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

Protocol registration

We report this study according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, online supplemental file 2) and TRIPOD-SRMA (Transparent Reporting of multivariable prediction models for Individual Prognosis Or Diagnosis: checklist for Systematic Reviews and Meta-Analyses, online supplemental file 3) checklists.22 23

Eligibility criteria

Any study that carried out an external validation to evaluate the performance of the ADNEX model, based on any study design and any study population, was eligible for inclusion in the systematic review. Exclusion criteria were studies that did not evaluate the performance of the ADNEX model; studies that only evaluated the predictive performance of updated versions of ADNEX; studies where only an abstract was available, or the full text could not be obtained; and case studies presenting the performance of ADNEX for individual patients (this criterion was not prespecified in the protocol but was added post hoc on review of the search results). Updating can refer to recalibration, refitting, or extension with additional predictors.24 Studies that conducted comparisons of ADNEX with other models, reporting performance metrics, were eligible for inclusion.

Information sources and search strategy

We created a search string and overall search strategy with the help of biomedical reference librarians from the KU Leuven Libraries. We searched the electronic databases Medline (through PubMed), Embase, Web of Science, and Scopus for published articles, and Europe PMC for preprints. The search dates were from the publication of the first ADNEX paper (15 October 2014) to 15 May 2023 (date when the final search was run). We also screened all articles citing the original ADNEX paper.5 The reference lists of relevant review and opinion articles retrieved by the search strings were checked for other potentially eligible articles. Forward and backward snowballing (forward and back cross reference checking) of the included articles was performed to identify additional publications.25 Language was not restricted, but for papers in languages other than English, Spanish, Dutch, French, or Swedish, we used an automatic translation tool (deepl.com) to decide whether to include a paper and to extract information. Online supplemental material S2 shows the full search strategy.

Study selection

The studies we identified in our search were imported into Zotero reference manager, where they were automatically deduplicated. The deduplicated records were then imported into the Rayyan web application for manual deduplication (by LB) and subsequent screening of the title and abstract by two independent authors. Disagreements were resolved by discussion between the two authors (LB and AL).26

Three of the authors (BVC, LV, and DT) were members of the IOTA group that developed ADNEX, so we divided the studies into those that were linked or not linked to IOTA. A study was linked to IOTA if it was coauthored by a member of the IOTA steering committee (online supplemental material S3). IOTA linked papers, as well as a few others with a potential conflict of interest (ie, including authors that are or were IOTA collaborators), were independently assessed by two of the authors (PD and GSC, medical statisticians with expertise in prediction modelling and unrelated to IOTA). All other studies were independently assessed (by LB and AL). Disagreements were resolved by discussion between reviewers, and for the non-IOTA papers, by discussion with authors BVC, LV, and JYV.

Data extraction and data items

Data were extracted and entered into a standardised data extraction form in Microsoft Excel. Data extraction focused on the general and design characteristics of the studies, target population, reference standard, sample size, performance results, reporting quality, methodological quality, and risk of bias (online supplemental table S1). The extraction form was adapted from the CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) tools, and PROBAST (Prediction model Risk Of Bias Assessment Tool).21 27 28

To describe the performance of the model, we extracted information on any reported measure related to discrimination, calibration, diagnostic accuracy, or clinical utility. The reference standard could be binary (eg, benign v malignant) or multinomial (eg, the five tumour types predicted by ADNEX). Performance data were extracted for all reported validations (ie, for ADNEX with and without CA125), subgroup analyses, sensitivity analyses, and for multicentre studies, results specific to each centre. For each study, we assessed the reporting of all TRIPOD items that were applicable to the external validation studies (online supplemental table S2). We also checked PROBAST’s signalling questions and evaluated risk of bias for each subdomain (participants, predictors, outcome, and analysis) and overall. We included our rationale for classification of the risk of bias.

