OBJECTIVES: To develop a risk prediction model to preoperatively discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic ovarian tumours. DESIGN: Observational diagnostic study using prospectively collected clinical and ultrasound data. SETTING: 24 ultrasound centres in 10 countries. PARTICIPANTS: Women with an ovarian (including para-ovarian and tubal) mass and who underwent a standardised ultrasound examination before surgery. The model was developed on 3506 patients recruited between 1999 and 2007, temporally validated on 2403 patients recruited between 2009 and 2012, and then updated on all 5909 patients. MAIN OUTCOME MEASURES: Histological classification and surgical staging of the mass. RESULTS: The Assessment of Different NEoplasias in the adneXa (ADNEX) model contains three clinical and six ultrasound predictors: age, serum CA-125 level, type of centre (oncology centres v other hospitals), maximum diameter of lesion, proportion of solid tissue, more than 10 cyst locules, number of papillary projections, acoustic shadows, and ascites. The area under the receiver operating characteristic curve (AUC) for the classic discrimination between benign and malignant tumours was 0.94 (0.93 to 0.95) on temporal validation. The AUC was 0.85 for benign versus borderline, 0.92 for benign versus stage I cancer, 0.99 for benign versus stage II-IV cancer, and 0.95 for benign versus secondary metastatic. AUCs between malignant subtypes varied between 0.71 and 0.95, with an AUC of 0.75 for borderline versus stage I cancer and 0.82 for stage II-IV versus secondary metastatic. Calibration curves showed that the estimated risks were accurate. CONCLUSIONS: The ADNEX model discriminates well between benign and malignant tumours and offers fair to excellent discrimination between four types of ovarian malignancy. The use of ADNEX has the potential to improve triage and management decisions and so reduce morbidity and mortality associated with adnexal pathology.

1st Department of Gynaecological Oncology and Gynaecology Medical University in Lublin Lublin Poland

Clinic of Obstetrics and Gynaecology University of Milan Bicocca San Gerardo Hospital Monza Italy

Department of Development and Regeneration KU Leuven Herestraat 49 box 7003 3000 Leuven Belgium

Department of Development and Regeneration KU Leuven Herestraat 49 box 7003 3000 Leuven Belgium Department of Obstetrics and Gynaecology University Hospitals Leuven Leuven Belgium

Department of Development and Regeneration KU Leuven Herestraat 49 box 7003 3000 Leuven Belgium Department of Obstetrics and Gynaecology University Hospitals Leuven Leuven Belgium Queen Charlotte's and Chelsea Hospital Imperial College London UK

Department of Electrical Engineering KU Leuven Leuven Belgium iMinds Medical Information Technologies KU Leuven Leuven Belgium

Department of Gynaecologic Oncology Istituto Nazionale Tumori Naples Italy

Department of Obstetrics and Gynaecology Azienda Ospedaliero Universitaria di Cagliari Cagliari Italy

Department of Obstetrics and Gynaecology Karolinska University Hospital Stockholm Sweden

Department of Obstetrics and Gynaecology Skåne University Hospital Malmö Lund University Malmö Sweden

Department of Obstetrics and Gynaecology Ziekenhuis Oost Limburg Genk Belgium

Department of Oncology Catholic University of the Sacred Heart Rome Italy

Department of Woman Mother and Neonate Buzzi Children's Hospital Biological and Clinical School of Medicine University of Milan Milan Italy

Gynaecological Oncology Center Department of Obstetrics and Gynaecology Charles University Prague Czech Republic

Gynaecology and Reproductive Medicine Unit S Orsola Malpighi Hospital University of Bologna Bologna Italy

Preventive Gynaecology Unit Division of Gynaecology European Institute of Oncology Milan Italy

Comment In

BMJ. 2014 Nov 18;349:g6689. doi: 10.1136/bmj.g6689. PubMed

Comment In

BMJ. 2014 Nov 18;349:g6691. doi: 10.1136/bmj.g6691. PubMed

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