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Background: Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent metaanalysis concluded that the International Ovarian Tumor Analysis algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant.Objective: We sought to develop and validate a model to predict the risk of malignancy in adnexal masses using the ultrasound features in the Simple Rules.Study Design: This was an international cross-sectional cohort study involving 22 oncology centers, referral centers for ultrasonography, and general hospitals. We included consecutive patients with an adnexal tumor who underwent a standardized transvaginal ultrasound examination and were selected for surgery. Data on 5020 patients were recorded in 3 phases from 2002 through 2012. The 5 Simple Rules features indicative of a benign tumor (B-features) and the 5 features indicative of malignancy (M-features) are based on the presence of ascites, tumor morphology, and degree of vascularity at ultrasonography. Gold standard was the histopathologic diagnosis of the adnexal mass (pathologist blinded to ultrasound findings). Logistic regression analysis was used to estimate the risk of malignancy based on the 10 ultrasound features and type of center. The diagnostic performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), negative predictive value (NPV), and calibration curves.Results: Data on 4848 patients were analyzed. The malignancy rate was 43% (1402/3263) in oncology centers and 17% (263/1585) in other centers. The area under the receiver operating characteristic curve on validation data was very similar in oncology centers (0.917; 95% confidence interval, 0.901-0.931) and other centers (0.916; 95% confidence interval, 0.873-0.945). Risk estimates showed good calibration. In all, 23% of patients in the validation data set had a very low estimated risk (<1%) and 48% had a high estimated risk (≥30%). For the 1% risk cutoff, sensitivity was 99.7%, specificity 33.7%, LR+ 1.5, LR- 0.010, PPV 44.8%, and NPV 98.9%. For the 30% risk cutoff, sensitivity was 89.0%, specificity 84.7%, LR+ 5.8, LR- 0.13, PPV 75.4%, and NPV 93.9%.Conclusion: Quantification of the risk of malignancy based on the Simple Rules has good diagnostic performance both in oncology centers and other centers. A simple classification based on these risk estimates may form the basis of a clinical management system. Patients with a high risk may benefit from surgery by a gynecological oncologist, while patients with a lower risk may be managed locally. [ABSTRACT FROM AUTHOR]

Objectives To develop a logistic regression model for discrimination between benign and malignant unilocular solid cysts with papillary projections but no other solid components, and to compare its diagnostic performance with that of subjective evaluation of ultrasound findings (subjective assessment), CA??125 and the risk of malignancy index (RMI). Methods Among the 3511 adnexal masses in the International Ovarian Tumor Analysis (IOTA) database there were 252 (7%) unilocular solid cysts with papillary projections but no other solid components ('unilocular cysts with papillations'). All had been examined with transvaginal ultrasound using the IOTA standardized research protocol. The ultrasound examiner had also classified each mass as certainly or probably benign, unclassifiable, or certainly or probably malignant. A logistic regression model to discriminate between benignity and malignancy was developed for all unilocular cysts with papillations (175 tumors in the training set and 77 in the test set) and for unilocular cysts with papillations for which the ultrasound examiner was not certain about benignity/malignancy (113 tumors in the training set and 53 in the test set). The gold standard was the histological diagnosis of the surgically removed adnexal mass. Results A model containing six variables was developed for all unilocular cysts with papillations. The model had an area under the receiver-operating characteristics curve (AUC) on the test set of 0.83 (95% CI, 0.74-0.93). The optimal risk cut-off, as defined on the training set (0.35), resulted in sensitivity 69% (20/29), specificity 79% (38/48), positive likelihood ratio (LR?+) 3.31 and negative likelihood ratio (LR-) 0.39 on the test set. The corresponding values for subjective assessment when using the ultrasound examiner's dichotomous classification of the mass as benign or malignant were 97% (28/29), 79% (38/48), 4.63 and 0.04. A model containing four variables was developed for unilocular cysts with papillations for which the ultrasound examiner was not certain about benignity/malignancy. The model had an AUC of 0.74 (95% CI, 0.60-0.88) on the test set. The optimal risk cut-off of the model, as defined on the training set (0.30), resulted in sensitivity 57% (12/21), specificity 78% (25/32), LR?+?2.61 and LR- 0.55 on the test set. The corresponding values for subjective assessment were 95% (20/21), 78% (25/32), 4.35 and 0.06. CA??125 and RMI had virtually no diagnostic ability. Conclusions Even though logistic regression models to predict malignancy in unilocular cysts with papillations can be developed, they have at most moderate performance and are not superior to subjective assessment for discrimination between benignity and malignancy. [ABSTRACT FROM AUTHOR]