We contacted study authors to obtain further information or results when centre specific results were not reported in multicentre studies, type of centre was not explicitly reported (if no response, the clinical coauthors, JYV, DT, and LV, classified the centre), overall performance was reported but not performance by menopausal status, or performance was not reported for patients who underwent surgery separately in studies that included patients who were managed surgically and non-surgically. Online supplemental table S1 and Open Science Framework repository (extraction sheet, https://osf.io/jtsvd/) have details on all extracted items.29

Statistical analysis and quantitative data synthesis

Data were summarised with descriptive statistics and data visualisations. Meta-analysis of performance, based on specific results for each centre in multicentre studies, where possible, was conducted with the random effects meta-analysis method. Meta-analysis was done separately for ADNEX with and without CA125. We used 95% confidence intervals for the summary performance and assessed heterogeneity with τ² and 95% prediction intervals. Online supplemental material S4-6 have details on the statistical methodology, including meta-analysis methods, explanations of net benefit and relative utility, and assessment of publication bias.

Meta-analysis of the AUC for benign versus malignant tumours was done on the logit scale. Meta-analysis for sensitivity and specificity was also performed on the logit scale with a random effects meta-analysis.30 Because the meta-analysis was conducted only for the 10% threshold for the risk of malignancy, we did not need the 95% confidence ellipse in receiver operating characteristic curve space, so we did not use the bivariate random effects model as specified in the protocol. The 10% threshold was most commonly used in the articles included in our systematic review, and is a commonly recommended threshold.4 Meta-analysis of net benefit31 and relative utility32 at the 10% risk of malignancy threshold was performed with bayesian trivariate random effects meta-analyses of sensitivity, specificity, and prevalence of malignancy.33 For bayesian methods, 95% credible intervals are reported instead of 95% confidence intervals. With the bayesian approach, the probability that the model is useful in a new centre can be estimated (ie, the probability that relative utility is >0). To deal with multinomial discrimination performance, we conducted a meta-analysis of AUC values between pairs of tumour outcomes (pairwise AUC values) on a logit scale. We only included studies that used the conditional risk method to calculate pairwise AUC values.34

Subgroups were defined based on geographical location, type of centre, and menopausal status. Sensitivity analyses were based on judgment of the risk of bias and on whether the study was linked to IOTA. As prespecified in the protocol, we only conducted a meta-analysis of performance if at least three estimates in a specific analysis could be retrieved from the included studies. To assess the association between prevalence of malignancy and AUC, and sensitivity and specificity at the 10% risk of malignancy threshold, we used meta-regression.35

Reporting bias and small study effects were visually explored with funnel plots adapted for the AUC. The body of evidence was assessed with an adapted version of GRADE (Grading of Recommendations Assessment, Development and Evaluation).36 All analyses were performed in R version 4.2.2 with the package metamisc for AUC, meta and mada for sensitivity and specificity, and rjags for net benefit and relative utility.37–40 Bayesian methods were computed with JAGS version 4.3.1.40

Patient and public involvement

Patients and the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. Preliminary results of the research were presented at the ISUOG World Congress in Seoul (October 2023). As this study was a systematic review, there was no collection of patients' data, and we use only the information publicly available in the published papers.

Critical appraisal: reporting completeness and risk of bias

Completeness of reporting the TRIPOD items was assessed for the 63 validations. Adherence to TRIPOD items was, on average, 61%: studies reported a mean of 16.5 out of 27 items (figure 2 and online supplemental figure S1). The least commonly reported items were comparison of demographics, predictors, and outcome between the model development and external validation data (item 13c; 5%), reporting of performance measures with confidence intervals (item 16; 11%), specification of all performance measures (item 10d; 11%), rationale for study sample size (item 8; 13%), and description of how missing data were handled (item 9; 22%).

