Correlation analysis between multi-slice CT perfusion imaging and microvessel density in ovarian tumors.
- Author:
Gui-Hua JIANG
1
;
Shao-Qing ZENG
;
Jun-Zhang TIAN
;
Chu-Lan LIN
;
Lan-Ying ZHANG
;
Bi-Ling ZHONG
;
Lian-Bao LIANG
Author Information
- Publication Type:Journal Article
- MeSH: Adult; Aged; Capillaries; pathology; Cystadenocarcinoma; blood supply; diagnostic imaging; Female; Fibroma; blood supply; diagnostic imaging; Humans; Middle Aged; Ovarian Neoplasms; blood supply; diagnostic imaging; Tomography, Spiral Computed; methods
- From: Journal of Southern Medical University 2009;29(11):2197-2200
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVETo analyze the correlation between the perfusion data and microvessel density (MVD) in ovarian tumors, and investigate the hemodynamic features of the tumors in terms of anatomy and functional CT imaging.
METHODSSix patients with surgically confirmed benign ovarian tumors and 6 with malignant ovarian tumors underwent multi-slice CT perfusion imaging to acquire the perfusion parameters including perfusion, PEI, TTP, BV peak enhancement image(PEI), time to peak(TTP) and blood volume(BV). The tumors were stained and counted by Immunohistochemical staining of the microvessels in the tumor was performed to detect the MVD.
RESULTSs The time-density curves of the benign ovarian tumors increased slowly, reaching the peak at 40 s; the curves of the malignant tumors rose rapidly and continuously and reached the peak at 25 s. The differences in the perfusion data (PEI, TTP, BV) were statistically significant between the benign and malignant tumors (P<0.05). The MVD of the malignant tumors was significantly greater than that of the benign tumors (P<0.05). The mean BV of the malignant ovarian tumor was positively correlated to MVD (r=0.786, P<0.05).
CONCLUSIONMulti-slice spiral CT perfusion imaging can provide accurate enhancement data of the ovarian tumors and helps in the diagnosis and differential diagnosis of the ovarian tumors by presenting the changes of the hemodynamic features in the tumors.