ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies
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Abstract
Objectives: To conduct a systematic review of studies externally validating the ADNEX (Assessment of Different Neoplasias in the adnexa) model for diagnosis of ovarian cancer and to present a meta-analysis of its performance. Design: Systematic review and meta-analysis of external validation studies. Data sources: Medline, Embase, Web of Science, Scopus, and Europe PMC, from 15 October 2014 to 15 May 2023. Eligibility criteria for selecting studies: All external validation studies of the performance of ADNEX, with any study design and any study population of patients with an adnexal mass. Two independent reviewers extracted the data. Disagreements were resolved by discussion. Reporting quality of the studies was scored with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) reporting guideline, and methodological conduct and risk of bias with PROBAST (Prediction model Risk Of Bias Assessment Tool). Random effects meta-analysis of the area under the receiver operating characteristic curve (AUC), sensitivity and specificity at the 10% risk of malignancy threshold, and net benefit and relative utility at the 10% risk of malignancy threshold were performed. Results: 47 studies (17 007 tumours) were included, with a median study sample size of 261 (range 24-4905). On average, 61% of TRIPOD items were reported. Handling of missing data, justification of sample size, and model calibration were rarely described. 91% of validations were at high risk of bias, mainly because of the unexplained exclusion of incomplete cases, small sample size, or no assessment of calibration. The summary AUC to distinguish benign from malignant tumours in patients who underwent surgery was 0.93 (95% confidence interval 0.92 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX with the serum biomarker, cancer antigen 125 (CA125), as a predictor (9202 tumours, 43 centres, 18 countries, and 21 studies) and 0.93 (95% confidence interval 0.91 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX without CA125 (6309 tumours, 31 centres, 13 countries, and 12 studies). The estimated probability that the model has use clinically in a new centre was 95% (with CA125) and 91% (without CA125). When restricting analysis to studies with a low risk of bias, summary AUC values were 0.93 (with CA125) and 0.91 (without CA125), and estimated probabilities that the model has use clinically were 89% (with CA125) and 87% (without CA125). Conclusions: The results of the meta-analysis indicated that ADNEX performed well in distinguishing between benign and malignant tumours in populations from different countries and settings, regardless of whether the serum biomarker, CA125, was used as a predictor. A key limitation was that calibration was rarely assessed. Systematic review registration: PROSPERO CRD42022373182.
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Meta-analysis and meta-regression methods can be used to quantify and understand heterogeneity in performance across external validation studies.
“Meta-analysis and meta-regression methods can be used to quantify and understand heterogeneity in performance across external validation studies.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [106]
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Performance of the IOTA ADNEX Model on Selected Group of Patients with Borderline Ovarian Tumours
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Performance of the IOTA ADNEX model combined with HE4 for identifying early-stage ovarian cancer
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Comparison of Ultrasound and Tumor Marker CA125 in Diagnosis of Adnexal Mass Malignancies
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Flagged first, then by citations
Research works printed but not linked: 1
- [49]Díaz L, Santos M, Zambrano B, et al. Ovarian tumors: risk of malignancy and IOTA ADNEX model indexes. No technology Doppler diagnostic options [Tumores de Ovario: Índices de Riesgo de Malignidad Y Modelo ADNEX de IOTA. Opciones Diagnósticas sin Tecnología Doppler]. Rev Obstet Ginecol Venez 2017; 77: 181– 95.
Cited by
Sorted by citations
Van Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100916 · Cited by 112 · see citing works
Self-citation
- Authors
- Barreñada, L., Ledger, A., Dhiman, P., Collins, G., Wynants, L., Verbakel, J. Y., Timmerman, D., Valentin, L., & Van Calster, B.
- Affiliations
- KU Leuven (BE)
- University of Oxford (GB)
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford, UK
- Maastricht University (NL)
- Universitair Ziekenhuis Leuven (BE)
- Lund University (SE)
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- ORCID
- Venue
- BMJ Medicine
- Publisher
- BMJ
- Published
- 2024-02
- Volume
- 3
- Issue
- 1
- Pages
- e000817
- ISSN
- 2754-0413 (ISSN-L)
- PMC
- PMC10875560
- OpenAlex
- W4391900026
- Cited by
- 42
- References
- 110
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 9
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMJ Medicine (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- University of Birmingham Research Portal (University of Birmingham) (publishedVersion, cc-by)
- Lirias (KU Leuven) (submittedVersion, cc-by)
- Lirias (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fonds Wetenschappelijk Onderzoek (G097322N)
- Cancer Research UK (C49297/A27294, PRCPJT-Nov21\100021)
- NIHR (N/A)
- Oxford Health NHS Foundation Trust (N/A)
- KU Leuven (C24M/20/064)
- Topics
- Health Sciences › Medicine › Reproductive Medicine › Ovarian cancer diagnosis and treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
- Health Sciences › Medicine › Oncology › Cancer Risks and Factors
- Keywords
- ADNEX model, ovarian cancer diagnosis, adnexal mass, external validation, CA125, risk prediction model, diagnostic accuracy, risk of bias, net benefit, relative utility, model calibration, benign versus malignant differentiation, risk of overdiagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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