Diagnostic formula for the differentiation of adnexal tumors by transvaginal sonography

Prömpeler, H; Madjar, H.; Sauerbrei, Willi; Lattermann, U.; Pfleiderer, A.

doi:10.1016/s0029-7844(96)00507-8

Cited by 19 publications

(7 citation statements)

References 17 publications

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“…Pelvic examination, gray‐scale ultrasound characteristics and tumor marker assessment are usually used in the preoperative diagnosis of cancer. The transvaginal approach is currently the preferred modality for evaluating tumor characteristics and has led investigators to morphological scoring systems (17) . It is thought that the older scoring systems of Granberg et al (7) , De Priest et al (9) , and Lerner et al (10) had many limitations in the systematic description of the lesion since they relied in part upon the subjective experience of the sonologist.…”

Section: Discussionmentioning

confidence: 99%

Pretreatment evaluation of adnexal tumors predicting ovarian cancer

Kobal¹,

Rakar²,

Ribič-Pucelj³

et al. 1999

Int J Gynecol Cancer

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Kobal B, Rakar S, Ribic-Pucelj M, Tomazevie T, Zaletel-Kragelj L. Pretreatment evaluation of adnexal tumors predicting ovarian cancer. The objective of this study was to determine the ability of tumor marker assessment, gray-scale transvaginal with color Doppler ultrasonography to predict ovarian malignancy. One hundred thirty-four subjects with ovarian masses who entered the study prospectively underwent pelvic examination, tumor marker assessment and gray-scale transvaginal with color flow Doppler ultrasonography preoperatively. Malignancy predictors were statistically evaluated with stepwise multiple logistic regression, and the scores from the model were transformed to probability for having a malignant disease. The presence of neovascularization, intracystic papillary projections, elevated serum CA 125, and age over 45 years were significant predictors for malignancy. Positive predictive value (PPV) for the regression model was 89.0%, and negative predictive value (NPV) was 96.8%. Probability for malignancy ranged from 0.004 to 0.991 depending on which covariates were included. Logistic regression analysis of pretreatment diagnostic gray-scale and color Doppler ultrasonographic characteristics, together with CA 125 enabled a creation of probability assessment scale for individual estimation of ovarian mass, which may contribute to final clinical decision.

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Section: Discussionmentioning

confidence: 99%

Pretreatment evaluation of adnexal tumors predicting ovarian cancer

Kobal¹,

Rakar²,

Ribič-Pucelj³

et al. 1999

Int J Gynecol Cancer

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“…The following ultrasound parameters were included: ascites, solid areas without acoustic shadows, masses with at least 30% solid area, tumor diameter, multilocular structures, and surface of the cyst. At cut-off level of 10%, the sensitivity and specificity were 86.5% and 92.6%, respectively for premenopausal and 93% and 82.7%, respectively for postmenopausal patients [9].…”

Section: Regression Modelsmentioning

confidence: 94%

Clinical, ultrasound parameters and tumor marker-based mathematical models and scoring systems in pre-surgical diagnosis of adnexal tumors

et al. 2016

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The choice of management for patients with adnexal tumors requires careful pre-surgical assessment. In case of adnexal masses, the diagnostic difficulties arise from the heterogenic nature of the adnexal diseases, presence of multiple functional changes, and lack of early symptoms of malignancy. A reliable pre-surgical differentiation cannot be performed using clinical features, ultrasound examination, or tumor markers alone. New diagnostic techniques and novel markers are under investigations, however no single test can be used to conclusively differentiate between malignant and non-malignant adnexal masses. Mathematical models and scoring systems based on different clinical, ultrasonographic and laboratory parameters alone or together may facilitate the diagnosis. Selected mathematical models and scoring systems are presented in this article. Models using only ultrasound features include simple rules, regression models, Gynecologic Imaging Report and Data System, and various morphologic scores. Some logistic regression models are based on multiple clinical and ultrasound data. The OVA1 test is based on five tumor markers without using other data. The Risk of Malignancy Algorithm uses two tumor markers with one clinical parameter. i.e. the menopausal status. Some models used clinical, ultrasound and tumor marker data together. This group of models includes risk of malignancy indices, artificial neural networks, and the ADNEX model. Although some of these models have been compared in the literature, more prospective studies are needed to select the most effective model, to develop the existing models, or to create new more effective models of oncological assessment of the adnexal tumors.

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“…Les recommandations doivent présenter au mé-decin l'éventail des procédures d'investigation et de traitement d'une masse annexielle, afin qu'il reconnaisse un cancer à un stade précoce et n'opère pas inutilement un kyste fonctionnel ou un kyste de rétention [2,3]. …”

Section: But Des Recommandationsunclassified

Guide de pratique en cas de découverte d'une masse annexielle

2004

Schweiz Ärzteztg

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Diagnostic formula for the differentiation of adnexal tumors by transvaginal sonography

Abstract: With four binary criteria, a useful diagnostic formula for tumor differentiation was obtained. However, estimates for sensitivity, specificity, and accuracy may be too optimistic because they were derived from the same data that were already used for model selection.

Cited by 19 publications

References 17 publications

Pretreatment evaluation of adnexal tumors predicting ovarian cancer

Pretreatment evaluation of adnexal tumors predicting ovarian cancer

Clinical, ultrasound parameters and tumor marker-based mathematical models and scoring systems in pre-surgical diagnosis of adnexal tumors

Guide de pratique en cas de découverte d'une masse annexielle

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