C. Van Holsbeke

Simple ultrasound-based rules for the diagnosis of ovarian cancer

To derive simple and clinically useful ultrasound‐based rules for discriminating between benign and malignant adnexal masses.

Ovarian cancer prediction in adnexal masses using ultrasound‐based logistic regression models: a temporal and external validation study by the IOTA group

The aims of the study were to temporally and externally validate the diagnostic performance of two logistic regression models containing clinical and ultrasound variables in order to estimate the risk of malignancy in adnexal masses, and to compare the results with the subjective interpretation of ultrasound findings carried out by an experienced ultrasound examiner (‘subjective assessment’).

Endometriomas: their ultrasound characteristics

To describe the ultrasound characteristics of endometriomas in pre‐ and postmenopausal patients and to develop rules that characterize endometriomas.

Diagnostic accuracy of transvaginal ultrasound examination for assigning a specific diagnosis to adnexal masses

To determine the sensitivity and specificity of subjective evaluation of gray‐scale and Doppler ultrasound findings (here called pattern recognition) when used by experienced ultrasound examiners with regard to making a specific diagnosis of adnexal masses.

Adding a single CA 125 measurement to ultrasound imaging performed by an experienced examiner does not improve preoperative discrimination between benign and malignant adnexal masses

To determine whether CA 125 measurement is superior to ultrasound imaging performed by an experienced examiner for discriminating between benign and malignant adnexal lesions, and to determine whether adding CA 125 to ultrasound examination improves diagnostic performance.

Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies

In order to ensure that ovarian cancer patients access appropriate treatment to improve the outcome of this disease, accurate characterization before any surgery on ovarian pathology is essential. The International Ovarian Tumor Analysis (IOTA) collaboration has standardized the approach to the ultrasound description of adnexal pathology. A prospectively collected large database enabled previously developed prediction models like the risk of malignancy index (RMI) to be tested and novel prediction models to be developed and externally validated in order to determine the optimal approach to characterize adnexal pathology preoperatively. The main IOTA prediction models (logistic regression model 1 (LR1) and logistic regression model 2 (LR2)) have both shown excellent diagnostic performance (area under the curve (AUC) values of 0.96 and 0.95, respectively) and outperform previous diagnostic algorithms. Their test performance almost matches subjective assessment by experienced examiners, which is accepted to be the best way to classify adnexal masses before surgery. A two‐step strategy using the IOTA simple rules supplemented with subjective assessment of ultrasound findings when the rules do not apply, also reached excellent diagnostic performance (sensitivity 90%, specificity 93%) and misclassified fewer malignancies than did the RMI. An evidence‐based approach to the preoperative characterization of ovarian and other adnexal masses should include the use of LR1, LR2 or IOTA simple rules and subjective assessment by an experienced examiner. Copyright

Imaging in gynecological disease (1): ultrasound features of metastases in the ovaries differ depending on the origin of the primary tumor

To describe the gray‐scale and color Doppler ultrasound findings of metastatic tumors in the ovary according to the origin of the primary tumor.

Adnexal masses difficult to classify as benign or malignant using subjective assessment of gray‐scale and Doppler ultrasound findings: logistic regression models do not help

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).

Imaging of gynecological disease (3): clinical and ultrasound characteristics of granulosa cell tumors of the ovary

To describe the clinical and ultrasound characteristics of granulosa cell tumors (GCTs) of the ovary, and to define the ultrasound appearance of GCTs based on pattern recognition.

Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods

To develop flexible classifiers that predict malignancy in adnexal masses using a large database from nine centers.