Objective: The purpose of this study was to evaluate the usefulness of virtual spherical tissue sampling using 3-dimensional (3D) ultrasound power Doppler angiography to enhance differentiation between normal and pathologic ovaries.

Methods: Twenty-seven cases with ovarian tumors were analyzed: 14 with invasive cancers and 13 with borderline tumors confirmed by surgery. The control subjects consisted of 53 healthy ovulating women. Ultrasound scans were done, and 3D volumes were analyzed with 3-/4-dimensional software for personal computers based on 3D vascularity indices: the vascularization index, flow index, and vascularization-flow index. A virtual spherical tissue sample of 1 cm3 was taken from the place of the highest vessel density contained completely within the contours of the ovary. Calculations for the whole solid volume were done for comparison.

Results: Vascularity indices for both 1-cm3 spherical samples and whole dense parts of the ovaries were compared in the following groups: (1) ovarian tumors versus controls, (2) normal ovaries in the proliferative versus secretory phase, (3) invasive cancers versus borderline tumors, (4) invasive cancers versus normal ovaries, and (5) borderline tumors versus normal ovaries. Spherical 1-cm3 sampling achieved a higher degree of discrimination between the groups compared with the whole solid-part approach.

Conclusions: Spherical 1-cm3 sampling of ovarian tissue with 3D ultrasound power Doppler angiography is a sensitive and promising approach to differentiate between ovarian tumors and normal ovaries. It opens the possibility to implement objective computerized positioning, standardized comparison, and analysis of ovarian tumors.

Download full-text PDF

Source
http://dx.doi.org/10.7863/jum.2008.27.3.425DOI Listing

Publication Analysis

Top Keywords

ovarian tumors
20
normal ovaries
16
spherical tissue
12
power doppler
12
doppler angiography
12
invasive cancers
12
borderline tumors
12
tissue sampling
8
sampling 3-dimensional
8
tumors
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!