4.7 Article

Evaluating the risk of ovarian cancer before surgery using the ADNEX model: a multicentre external validation study

Journal

BRITISH JOURNAL OF CANCER
Volume 115, Issue 5, Pages 542-548

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/bjc.2016.227

Keywords

diagnostic imaging; ovarian neoplasm; statistical models; ultrasonography

Categories

Funding

  1. National Institute for Health Research (NIHR) Biomedical Research Centre based at Imperial College Healthcare NHS Trust
  2. FWO [G049312N, G0B4716N]
  3. Internal Funds KU Leuven Grant [C24/15/037]
  4. Imperial College London

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Background: The International Ovarian Tumour Analysis (IOTA) group have developed the ADNEX (The Assessment of Different NEoplasias in the adneXa) model to predict the risk that an ovarian mass is benign, borderline, stage I, stages II-IV or metastatic. We aimed to externally validate the ADNEX model in the hands of examiners with varied training and experience. Methods: This was a multicentre cross-sectional cohort study for diagnostic accuracy. Patients were recruited from three cancer centres in Europe. Patients who underwent transvaginal ultrasonography and had a histological diagnosis of surgically removed tissue were included. The diagnostic performance of the ADNEX model with and without the use of CA125 as a predictor was calculated. Results: Data from 610 women were analysed. The overall prevalence of malignancy was 30%. The area under the receiver operator curve (AUC) for the ADNEX diagnostic performance to differentiate between benign and malignant masses was 0.937 (95% CI: 0.915-0.954) when CA125 was included, and 0.925 (95% CI: 0.902-0.943) when CA125 was excluded. The calibration plots suggest good correspondence between the total predicted risk of malignancy and the observed proportion of malignancies. The model showed good discrimination between the different subtypes. Conclusions: The performance of the ADNEX model retains its performance on external validation in the hands of ultrasound examiners with varied training and experience.

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