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. 2024 Feb 17;3(1):e000817.
doi: 10.1136/bmjmed-2023-000817. eCollection 2024.

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

Lasai Barreñada  1 , Ashleigh Ledger  1 , Paula Dhiman  2 , Gary Collins  2 , Laure Wynants  1   3 , Jan Y Verbakel  4   5   6 , Dirk Timmerman  1   7 , Lil Valentin  8   9 , Ben Van Calster  1   6   10
Affiliations

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

Lasai Barreñada et al. BMJ Med. .

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.

Keywords: Obstetrics; Statistics.

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Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from Research Foundation Flanders (FWO), Internal Funds KU Leuven, National Institute for Health and Care Research Community Healthcare MedTech, In Vitro Diagnostics Co-operative at Oxford Health NHS Foundation Trust, Cancer Research UK, and CRUK for the submitted work; 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 implement and test 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; GC is a statistics editor for the BMJ; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart of study inclusions and exclusions
Figure 2
Figure 2
Adherence to reporting the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) items in 63 validations
Figure 3
Figure 3
Risk of bias overall and by subdomain in 63 validations, assessed by PROBAST (Prediction model Risk Of Bias Assessment Tool)
Figure 4
Figure 4
Forest plot of area under the receiver operating curve in studies where the ADNEX (Assessment of Different Neoplasias in the adnexa) model was used without CA125 (cancer antigen 125). Results in the forest plot are centre specific results, so studies with more than one centre can appear multiple times. CI=confidence interval
Figure 5
Figure 5
Forest plot of area under the receiver operating characteristic curve in studies where the ADNEX (Assessment of Different Neoplasias in the adnexa) model was used with CA125 (cancer antigen 125). Results in the forest plot are centre specific results, so studies with more than one centre can appear multiple times. CI=confidence interval
Figure 6
Figure 6
Forest plot of sensitivity and specificity at the 10% risk of malignancy threshold in studies where the ADNEX (Assessment of Different Neoplasias in the adnexa) model was used without CA125 (cancer antigen 125). Results in the forest plot are centre specific results, so studies with more than one centre can appear multiple times. CI=confidence interval
Figure 7
Figure 7
Forest plot of sensitivity and specificity at the 10% risk of malignancy threshold in studies where the ADNEX (Assessment of Different Neoplasias in the adnexa) model was used with CA125 (cancer antigen 125).6 8 41 45 49 53 58–60 63 66–69 71 72 74 76 78 80–83 Results in the forest plot are centre specific results, so studies with more than one centre can appear multiple times. CI=confidence interval
See this image and copyright information in PMC

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