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

Objectives: To conduct a systematic review of studies externally validating the ADNEX model for ovarian cancer diagnosis and perform a meta-analysis of its performance. Design: Systematic review, meta-analysis Data sources: Medline, EMBASE, WOS, Scopus, and EuropePMC up to 15/05/2023. Review methods: We included external validation studies of the performance of ADNEX using any study design and any study population comprising patients with an adnexal mass. Two independent reviewers extracted data. Disagreements were resolved through discussion. Reporting quality of the studies was scored using the TRIPOD reporting guideline and methodological conduct and risk of bias using the PROBAST tool. We performed random effects meta-analysis of the AUC, sensitivity and specificity at the 10% risk of malignancy threshold, and Net Benefit and Relative Utility at the 10% risk of malignancy threshold. Results: We included 47 studies (17,007 tumours) with median study sample size 261 (range 24-4905). On average, 61% of TRIPOD items were reported. Handling of missing data, sample size justification, and model calibration were rarely described. 91% of validations were at high risk of bias, mainly due to the unexplained exclusion of incomplete cases, low sample size, or absent calibration assessment. The summary AUC to distinguish benign from malignant tumours in operated patients was 0.93 (95% CI 0.92-0.94, 95% prediction interval 0.85-0.98) for ADNEX with CA125 as a predictor (9202 tumours, 43 centres, 18 countries, 21 studies) and 0.93 (95% CI 0.91-0.94, 95% prediction interval 0.85-0.98) for ADNEX without CA125 (6309 tumours, 31 centres, 13 countries, 12 studies). The estimated probability that the model has clinical utility in a new centre was 95% (with CA125) and 91% (without CA125). When restricting analysis to studies at low risk of bias, summary AUCs were 0.93 (with CA125) and 0.91 (without CA125), and estimated probabilities that the model has clinical utility were 89% (with CA125) and 87% (without CA125). Discussion: ADNEX performed well to distinguish benign from malignant tumours in populations from different countries and settings regardless of whether CA125 was used or not. A key limitation is that calibration was rarely assessed. Review registration: PROSPERO, CRD42022373182 ### Competing Interest Statement BVC, LV and DT are members of the steering committee of the International Ovarian Tumour Analysis (IOTA) consortium and were involved in the development of the ADNEX model. BVC and DT report consultancy work done by KU Leuven to help implementing and testing the ADNEX model in ultrasound machines by Samsung Medison, GE Healthcare, Canon Medical Systems Europe, and Shenzhen Mindray Bio-medical Electronics, outside the submitted work. ### Clinical Protocols ### Funding Statement This research was supported by the Research Foundation - Flanders (FWO) under grant G097322N with BVC and DT as supervisors. LW and BVC were supported by Internal Funds KU Leuven (grant C24M/20/064). JYV was supported by the National Institute for Health and Care Research (NIHR) Community Healthcare MedTech and In Vitro Diagnostics Co-operative at Oxford Health NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. GSC is supported by Cancer Research UK (programme grant: C49297/A27294). PD is supported by CRUK (project grant: PRCPJT-Nov21\100021) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This systeamtic review uses only published data about the performance of ADNEX model. This data is available in each of the 47 included articles and it is correctly referenced in the manuscript. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data and code used for the analysis and the generation of the figures is publicly available in Open Science Framework (). [80] This includes the extraction sheet, one script for running the meta-analysis and one script for generating all the figures included in the paper (except PRISMA Flowchart). Note that for some rows in the extraction sheet we deleted the performance measures for the menopausal subgroups because this information was provided by authors upon request therefore it is not publicly available.