Use of ultrasound pattern recognition by expert operators to identify borderline ovarian tumors: a study of diagnostic performance and interobserver agreement

Yazbek, Joseph; Ameye, Lieveke; Timmerman, Dirk; Testa, Antonia Carla; Valentin, Lil; Holsbeke, C. Van

doi:10.1002/uog.7334

Cited by 22 publications

(14 citation statements)

References 16 publications

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“…No published mathematical model has yet replaced the effectiveness of this subjective evaluation, even in a subgroup of tumors qualified as "difficult to diagnose" 10 or for borderline tumors, 11,12 whose diagnosis may be more difficult than for invasive and benign tumors. 13 In our experience, the univariate analysis also showed that the clinician's judgment is the most discriminating factor in the diagnosis of malignancy, with an AUC of 0.795, sensitivity of 86%, and specificity of 73%. These numbers are nevertheless lower than published data, for which there are two possible reasons.…”

Section: Discussionmentioning

confidence: 64%

Preoperative Diagnosis of Ovarian Tumors Using Pelvic Contrast-Enhanced Sonography

Veyer¹,

Marret²,

Bleuzen³

et al. 2010

Journal of Ultrasound in Medicine

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Section: Discussionmentioning

confidence: 64%

Preoperative Diagnosis of Ovarian Tumors Using Pelvic Contrast-Enhanced Sonography

Veyer¹,

Marret²,

Bleuzen³

et al. 2010

Journal of Ultrasound in Medicine

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“…The operators were also asked to provide the degree of confidence with which they made the diagnosis, confidence being classified into certain, probable and uncertain diagnosis. Full details of the image acquisition techniques, the histological classification and staging of tumors and the information provided to each of the examiners are given by Yazbek et al 9 The main outcome measure was the degree of confidence of the three experts in making the diagnosis of benign, borderline or invasive malignant ovarian tumor.…”

Section: Methodsmentioning

confidence: 99%

Confidence of expert ultrasound operators in making a diagnosis of adnexal tumor: effect on diagnostic accuracy and interobserver agreement

Yazbek

Ameye

Testa

et al. 2009

Ultrasound in Obstet & Gyne

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“…1, 2) had very similar sonographic features, a smaller diameter, fewer locules, higher numbers of papillary excrescences, and higher color scores inside solid components than mucinous BOTs of intestinal types [83]. They often manifest as unilocular-solid or multilocular-solid tumors with irregular and perfunded papillary projections without other signs of complexity, such as a solid pattern, irregular septae, and irregular inner walls due to tumor deposits in young women [81][82][83][84][85][86]. Their cyst fluid can be anechoic or have low-level or ground-glass echogenicity.…”

Section: Diagnosismentioning

confidence: 96%

Diagnosis, Treatment, and Follow-Up of Borderline Ovarian Tumors

et al. 2012

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After completing this course, the reader will be able to:1. Compare the epidemiologic and reproductive risk factors in BOTs with those in ovarian cancers and describe the molecular background of development of BOTs.2. Use the pathological terminology with either original grouping of borderline category or new subclassification of BOTs and assess the major predictor of recurrence and survival.3. Determine an appropriate diagnostic algorithm for patients with symptoms suggesting malignant ovarian tumors that will identify borderline ovarian tumors when present.This article is available for continuing medical education credit at CME.TheOncologist.com. CME CME ABSTRACTBorderline ovarian tumors represent a heterogeneous group of noninvasive tumors of uncertain malignant potential with characteristic histology. They occur in younger women, are present at an early stage, and have a favorable prognosis, but symptomatic recurrence and death may be found as long as 20 years after therapy in some patients. The molecular changes in borderline ovarian tumors indicate linkage of this disease to type I ovarian tumors (lowgrade ovarian carcinomas). The pathological stage of disease and subclassification of extraovarian disease into invasive and noninvasive implants, together with the presence of postoperative macroscopic residual disease, appear to be the major predictor of recurrence and survival. However, it should be emphasized that the most important negative prognostic factor for recurrence is just the use of conservative surgery, but without any impact on patient survival because most recurrent diseases are of the borderline type-easily curable and with an excellent prognosis. Borderline tumors are difficult masses to correctly preoperatively diagnose using imaging methods because their macroscopic features may overlap with invasive and benign ovarian tumors. Over the past several decades, surgical therapy has shifted from a radical approach to more conservative treatment; however, oncologic safety must always be balanced. Follow-up is essential using routine ultrasound imaging, with special attention paid to the remaining ovary in conservatively treated patients. Current literature on this topic leads to a number of controversies that will be discussed thoroughly in this article, with the aim to provide recommendations for the clinical management of these patients. The Oncologist

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Use of ultrasound pattern recognition by expert operators to identify borderline ovarian tumors: a study of diagnostic performance and interobserver agreement

Abstract: Objective To assess the accuracy and reproducibility of ultrasound 'pattern recognition' for the diagnosis of borderline ovarian tumors by asking experienced ultrasound operators to evaluate representative images of different types of adnexal tumor. Methods

Cited by 22 publications

References 16 publications

Preoperative Diagnosis of Ovarian Tumors Using Pelvic Contrast-Enhanced Sonography

Preoperative Diagnosis of Ovarian Tumors Using Pelvic Contrast-Enhanced Sonography

Confidence of expert ultrasound operators in making a diagnosis of adnexal tumor: effect on diagnostic accuracy and interobserver agreement

Diagnosis, Treatment, and Follow-Up of Borderline Ovarian Tumors

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