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. 1999 Jan;13(1):11-6.
doi: 10.1046/j.1469-0705.1999.13010011.x.

Subjective assessment of adnexal masses with the use of ultrasonography: an analysis of interobserver variability and experience

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Free article

Subjective assessment of adnexal masses with the use of ultrasonography: an analysis of interobserver variability and experience

D Timmerman et al. Ultrasound Obstet Gynecol. 1999 Jan.
Free article

Abstract

Objective: The aim of the study was to evaluate the subjective assessment of ultrasonographic images for discriminating between malignant and benign adnexal masses.

Study design: The study was prospective. Initially, one ultrasonographer preoperatively assessed 300 consecutive patients with adnexal masses. Subsequently, the recorded transparent photographic prints were independently assessed by five investigators, with different qualifications and level of experience, who were also given a brief clinical history of the patients (i.e. the age, menstrual status, family history of ovarian cancer, previous pelvic surgery and the presenting symptoms). The diagnostic performance of the observers was compared with the histopathology classification of malignant or benign tumors. The end-points were accuracy, interobserver agreement and the possible effect of experience.

Results: The first ultrasonographer and the most experienced investigator both obtained an accuracy of 92%. There was very good agreement between these two investigators in the classification of the adnexal masses (Cohen's kappa 0.85). The less experienced observers obtained a significantly lower accuracy, which varied between 82% and 87%. Their interobserver agreement was moderate to good (Cohen's kappa 0.52 to 0.76).

Conclusion: Experienced ultrasonographers using some clinical information and their subjective assessment of ultrasonographic images can differentiate malignant from benign masses in most cases. The accuracy and the level of interobserver agreement are both correlated with experience. About 10% of masses were extremely difficult to classify (only < 50% of assessors were correct).

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