Correlation Analysis of Ultrasonography with Microvascular Architecture MVD and VEGF in Nude Mice with Ovarian Cancer

Objective This article aims to analyze correlations between microvascular density (MVD) of nude mice as well as VEGF and contrast-enhanced ultrasound (CEUS) parameters. Method Construct a model with 60 ovarian cancer of nude mice. Via ultrasound contrast agent injected intravenously.the enhancement features of tumors were observed in real time and the time-intensity curve (TIC) of CEUS was also obtained. By conducting quantitative analysis of Gamma-fit, we were able to get the relative parameters. Utilizing CD34 and CD31 for counting, the indices of MVD and VEGF level of “hotspot” area inside the tumors were measured. Through the analysis of CEUS image characteristics of ovarian cancer model in contrast with IHC indices, we discussed about their correlations. We matched 10 nude mice granuloma models to find differences between IHC quotas and CEUS parameters. Result The differences in CEUS clearing patterns of nude mice with different growth periods (30 days / 40 days) were considered statistically significant. surface density and H-score of CD31, CD34 and VEGF were significantly different between the transplanted tumor group and the granuloma group in nude mice.Statistics in correlation between CEUS parameter indices and IHC showed that the area under the curve (AUC)and average optical density of CD31 are positively correlated，the arrival time (ATM) and average optical density of VEGF are positively correlated. Conclusion There are differences among the microvascular architectures of ovarian cancer given different growth cycles. CEUS has shown its value during the clearing phases. The microvascular architecture of ovarian cancer and granuloma is obviously different. However, there are overlaps between their CEUS performances, where there is no significant statistical difference. The area under the curve (AUC) and time to arrival (ATM) of the quantifiers were positively correlated with CD31 and VEGF, which is helpful for clinical applications in early diagnosis and prognosis of ovarian cancer patient