| Literature DB >> 35685426 |
Liguo Li1, Huihui Zhai1, Qiumei Zhang1, Yuan Feng1, Chunhui Yang1, Hong Li1, Hongfen He1.
Abstract
Endometrial tumor has increased in occurrence and fatality in China during the last 11 years, owing to inconsistent hormone use and modifications in people living surrounding and lifestyles. One of the three main gynaecological tumors is endometrial carcinoma (EC). Longer waiting duration of operation was linked to a lower chance of sustainability in endometrial tumor patients. Despite the great sustainability rate of endometrial tumor, only around 46 percent of patients undergo adjuvant treatment. Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and circulating free DNA (cfDNA) are the most investigated tumor noninvasive indicators. These circulating biomarkers are important in the knowledge of metastasis and tumorigenesis, and they could help researchers comprehend how cancer dynamics evolve throughout the therapy and illness development. In patients with solid tumor, the existence of circulating tumor cells (CTCs) in the peripheral blood is linked to a weak prognosis. However, there is a scarcity of information on how to detect CTCs in endometrial cancer (EC). Hence, in this paper, we analyze the guiding effect of CTCs on postoperative adjuvant treatment for sufferers with initial phase endometrial tumor using multi-cox regression method. The dataset is selected and the blood samples are collected using plasma separation method. The CTC is detected using differential diagnosis. The morphology and biological features, Immunocytochemistry, Genomic analysis, Transcriptomic analysis, Proteomic analysis, and molecular analysis are performed and the outcomes are evaluated.Entities:
Year: 2022 PMID: 35685426 PMCID: PMC9174000 DOI: 10.1155/2022/4327977
Source DB: PubMed Journal: J Oncol ISSN: 1687-8450 Impact factor: 4.501
Figure 1Schematic representation of the proposed method.
Comparison of different Techniques for detecting endometrial cancer.
| Techniques | Accuracy (%) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|
| Neural network [ | 96 | 89 | 92 |
| Random forest [ | 78 | 75 | 80 |
| CNN [ | 94.5 | 87 | 85 |
| Multi-cox regression model | 97.84 | 92 | 94 |
Figure 2Comparison of Accuracy (%) for existing and proposed methods.
Figure 3Comparison of Specificity (%) for the existing and proposed method.
Figure 4Comparison of Sensitivity (%) for the existing and proposed method.