Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.
Endometrial Cancer Prognosis Of Endometrial Cancer Patients Application Of Machine Learning Approaches Choice Of Treatment Modalities Machine Learning Machine Learning Approaches Endometrial Cancer Diagnosis Modalities For Prevention Early Cancer Diagnosis Development Of Rapid Systems
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Round-ups are the summaries of handpicked papers around trending topics published every week. These would enable you to scan through a collection of papers and decide if the paper is relevant to you before actually investing time into reading it.
Climate change Research Articles published between Sep 12, 2022 to Sep 18, 2022
Sep 19, 2022
Articles Included: 5
Rainfall projections from the Coupled Model Intercomparison Project (CMIP) models are strongly tied to projected sea surface temperature (SST) spatial...Read More
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