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• W3205102014 abstract "The abundance of T cells helps predict the patient's response to immunotherapy, so researchers hope this new method could provide specific and more effective treatments for cancer. Scientists analyzed DNA sequencing data from patients 'cancerous tumors to see if they could detect T cell deficiency. DNA sequencing is often performed on cancerous patients' tumors. It is done to classify them and understand how the cancer progresses. Estimation of immune cells, which are important for cancer control, affects patient survival and guides treatment. Our goal was to be able to develop a new method for annotating immune cells directly from DNA sequences without the need for further data. DNA sequencing allows scientists to see the history and evolution of tumors. In this study, scientists developed a way to calculate the historical levels of T cells; By reassembling or modifying them, they are provided with tools that allow them to detect attackers. In particular, the scientists found a signal for the loss of TREC cell division, and by recording this, they were able to accurately estimate the number of T cells in the tumor. In recent years, screening inhibitors (IPCs), a type of immunotherapy, have emerged as a revolutionary treatment for many types of cancer. ICPs work by blocking proteins called checkpoints made by T lymphocytes. These checkpoints prevent strong immune responses, as they can sometimes prevent T lymphocytes from killing cancer cells. When these checkpoints are closed, T cells are better able to kill cancer cells. One of the biomarkers showed that the potential success of immunotherapy was predicted by the number of T cells available, and that the more T cells available to IPCs, the more cancer cells were killed. It provides the patient's response to treatment without the need for additional data. In fact, the process we have developed can go beyond the standard DNA sequence at no extra time or cost. Our tools also make it possible to study the immune system more than just cancer. Keywords: Cancer; Cells; Tissues; Tumors; Prevention; Prognosis; Diagnosis; Imaging; Screening; Treatment; Management" @default.
• W3205102014 created "2021-10-25" @default.
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• W3205102014 date "2021-10-13" @default.
• W3205102014 modified "2023-09-27" @default.
• W3205102014 title "Mammo Screen Artificial Intelligence (AI) Tool Improves Diagnostic Performance of Radiologists in Detecting Breast Cancer" @default.
• W3205102014 doi "https://doi.org/10.36811/ijho.2021.110017" @default.
• W3205102014 hasPublicationYear "2021" @default.
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