Objectives To estimate the ability to discriminate between benign and malignant adnexal masses of different size using: subjective assessment, two International Ovarian Tumor Analysis (IOTA) logistic regression models (LR1 and LR2), the IOTA simple rules and the risk of malignancy index (RMI). Methods We used a multicenter IOTA database of 2445 patients with at least one adnexal mass, i.e. the database previously used to prospectively validate the diagnostic performance of LR1 and LR2. The masses were categorized into three subgroups according to their largest diameter: small tumors (diameter < 4 cm; n = 396), medium-sized tumors (diameter, 4-9.9 cm; n = 1457) and large tumors (diameter ≥ 10 cm, n = 592). Subjective assessment, LR1 and LR2, IOTA simple rules and the RMI were applied to each of the three groups. Sensitivity, specificity, positive and negative likelihood ratio (LR+, LR−), diagnostic odds ratio (DOR) and area under the receiver-operating characteristics curve (AUC) were used to describe diagnostic performance. A moving window technique was applied to estimate the effect of tumor size as a continuous variable on the AUC. The reference standard was the histological diagnosis of the surgically removed adnexal mass. Results The frequency of invasive malignancy was 10% in small tumors, 19% in medium-sized tumors and 40% in large tumors; 11% of the large tumors were borderline tumors vs 3% and 4%, respectively, of the small and medium-sized tumors. The type of benign histology also differed among the three subgroups. For all methods, sensitivity with regard to malignancy was lowest in small tumors (56-84% vs 67-93% in medium-sized tumors and 74-95% in large tumors) while specificity was lowest in large tumors (60-87%vs 83-95% in medium-sized tumors and 83-96% in small tumors ). The DOR and the AUC value were highest in medium-sized tumors and the AUC was largest in tumors with a largest diameter of 7-11 cm. Conclusion Tumor size affects the performance of subjective assessment, LR1 and LR2, the IOTA simple rules and the RMI in discriminating correctly between benign and malignant adnexal masses. The likely explanation, at least in part, is the difference in histology among tumors of different size. Copyright © 2012 ISUOG. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective To develop a logistic regression model that can discriminate between benign and malignant adnexal masses perceived to be difficult to classify by subjective evaluation of gray-scale and Doppler ultrasound findings (subjective assessment) and to compare its diagnostic performance with that of subjective assessment, serum CA 125 and the risk of malignancy index (RMI). Methods We used data from the 3511 patients with an adnexal mass included in the International Ovarian Tumor Analysis (IOTA) studies. All patients had been examined using transvaginal gray-scale and Doppler ultrasound following a standardized research protocol carried out by an experienced ultrasound examiner using a high-end ultrasound system. In addition to prospectively collecting information on > 40 clinical and ultrasound variables, the ultrasound examiner classified each mass as certainly or probably benign, unclassifiable, or certainly or probably malignant. A logistic regression model to discriminate between benignity and malignancy was developed for the unclassifiable masses ( n = 244, i.e. 7% of all tumors) using a training set (160 tumors, 45 malignancies) and then tested on a test set (84 tumors, 28 malignancies). The gold standard was the histological diagnosis of the surgically removed adnexal mass. The area under the receiver-operating characteristics curve (AUC), sensitivity, specificity, positive likelihood ratio (LR+) and negative likelihood ratio (LR−) were used to describe diagnostic performance and were compared between subjective assessment, CA 125, the RMI and the logistic regression model created. Results One variable was retained in the logistic regression model: the largest diameter (in mm) of the largest solid component of the tumor (odds ratio (OR) = 1.04; 95% CI, 1.02-1.06). The model had an AUC of 0.68 (95% CI, 0.59-0.78) on the training set and an AUC of 0.65 (95% CI, 0.53-0.78) on the test set. On the test set, a cut-off of 25% probability of malignancy (corresponding to the largest diameter of the largest solid component of 23 mm) resulted in a sensitivity of 64% (18/28), a specificity of 55% (31/56), an LR+ of 1.44 and an LR− of 0.65. The corresponding values for subjective assessment were 68% (19/28), 59% (33/56), 1.65 and 0.55. On the test set of patients with available CA 125 results, the LR+ and LR− of the logistic regression model (cut-off = 25% probability of malignancy) were 1.29 and 0.73, of subjective assessment were 1.45 and 0.63, of CA 125 (cut-off = 35 U/mL) were 1.24 and 0.84 and of RMI (cut-off = 200) were 1.21 and 0.92. Conclusions About 7% of adnexal masses that are considered appropriate for surgical removal cannot be classified as benign or malignant by experienced ultrasound examiners using subjective assessment. Logistic regression models to estimate the risk of malignancy, CA 125 measurements and the RMI are not helpful in these masses. Copyright © 2011 ISUOG. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to confirm the diagnostic capability of two logistic regression models that contain ultrasound and clinical variables to calculate the malignancy in adnexal masses. The study involved 1938 patients with adnexal masses, who underwent to transvaginal ultrasound assessment by a radiologist or a gynecologist skilled in ultrasonography. Results show that both models provide accurate differentiation between malignant and benign masses.

An abstract of the article "Lesion size affects the diagnostic performance of the International Ovarian Tumor Analysis (IOTA) logistic regression models, the IOTA simple rules and the risk of malignancy index to estimate the risk of malignancy in adnexal masses," by A. Di Legge is presented.

An abstract of a research paper related to logistic regression model and risk of malignancy (RMI) model in preoperative adnexal masses by A. Czekierdowski and colleagues is presented.