Abstract
Background: With increasing incidence and mortality, cancer is the leading cause of death in China and is a major public health problem. From 2003 to 2010, the incidence and mortality of ovarian cancer maintained a stable trend in China, but due to the large population, the new cases and deaths of ovarian cancer remain high. In 2015, approximately 52,100 women in China were diagnosed with ovarian cancer, and about 22,500 women died of ovarian cancer.
Cancer Differentiation Analysis (CDA) technology is a novel bio-physical based cancer detection approach with multi-level and multi-parameter diagnostic method which detects signals from both protein, cellular, and molecular levels, in which multiple aspects of information can be collected to improve diagnostic accuracy. Initial results showed that CDA technology is capable of detecting ovarian cancer with a high degree of sensitivity and specificity.
Methods: In this study, samples from 449 ovarian cancer patients and 922 healthy female individuals were measured. Peripheral blood of each individual was drawn in EDTA tubes. One class of bio-physical property in blood samples was utilized for CDA tests. CDA data were conducted using SPSS, and the results were shown in table 1.
Results: The average CDA values of ovarian cancer and control groups were 51.09 and 36.69 (rel. units) respectively. The results indicated that ovarian cancer could be significantly distinguished from the control (p < 0.001). Area under ROC curve (AUC) was 0.950, and sensitivity and specificity was 87.1% and 90.0% respectively.
Conclusions: IrInitial results showed that CDA technology could effectively distinguish ovarian cancer from healthy individuals. As a novel bio-physical based cancer detection approach with multi-level and multi-parameter expressions, CDA could be a potential candidate for ovarian cancer detection.
- Meeting: Shenzheng New Horizons in Cancer Research. 2019.
Author(s): Yue Lin, Hongmei Tao, Xing Tang, Xuedong Du, Chris Chang Yu*
Anpac Bio-Medical Science Co., Ltd., Shanghai, China
