Comparison of the ADNEX and ROMA risk prediction models for the diagnosis of ovarian cancer: a multicentre external validation in patients who underwent surgery

Chiara Landolfo, Jolien Ceusters, Lil Valentin, Wouter Froyman, Toon Van Gorp, Ruben Heremans, Thaïs Baert, Roxanne Wouters, Ann Vankerckhoven, Anne Sophie Van Rompuy, Jaak Billen, Francesca Moro, Floriana Mascilini, Adam Neumann, Caroline Van Holsbeke, Valentina Chiappa, Tom Bourne, Daniela Fischerova, Antonia Testa, An CoosemansDirk Timmerman, Ben Van Calster

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)934-940
JournalBritish Journal of Cancer
Volume130
Issue number6
Early online date2024
DOIs
Publication statusPublished - 2024

Subject classification (UKÄ)

  • Gynaecology, Obstetrics and Reproductive Medicine

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