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Fifty seven (90%) of 63 validations were rated as having a high risk of bias, two (3%) as uncertain risk of bias, and four (6%) as low risk of bias (figure 3, online supplemental figures S2-5, and Open Science Framework repository, extraction sheet https://osf.io/jtsvd/).29 Forty three (68%) validations had a high risk of bias for the participant domain, mostly by having incomplete data as an exclusion criterion. Fifty seven (90%) validations had a high risk of bias for the analysis domain, mostly because of small sample size (69%; ie, <100 tumours in the smallest group), not including all participants in the analysis (85%), inappropriate handling of missing data (82%), and incomplete evaluation of model performance (89%, in most instances by not reporting an assessment of calibration).

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Adherence to TRIPOD items in 36 studies without a focus on selected histologies or clinical subgroups was, on average, 65% (17.47 of 27 items). In these studies, two had a low risk of bias, one had an unclear risk of bias, and 33 had a high risk of bias.

Meta-analyses of performance

The meta-analysis included studies without post hoc selection based on histology and without a focus on a clinical subgroup only (n=36) that reported the meta-analysed metrics. Only two studies included in the meta-analysis used tumour as the unit of study.42 51 The AUC for benign versus malignant tumours in patients who underwent surgery was reported in 12 studies (6309 tumours, 31 centres, and 13 countries) for ADNEX without CA125 (online supplemental table S4 and table S6), and the summary AUC was 0.93 (95% confidence interval 0.91 to 0.94, 95% prediction interval 0.85 to 0.98) (table 2 and figure 4).6 45 49 53 54 60 66–69 72 83 Twenty one studies (9202 tumours, 43 centres, and 18 countries) reported the AUC for benign versus malignant tumours in patients who underwent surgery for ADNEX with CA1256 41 42 46 49 53 58 60 63 64 67–69 72 74–76 78 80 82 83 (online supplemental table S4 and table S6) and the summary AUC was 0.93 (95% confidence interval 0.92 to 0.94, 95% prediction interval 0.85 to 0.98) (table 2 and figure 5).

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Sensitivity and specificity at the 10% risk of malignancy threshold in patients who underwent surgery were reported in 10 studies for ADNEX without CA125 (online supplemental table S4 and table S7).6 45 49 54 60 66 68 69 72 83 The summary sensitivity and specificity were 0.93 (95% confidence interval 0.90 to 0.95, 95% prediction interval 0.73 to 0.99) and 0.75 (95% confidence interval 0.70 to 0.79, 95% prediction interval 0.46 to 0.91), respectively (figure 6 and online supplemental table S8). For ADNEX with CA125, sensitivity and specificity at the 10% risk of malignancy threshold in patients who underwent surgery were reported in 23 studies (online supplemental table S4 and table S7).6 8 41 45 49 53 58–60 63 66–69 71 72 74 76 78 80–83 The summary sensitivity and specificity were 0.94 (95% confidence interval 0.92 to 0.95, 95% prediction interval 0.80 to 0.98) and 0.77 (95% confidence interval 0.73 to 0.81, 95% prediction interval 0.47 to 0.93), respectively (figure 7 and online supplemental table S8).

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Net benefit and relative utility were calculated based on studies that presented sensitivity and specificity at the 10% risk of malignancy threshold in patients who underwent surgery (online supplemental table S7). For ADNEX without CA125, the summary net benefit was 0.28 (95% confidence interval 0.21 to 0.35, 95% prediction interval 0.05 to 0.68) and the summary relative utility was 0.50 (95% confidence interval 0.37 to 0.62, 95% prediction interval −0.44 to 0.79). The probability that the model is clinically useful in a random new centre was estimated to be 91% (online supplemental table S9, figure S6, and figure S7). For ADNEX with CA125, the summary net benefit was 0.28 (95% confidence interval 0.22 to 0.33, 95% prediction interval 0.05 to 0.65), and the summary relative utility was 0.54 (95% confidence interval 0.45 to 0.61, 95% prediction interval −0.12 to 0.78). The probability that the model is clinically useful in a random new centre was estimated to be 95% (online supplemental table S9, figure S8,9).

