Comparison of the ADNEX and ROMA risk prediction models for the diagnosis of ovarian cancer: a multicentre external validation in patients who underwent surgery
Open accessAbstract
Background Several diagnostic prediction models to help clinicians discriminate between benign and malignant adnexal masses are available. This study is a head-to-head comparison of the performance of the Assessment of Different NEoplasias in the adneXa (ADNEX) model with that of the Risk of Ovarian Malignancy Algorithm (ROMA).Methods This is a retrospective study based on prospectively included consecutive women with an adnexal tumour scheduled for surgery at five oncology centres and one non-oncology centre in four countries between 2015 and 2019. The reference standard was histology. Model performance for ADNEX and ROMA was evaluated regarding discrimination, calibration, and clinical utility.Results The primary analysis included 894 patients, of whom 434 (49%) had a malignant tumour. The area under the receiver operating characteristic curve (AUC) was 0.92 (95% CI 0.88-0.95) for ADNEX with CA125, 0.90 (0.84-0.94) for ADNEX without CA125, and 0.85 (0.80-0.89) for ROMA. ROMA, and to a lesser extent ADNEX, underestimated the risk of malignancy. Clinical utility was highest for ADNEX. ROMA had no clinical utility at decision thresholds <27%.Conclusions ADNEX had better ability to discriminate between benign and malignant adnexal tumours and higher clinical utility than ROMA.Clinical trial registration clinicaltrials.gov NCT01698632 and NCT02847832.
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The TransIOTA study externally validated ADNEX for its ability to distinguish benign from malignant tumours, using data from 894 women recruited between 2015 and 2019 in one secondary and five tertiary care centres in four countries.
“The TransIOTA study externally validated ADNEX for its ability to distinguish benign from malignant tumours, using data from 894 women recruited between 2015 and 2019 in one secondary and five tertiary care centres in four countries (Belgium, Italy, Czech Republic, and United Kingdom).”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [32]
There were 434 women with a malignant tumour (prevalence 49%).
“There were 434 women with a malignant tumour (prevalence 49%). 32”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [32]
ADNEX can inform decisions regarding type of surgery (advanced vs conservative) for patients examined at an oncology centre or regarding referral to an oncology centre for patients examined elsewhere.
“The model can inform decisions regarding the type of surgery (advanced vs conservative) for patients examined at an oncology centre or regarding referral to an oncology centre for patients examined elsewhere. 32”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [32]
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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
- Landolfo, C., Ceusters, J., Valentin, L., Froyman, W., Van Gorp, T., Heremans, R., Baert, T., Wouters, R., Vankerckhoven, A., Van Rompuy, A.-S., Billen, J., Moro, F., Mascilini, F., Neumann, A., Van Holsbeke, C., Chiappa, V., Bourne, T., Fischerova, D., Testa, A., Coosemans, A., Timmerman, D., & Van Calster, B.
- Affiliations
- Agostino Gemelli University Polyclinic (IT)
- KU Leuven (BE)
- Lund University (SE)
- Universitair Ziekenhuis Leuven (BE)
- Oncoinvent (Norway) (NO)
- Charles University (CZ)
- Queen Charlotte's and Chelsea Hospital (GB)
- ORCID
- Venue
- British Journal of Cancer
- Published
- 2024-01-19
- Volume
- 130
- Issue
- 6
- Pages
- 934-940
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- PMC
- PMC10951363
- OpenAlex
- W4391014881
- Cited by
- 14
- References
- 54
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 4
- FWCI
- 5.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- British Journal of Cancer (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Fonds Wetenschappelijk Onderzoek (18B2921N)
- Kom Op Tegen Kanker Internal Funds
- Linbury Trust Grant
- Vetenskapsrådet (K2014-99X-22475-01-3)
- the Malmö General Hospital Foundation for fighting against cancer Avtal om läkarutbildning och forskning (ALF)-medel Landstingsfinansierad Regional Forskning
- DH | National Institute for Health Research
- 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 › Surgery › Intraperitoneal and Appendiceal Malignancies
- Keywords
- ovarian cancer, ADNEX model, ROMA index, adnexal tumors, risk prediction model, CA125, benign versus malignant differentiation, external validation, model discrimination, model calibration, clinical utility
- MeSH terms
- Adnexal Diseases, Algorithms, Female, Humans, Ovarian Neoplasms, Retrospective Studies, Sensitivity and Specificity, Ultrasonography, CA-125 Antigen
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