Abstract:

Ovarian cancer is one of the malignant tumors with extremely high mortality in the female reproductive system. Its early symptoms are not obvious, and it is prone to misdiagnosis and missed diagnosis. More than 70% of patients are already in the advanced stage at the time of diagnosis, and the survival rate is relatively low. Currently, the sensitivity and specificity of common screening methods such as carbohydrate antigen 125 testing and transvaginal ultrasound examination in the early diagnosis of ovarian cancer are limited. In recent years, with the development of gene sequencing technology, molecular diagnosis has become an important means for the early detection of ovarian cancer. In particular, the detection based on circulating tumor DNA (ctDNA) has received extensive attention. CtDNA is a DNA fragment released by tumor cells into the blood, carrying tumor characteristics and capable of dynamically and real-time reflecting the tumor's status. The mutation detection and methylation pattern recognition of ovarian cancer based on ctDNA have significantly improved the detection sensitivity. In addition, the combination of ctDNA and machine learning methods has further increased the early detection rate of ovarian cancer. In the future, with the research on detection technology and large-scale population data, as well as the integration of multi-omics data, the application of ctDNA in the diagnosis and treatment of ovarian cancer will be greatly promoted, and the quality of patients' lives will be improved.

Key words: Circulating tumor DNA, Ovarian neoplasms, Carcinoma, Sequence analysis, DNA, Methylation, Machine learning, Early diagnosis