Pairwise AUC values in patients who underwent surgery were reported in four studies for ADNEX without CA125 and in five studies for ADNEX with CA125 (online supplemental table S10).6 68 72 80 83 The summary pairwise AUC values for ADNEX without CA125 ranged from 0.66 (stage II-IV primary invasive v metastatic) to 0.97 (benign v stage II-IV primary invasive) (online supplemental table S11). For ADNEX with CA125, the summary pairwise AUC values ranged from 0.72 (borderline v stage I primary invasive) to 0.98 (benign v stage II-IV primary invasive).

The AUC for benign versus malignant tumours in patients managed with and without surgery combined was reported in two studies (5167 tumours, 18 centres, and eight countries) with a summary estimate of 0.94 (95% confidence interval 0.93 to 0.96, 95% prediction interval 0.88 to 0.99) for ADNEX with CA125 (table 2).6 7 ADNEX without CA125 was assessed in only one study (4905 tumours, 17 centres, and seven countries).6 This study reported a summary AUC of 0.94 (95% confidence interval 0.91 to 0.95, 95% prediction interval 0.82 to 0.98).

Table 2 and online supplemental tables S6-9 present the sensitivity and subgroup results for AUC, specificity, sensitivity, net benefit, and relative utility. These results showed that the findings were robust (AUC values ranged from 0.90 to 0.95 across all analyses) and clinical utility was suggested in all subgroups. When limiting the analyses to studies with a low risk of bias, summary AUC values were 0.93 (with CA125) and 0.91 (without CA125). Sensitivity was higher and specificity lower in oncology versus non-oncology centres and in patients who were postmenopausal versus premenopausal. In line with these findings, meta-regression suggested that the prevalence of malignancy was not related to AUC but was related positively to sensitivity and negatively to specificity (online supplemental figures S10-12).

Meta-analysis of calibration only in patients who underwent surgery was not feasible because only one study reported calibration slope and intercept.6 Four studies presented a calibration plot; in three studies72 80 83 the estimated risks were close to the observed risks and in one study6 the risk of malignancy was slightly underestimated (online supplemental material S8).

Based on the subdomains of the GRADE assessment, we found that the risk of bias in the studies included in this meta-analysis was a substantial limitation affecting the certainty of our meta-analysis results. Only two studies (representing 5511 tumours or 32% of the total tumours included in this review) were not classified as having a high risk of bias,6 72 but the sensitivity analysis according to risk of bias showed consistent findings (table 2, and online supplemental table S8 and table S9). Funnel plots for AUC did not suggest publication bias (online supplemental figure S13).

Principal findings

The ADNEX model performed well in classifying tumours and in differentiating between benign and malignant tumours across various settings and populations. Our results indicated that ADNEX was clinically useful at the 10% risk of malignancy threshold (eg, to help decide whether a patient should be referred for assessment to a gynaecological oncology centre). We found deficiencies in study reporting, and most studies were judged to have a high risk of bias, but our sensitivity analyses indicated that performance was almost identical in studies with a low risk and high risk of bias.

For ADNEX with CA125, the AUC was 0.93 based on all studies, versus 0.93 when based only on studies with a low risk of bias. For ADNEX without CA125, the AUC values were 0.93 (all studies) and 0.91 (low risk of bias only). High risk of bias was mainly caused by small sample size, no assessment of calibration performance, and unjustified use of complete case analysis. Small sample size implies that the estimated AUC value is less precise but it does not systematically affect the AUC, unless publication bias exists. The funnel plots did not suggest publication bias. Absence of calibration does not affect the AUC. Using complete cases, in terms of CA125 or other predictors, might lead to underestimation of AUC because missing values tend to be associated with the examiner's subjective impression that the tumour is benign.84 Complete case analysis would then tend to exclude clearly benign tumours, which would make the sample more homogeneous and reduce the AUC. Our results, however, suggest that the effect of complete case analysis on performance might have been minimal. The effect on calibration could not be assessed. Taken together, we believe that the results of our meta-analysis are reliable.

