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5. Prospective external validation of the ‘ovarian crescent sign’ as a single ultrasound parameter to distinguish between benign and malignant adnexal pathology

Author

Van Holsbeke, C., primary, Van Belle, V., additional, Leone, F. P. G., additional, Guerriero, S., additional, Paladini, D., additional, Melis, G. B., additional, Greggi, S., additional, Fischerova, D., additional, De Jonge, E., additional, Neven, P., additional, Bourne, T., additional, Valentin, L., additional, Van Huffel, S., additional, and Timmerman, D., additional

Published
2010
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6. Acoustic streaming cannot discriminate reliably between endometriomas and other types of adnexal lesion: a multicenter study of 633 adnexal masses

Author

Van Holsbeke, C., primary, Zhang, Jingh, additional, Van Belle, V., additional, Paladini, D., additional, Guerriero, S., additional, Czekierdowski, A., additional, Muggah, H., additional, Ombelet, W., additional, Jurkovic, D., additional, Testa, A. C., additional, Valentin, L., additional, Van Huffel, S., additional, Bourne, T., additional, and Timmerman, D., additional

Published
2010
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8. Intravenous contrast ultrasound examination using contrast‐tuned imaging (CnTI™) and the contrast medium SonoVue® for discrimination between benign and malignant adnexal masses with solid components

Author

Testa, A. C., primary, Timmerman, D., additional, Van Belle, V., additional, Fruscella, E., additional, Van Holsbeke, C., additional, Savelli, L., additional, Ferrazzi, E., additional, Leone, F. P. G., additional, Marret, H., additional, Tranquart, F., additional, Exacoustos, C., additional, Nazzaro, G., additional, Bokor, D., additional, Magri, F., additional, Van Huffel, S., additional, Ferrandina, G., additional, and Valentin, L., additional

Published
2009
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11. OC164: Ultrasound contrast examination to discriminate benign and malignant adnexal masses

Author

Testa, A. C., primary, Timmerman, D., additional, Fruscella, E., additional, Van Holsbeke, C., additional, Savelli, L., additional, Ferrazzi, E., additional, Leone, F.P.G., additional, Marret, H., additional, Exacoustos, C., additional, De Placido, G., additional, Bokor, D., additional, Ferrandina, G., additional, Van Belle, V., additional, and Valentin, L., additional

Published
2008
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16. Acoustic streaming cannot discriminate reliably between endometriomas and other types of adnexal lesion: a multicenter study of 633 adnexal masses

Author

Willem Ombelet, Dirk Timmerman, Artur Czekierdowski, Dario Paladini, Henry Muggah, Davor Jurkovic, C. Van Holsbeke, Lil Valentin, Jingh Zhang, S. Van Huffel, Antonia Carla Testa, Stefano Guerriero, Tom Bourne, Vanya Van Belle, Van Holsbeke, C, Zhang, J, Van Belle, V, Paladini, Dario, Guerriero, S, Czekierdowski, A, Muggah, H, Ombelet, W, Jurkovic, D, Testa, Ac, Valentin, L, Van Huffel, S, Bourne, T, and Timmerman, D.

Subjects
Adult, Pathology, medicine.medical_specialty, Adolescent, Endometriosis, Adnexal mass, Diagnosis, Differential, Lesion, Young Adult, Acoustic streaming, Ovarian tumor, Positive predicative value, otorhinolaryngologic diseases, medicine, Humans, Radiology, Nuclear Medicine and imaging, Cyst, Prospective Studies, Ultrasonography, Ovarian Neoplasms, Radiological and Ultrasound Technology, business.industry, Reproducibility of Results, Obstetrics and Gynecology, Echogenicity, adnexal masses, General Medicine, Middle Aged, medicine.disease, Settore MED/40 - GINECOLOGIA E OSTETRICIA, Reproductive Medicine, Adnexal Diseases, Female, sense organs, medicine.symptom, Nuclear medicine, business, psychological phenomena and processes
Abstract

