Cited by 42Ovarian cancer diagnosis and treatmentBRCA gene mutations in cancerCancer Risks and Factors

ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies

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Barreñada, Lasai, Ledger, Ashleigh, Dhiman, Paula, Collins, Gary, Wynants, Laure, Verbakel, Jan Y, Timmerman, Dirk, Valentin, Lil +1 more·2024·BMJ Medicine

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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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  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.

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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
Venue
BMJ Medicine
Publisher
BMJ
Published
2024-02
Volume
3
Issue
1
Pages
e000817
ISSN
2754-0413 (ISSN-L)
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
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

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