public-private partnership led by researchers at the University of Texas at Austin has created a new mathematical modeling method that can accurately predict the response of breast cancer patients to treatments such as chemotherapy immediately after the start of treatment. This is a major improvement over current methods in which the effectiveness of the first-line treatment can only be determined after the patient has already undergone several cycles of treatment.

Neoadjuvant therapy (NAT) is designed to shrink tumors and is often the first step in local progression. Cancer Preoperative treatment may be necessary. Examples include chemotherapy, hormone therapy, and more recently immunotherapy. As we know, these treatments are very effective. However, they can also adversely affect the overall health of the patient with no guarantee of success. Therefore, the development of a method to predict a patient’s response to NAT is an important step forward.

When you evaluate something after it has happened, if it doesn’t work, you can’t intervene. But if you can predict what will happen before something happens, you can step in and try to improve the outcome.

“The goal is to address this unmet need by integrating advanced MRI data with biology-based mathematical modeling to develop ways to predict and optimize the response of breast cancer to NAT.” Said Tom Yankirov, director of the Center for Computational Oncology. He is a fellow of the University of Texas at the Austin Institute of Computational Engineering and Sciences and the Livestrong Cancer Institutes, and was appointed to the Department of Biomedical Engineering (BME) at Dell Medical School and Cockrell School.

Yankeelov, who led the study, described the study as “the culmination of several years of research in a public-private partnership,” including UT Austin’s Oden Institute, BME, Live Strong Cancer Institute by Delmed and Texas Oncology, Dell. Seton Medical Center and Austin Radiology Association at the University of Texas.

The new method is “Big data“Approach.

Big data approaches rely solely on statistical inference from the characteristics of large populations. In other words, access to large and relevant patient data sets is important. However, individual patients can be very different from the large populations used to infer information about an individual, and this still does not guarantee better patient outcomes.

“There is growing evidence that the ‘big data only’ approach inevitably masks patient-specific conditions over time, especially in the case of heterogeneous diseases such as cancer,” Yankirov said. Declared. “One set of MRI data is needed before the patient begins treatment, and a second set is needed very soon after starting treatment. From these two data sets, the mathematical model of the tumor is calibrated. , Make patient-specific predictions. Tumors respond to prescribed treatments. “

Research is introduced in the latest version of Nature protocol.. However, the publication of the treaty does not mean the end of this partnership.

Conducting this study in a community health clinic shows that it can affect the real world beyond academic settings. However, success with this presents a unique set of challenges.

“This technology won’t help anyone until we can walk around the lab,” said Jack Virostko, assistant professor at Dell Med and co-author of the study. “We are actively working to introduce it to the communities where most patients receive care. This treaty shows that it is possible.

The success of a partnership between different groups depends more than on the discovery of new discoveries. It also depends on good cooperation between all parties.

Debrapat, Co-Investigator and Vice President of Policy and Strategic Initiatives at Texas Oncology, Clinical Professor at Dell, said: A member of the Med and Livestrong Cancer Institutes. “This work that we are starting together allows us to enable bedside research from the optimal bench to better transform cancer treatments. ”

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For more information:
Angela M. Jarrett et al, Quantitative magnetic resonance imaging and tumor prediction in breast cancer patients in a community setting, Nature protocol (2021). DOI: 10.1038 / s41596-021-00617-y

Quote: The technique is the treatment of cancer Obtained on October 5, 2021 from (October 5, 2021) Predict the reaction of tumors

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