Neoplasms

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Gland and Hormone Related Diseases

All Conditions

Symptoms and General Pathology

Rare Diseases

Ovarian Neoplasms

Genital Diseases, Female

Endocrine Gland Neoplasms

Cysts

Ovarian Cysts

Genital Neoplasms, Female

Urogenital Neoplasms

Endocrine System Diseases

Genital Diseases

Adnexal Diseases

Ovarian Diseases

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Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Ovarian Cancer

Standardized transvaginal ultrasound examination

Dirk Timmerman, MD, PhD

Wouter Froyman, MD

Dirk Timmerman, PhD

Tom Bourne, MD, PhD

Ben Van Calster, MSc, PhD

Ignace Vergote, MD, PhD

Lil Valentin, MD, PhD

Antonia C Testa, MD, PhD

Sabine Van Huffel, MSc, PhD

The ability of different methods to discriminate between benign and malignant adnexal masses has been compared in a meta-analysis showing that the IOTA Simple Rules and the IOTA logistic regression model LR2 were superior to RMI and to all other methods for predicting malignancy in an adnexal mass included in the meta-analysis. However, a fair comparison of methods requires them to be applied on the same tumor population.

The general objective of this study is to prospectively validate the Simple Rules, ADNEX, the Simple Rules risk model, LR2, and RMI on a large multicenter dataset to allow direct comparison of these tools.

IOTA7 is an international multicenter prospective observational study including different types of ultrasound centers and examiners with different levels of ultrasound experience. In total, about 1700 adnexal masses with histological outcome will be included in IOTA 7.

Ovarian cancer is a common and lethal disease for which early detection and treatment in high volume centers and by specialized clinicians is known to improve survival. Hence, accurate methods to preoperatively characterize the nature of an ovarian tumor are pivotal.

The best ultrasound method for discrimination between benign and malignant adnexal masses is subjective assessment, i.e. subjective evaluation of ultrasound findings. Subjective assessment, however, requires a very experienced ultrasound examiner. More objective methods may be preferred by less experienced examiners who are not confident with using subjective assessment. The Risk of Malignancy Index (RMI) is one such method. There are also more recently developed methods. The International Ovarian Tumor Analysis (IOTA) group have created logistic regression models (LR1, LR2, and the ADNEX model) including clinical and ultrasound information to calculate the likelihood of malignancy in adnexal masses.

The IOTA group has also suggested simple ultrasound rules that can be used to classify adnexal masses as benign or malignant.

The ability of different methods to discriminate between benign and malignant adnexal masses has been compared in a meta-analysis showing that the IOTA Simple Rules and the IOTA logistic regression model LR2 were superior to RMI and to all other methods for predicting malignancy in an adnexal mass included in the meta-analysis. However, a fair comparison of methods requires them to be applied on the same tumor population.

The general objective of this study is to prospectively validate the Simple Rules, ADNEX, the Simple Rules risk model, LR2, and RMI on a large multicenter dataset to allow direct comparison of these tools. The patients will be examined by operators of varying levels of experience, such that the investigators can study how experience might affect diagnostic performance.

IOTA7 is an international multicenter prospective observational study including different types of ultrasound centers and examiners with different levels of ultrasound experience. In total, about 1700 adnexal masses with histological outcome will be included in IOTA 7.

Patients with a known or suspected adnexal mass examined with transvaginal (or transrectal if vaginal is not possible) ultrasound by an IOTA certified ultrasound examiner and confirmed to have an adnexal mass judged not to be physiological but likely to undergo surgery (primarily planned for surgical management based on subjective assessment by the ultrasound investigator) will be recruited consecutively Upon inclusion in the study, an oral or written (depending on the requirements of the local Ethics Committee) informed consent is obtained from the patient.

Data collection is done through the web-based clinical data miner (CDM) software. Data are stored on a secure server.

First, clinical information about the patient is entered into CDM. Second, the ultrasound examiner provides a diagnosis based on subjective assessment, and notes the suggested management. Third, detailed ultrasound information needed for the different models to be validated is entered. When these data have been frozen (so they can no longer be changed) the examiner gets access to the results of the Simple Rules and ADNEX. The ultrasound examiner then records whether these results make him/her change the management suggested on the basis of subjective assessment. If so the new management is specified.

Gold standard for validation of discriminative ability and calibration is the histology of the adnexal mass within 180 days after the ultrasound examination following surgical removal by laparotomy or laparoscopy as considered appropriate by the surgeon. In case of malignancy, the stage of the malignant tumors using the classification of the International Federation of Gynecology and Obstetrics (FIGO) is noted.

Prospective Validation and Comparison of Different Ultrasound Methods for Discrimination Between Benign and Malignant Ovarian/Tubal Masses Prior to Surgery

Prospective Validation and Comparison of Subjective Assessment, the International Ovarian Tumor Analysis (IOTA) ADNEX Model, Logistic Regression Model LR2, Simple Rules and the Risk of Malignancy Index (RMI) for Discrimination Between Benign and Malignant Adnexal Masses in the Hands of Ultrasound Examiners With Different Levels of Experience

KU Leuven

Prof. Dr.

Spots Global Cancer Trial Database for logistic regression models