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

Abstract

ObjectivesTo 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.DesignSystematic review and meta-analysis of external validation studiesData sourcesMedline, Embase, Web of Science, Scopus, and Europe PMC, from 15 October 2014 to 15 May 2023.Eligibility criteria for selecting studiesAll 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.Results47 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).ConclusionsThe 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 registrationPROSPERO CRD42022373182.

Enabling preprint discovery, evaluation, and analysis with Europe PMC

“…As a result, Europe PMC adoption as a search engine for preprints in systematic reviews of the research literature has been referenced by numerous studies [62][63][64][65][66][67][68][69][70][71].…”

“…Systematic reviewers can export all or a selection of search results in various data formats including XML and CSV. As a result, Europe PMC adoption as a search engine for preprints in systematic reviews of the research literature has been referenced by numerous studies [62][63][64][65][66][67][68][69][70][71].…”

Ultrasound-Based Deep Learning Models Performance versus Expert Subjective Assessment for Discriminating Adnexal Masses: A Head-to-Head Systematic Review and Meta-Analysis

“…For this reason, scoring systems and logistic models, such as the Risk of Malignancy index, IOTA simple rules, Risk Assessment based on Simple rules, LR1 model, LR2 model, ADNEX model, GI-RADS and O-RADS classification systems were developed in an attempt to overcome this limitation [21,22,[50][51][52][53][54]. No doubt, these approaches have produced a significant advance in the ultrasound discrimination of adnexal masses, allowing lessexperienced examiners achieve good results in terms of diagnostic performance [55][56][57][58][59][60][61][62][63]. However, the expert examiner is still the most cost-effective approach [49].…”

Added value of serum proteins to clinical and ultrasound information in predicting the risk of malignancy in ovarian tumors

“…ADNEX has an AUC of 0.93 to discriminate between benign and any type of malignant tumors in patients scheduled for surgery or expectant management. [6][7] The protein biomarker CA125 is an optional predictor in ADNEX. Using the version with CA125 mainly improves the discrimination of stage II-IV invasive tumors from stage I invasive and secondary metastatic tumors.…”