Strengths and limitations of this study

Strengths of our systematic review include meta-analysis of ADNEX both as a risk model and as a diagnostic test, and the thorough critical appraisal of risk of bias and reporting quality with recommended checklists.21 28 Our study also had limitations. Some of the authors of this study had a conflict of interest because of their involvement in developing ADNEX or in some of the included external validation studies. To deal with this conflict of interest, independent researchers with expertise in study methodology and prediction modelling evaluated the IOTA related studies. A limitation of our findings (but not of our study) is that calibration performance was reported in only four studies and therefore meta-analysis of calibration was not possible.

Comparison with other studies

Previous systematic reviews have conducted meta-analyses of the diagnostic performance of ADNEX with CA125.14–18 These studies used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool88 to assess risk of bias, and found that 0-64% of studies had a high risk of bias in at least one domain. We identified 45 of 47 studies with a high risk of bias with PROBAST, designed for the appraisal of risk prediction models. None of the previous meta-analyses of ADNEX included clinical utility, calibration, or AUC. Our results align with those of other systematic reviews of risk prediction modelling studies in various domains. These studies consistently indicated that reporting in the original studies was poor and that many studies had a high risk of bias.89–95 The results in terms of sensitivity and specificity of ADNEX in our meta-analysis, however, were similar to those in the other meta-analyses of ADNEX performance.

Study implications

ADNEX is intended for use by gynaecologists to help them decide on the most appropriate management of an adnexal mass detected on ultrasound. Our findings support the use of ADNEX in choosing between surgery and conservative follow-up. Conservative follow-up might be appropriate in patients with a low risk of malignancy (eg, <1%, based on meta-analysis of patients managed with and without surgery). Our results also support the use of ADNEX in deciding on the most appropriate management if conservative management is not suitable (eg, surgery at a local hospital if the risk of malignancy is <10% or referral to an oncology centre if the risk is>10%; based on meta-analysis of patients who underwent surgery).4

ADNEX can also be helpful in deciding on the management of a suspected malignancy (ie, investigations to find the primary tumour if a metastasis in the ovary is likely, or fertility sparing surgery if a borderline tumour is likely; based on meta-analysis in patients who underwent surgery). Because the AUC values of ADNEX with and without CA125 were similar, and because adding CA125 mainly helps to distinguish between different types of malignant tumours, we argue that the main use of ADNEX without CA125 is to help decide whether conservative follow-up, surgery in a local centre, or referral to an oncology centre is appropriate. The main use of ADNEX with CA125 is to help decide on the optimal management of a tumour suspected to be malignant, because it differentiates better between malignant subtypes than ADNEX without CA125.

Although our findings suggest that ADNEX is clinically useful, well conducted validations of any model are always of value to monitor its performance in diverse regions and clinical settings, and over time.96 To improve the performance of ADNEX even more, efforts to update the ADNEX formula are of interest.96 97 If further validation studies are conducted, we recommend including a validation of ADNEX without CA125, using a sufficiently large sample that allows calibration and multinomial discrimination to be assessed, and including patients irrespective of whether they are managed surgically or non-surgically (despite the challenges about reference standard for patients managed without surgery).34 98 Methodological recommendations for validation studies include using available tools to guarantee adequate sample size, describe missing data in detail and use methods such as imputation when needed, and assess calibration performance.24 97 99–101 Adherence to the TRIPOD reporting checklists is important to maximise the value of the validation study (www.tripod-statement.org).

Conclusions

ADNEX has been validated in many studies, with AUC values >0.90 in differentiating between benign and malignant tumours in various settings, and with strong results for its clinical utility at the 10% risk of malignancy threshold. Because of the lack of assessment of calibration in most studies, evaluating the accuracy of the estimated risks in a substantial way was not possible in this study.