Benign and malignant ovarian masses: Selection of the most discriminating gray-scale and doppler sonographic features

Douglas L. Brown*, Peter M. Doubilet, Frank H Miller, Mary C. Frates, Faye C. Laing, Donald N. DiSalvo, Carol B. Benson, Mark H. Lerner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

158 Scopus citations

Abstract

PURPOSE: To determine the gray-scale and Doppler sonographic features that best enable discrimination between malignant and benign ovarian masses and develop a scoring system for accurate diagnosis with these features. MATERIALS AND METHODS: Gray-scale and Doppler sonographic features of 211 adnexal masses were correlated with the final diagnosis; the most discriminating features for malignancy were selected with stepwise logistic regression. RESULTS: Twenty-eight masses were malignant and 183 benign. All masses with a markedly hyperechoic solid component or no solid component were benign. For masses with a nonhyperechoic solid component, additional features that allowed statistically significant discrimination of benignity from malignancy were, in decreasing order of importance, (a) location of flow at conventional color Doppler imaging, (b) amount of free intraperitoneal fluid, and (c) presence and thickness of septations. A scoring formula that made us of values based on the logistic regression equation had an area under the receiver operating characteristic curve of 0.98 ± 0.01. The cutoff score with the highest accuracy had a sensitivity of 93% and specificity of 93%. CONCLUSION: A solid component is the most statistically significant predictor of a malignant ovarian mass. A multiparameter scoring system that uses three gray-scale and one Doppler feature, developed by means of stepwise logistic regression, has high sensitivity and specificity for predicting malignancy.

Original languageEnglish (US)
Pages (from-to)103-110
Number of pages8
JournalRadiology
Volume208
Issue number1
DOIs
StatePublished - Jan 1 1998

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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