Objective To determine the ability of acoustic streaming to discriminate between endometriomas and other adnexal masses. Methods We used data from 1938 patients with an adnexal mass included in Phase 2 of the International Ovarian Tumor Analysis (IOTA) study. All patients had been examined by transvaginal gray-scale and Doppler ultrasound following a standardized research protocol. Assessment of acoustic streaming was voluntary and was carried out only in lesions containing echogenic cyst fluid. Acoustic streaming was defined as movement of particles inside the cyst fluid during gray-scale and/or color Doppler examination provided that the probe had been held still for two seconds to ensure that the movement of the particles was not caused by movement of the probe or the patient. Only centers where acoustic streaming had been evaluated in > 90% of cases were included. Sensitivity, specificity, positive and negative likelihood ratios (LR+, LR-), and positive and negative predictive values (PPV and NPV) of acoustic streaming with regard to endometrioma were calculated. Results 460 (24%) masses were excluded because they were examined in centers where

Published
2010

17. Extending the c-statistic to nominal polytomous outcomes: the Polytomous Discrimination Index.

Author

Van Calster B, Van Belle V, Vergouwe Y, Timmerman D, Van Huffel S, and Steyerberg EW

Subjects
Female, Humans, Male, Numerical Analysis, Computer-Assisted, Ovarian Neoplasms diagnosis, Prognosis, Testicular Neoplasms drug therapy, Area Under Curve, Data Interpretation, Statistical, ROC Curve
Abstract

Diagnostic problems in medicine are sometimes polytomous, meaning that the outcome has more than two distinct categories. For example, ovarian tumors can be benign, borderline, primary invasive, or metastatic. Extending the main measure of binary discrimination, the c-statistic or area under the ROC curve, to nominal polytomous settings is not straightforward. This paper reviews existing measures and presents the polytomous discrimination index (PDI) as an alternative. The PDI assesses all sets of k cases consisting of one case from each outcome category. For each category i (i = 1, … ,k), it is assessed whether the risk of category i is highest for the case from category i. A score of 1∕k is given per category for which this holds, yielding a set score between 0 and 1 to indicate the level of discrimination. The PDI is the average set score and is interpreted as the probability to correctly identify a case from a randomly selected category within a set of k cases. This probability can be split up by outcome category, yielding k category-specific values that result in the PDI when averaged. We demonstrate the measures on two diagnostic problems (residual mass histology after chemotherapy for testicular cancer; diagnosis of ovarian tumors). We compare the behavior of the measures on theoretical data, showing that PDI is more strongly influenced by simultaneous discrimination between all categories than by partial discrimination between pairs of categories. In conclusion, the PDI is attractive because it better matches the requirements of a measure to summarize polytomous discrimination., (Copyright © 2012 John Wiley & Sons, Ltd.)

Published
2012
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18. Additive survival least-squares support vector machines.

Author

Van Belle V, Pelckmans K, Suykens JA, and Van Huffel S

Subjects
Algorithms, Breast Neoplasms diagnosis, Breast Neoplasms drug therapy, Breast Neoplasms metabolism, Epidemiologic Research Design, Female, Humans, Kaplan-Meier Estimate, Least-Squares Analysis, Neural Networks, Computer, Oligonucleotide Array Sequence Analysis, Principal Component Analysis, Prognosis, Proportional Hazards Models, Recurrence, Risk, Artificial Intelligence, Survival Analysis
Abstract

This work studies a new survival modeling technique based on least-squares support vector machines. We propose the use of a least-squares support vector machine combining ranking and regression. The advantage of this kernel-based model is threefold: (i) the problem formulation is convex and can be solved conveniently by a linear system; (ii) non-linearity is introduced by using kernels, componentwise kernels in particular are useful to obtain interpretable results; and (iii) introduction of ranking constraints makes it possible to handle censored data. In an experimental setup, the model is used as a preprocessing step for the standard Cox proportional hazard regression by estimating the functional forms of the covariates. The proposed model was compared with different survival models from the literature on the clinical German Breast Cancer Study Group data and on the high-dimensional Norway/Stanford Breast Cancer Data set.

Published
2010
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