ArticleBMJ (Clinical research ed.)2020
Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study.
Article in BMJ (Clinical research ed.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01698632 (A Multicentre Study to Examine the Short and Long Term Outcomes of the Conservative Management of Benign-looking Adnexal Masses and the Pre-operative Characterisation of Ovarian Tumours), which is not on this map. Cited by 50 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multicentre Study to Examine the Short and Long Term Outcomes of the Conservative Management of Benign-looking Adnexal Masses and the Pre-operative Characterisation of Ovarian Tumours
Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it, 120 citations in OpenAlex.
- Diagnostic accuracy of ultrasound models for assessment of ovarian tumors: systematic review and meta-analysis.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Pooled it
- Pooled it
- Article
- Terms, definitions and measurements to describe the sonographic features of adnexal tumors: updated consensus opinion from the International Ovarian Tumor Analysis (IOTA) Group.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Article
- Clustered flexible calibration plots for binary outcomes using random effects modeling.Research synthesis methods · 2026Article
- Developing and validating ultrasound-based machine-learning models incorporating radiomics features to predict malignancy in adnexal masses.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence-Driven Prognostic Models in Oncology.International journal of molecular sciences · 2026Review
- Evaluating Performance of a Deep Learning-based Artificial Intelligence Model for Ovarian Tumor Classification Using a Multicenter CT Dataset.Radiology. Imaging cancer · 2026Article
- Predicting adverse outcomes in dilated cardiomyopathy using 3D echocardiography: penalised Cox regression versus machine learning.BMC cardiovascular disorders · 2026Article
- Development and validation of an artificial intelligence-based model for diagnosing benign, borderline, and malignant adnexal masses.NPJ precision oncology · 2026Article
- Diagnostic tests for ovarian cancer in premenopausal women with non-specific symptoms (ROCkeTS): prospective, multicentre, cohort study.BMJ (Clinical research ed.) · 2026Article
- The Quantification Paradox in Gynecologic Color Doppler Ultrasound: From Spectral Indices to Microvascular Imaging and Artificial Intelligence.International journal of women's health · 2026Review
- Beyond AUC: a clinician's guide to building and trusting prediction models in oncology-a narrative review.Frontiers in oncology · 2026Review
- Imaging in gynecological disease (29): clinical and ultrasound features of primary ovarian immature teratoma.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Article
- A Simple O-RADS-Based Ultrasound Scoring System for Risk Stratification of Ovarian Cystic Lesions: A Multicenter Study.International journal of women's health · 2026Article
- ADNEX-AI: automated extraction of ultrasound predictors for interpretable ovarian cancer risk stratification.NPJ precision oncology · 2025Article
- Added value of serum proteins to clinical and ultrasound information in predicting the risk of malignancy in ovarian tumors.iScience · 2025Article
- A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow.NPJ digital medicine · 2025Article
- Imaging in gynecological disease (28): clinical and ultrasound characteristics of serous and mucinous cystadenomas in the adnexa.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Article
- Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid morphology on ultrasound.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
31 authors at 20 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the performance of diagnostic prediction models for ovarian malignancy in all patients with an ovarian mass managed surgically or conservatively.
designMulticentre cohort study.
setting36 oncology referral centres (tertiary centres with a specific gynaecological oncology unit) or other types of centre.
participantsConsecutive adult patients presenting with an adnexal mass between January 2012 and March 2015 and managed by surgery or follow-up.
main outcome measuresOverall and centre specific discrimination, calibration, and clinical utility of six prediction models for ovarian malignancy (risk of malignancy index (RMI), logistic regression model 2 (LR2), simple rules, simple rules risk model (SRRisk), assessment of different neoplasias in the adnexa (ADNEX) with or without CA125). ADNEX allows the risk of malignancy to be subdivided into risks of a borderline, stage I primary, stage II-IV primary, or secondary metastatic malignancy. The outcome was based on histology if patients underwent surgery, or on results of clinical and ultrasound follow-up at 12 (±2) months. Multiple imputation was used when outcome based on follow-up was uncertain.
resultsThe primary analysis included 17 centres that met strict quality criteria for surgical and follow-up data (5717 of all 8519 patients). 812 patients (14%) had a mass that was already in follow-up at study recruitment, therefore 4905 patients were included in the statistical analysis. The outcome was benign in 3441 (70%) patients and malignant in 978 (20%). Uncertain outcomes (486, 10%) were most often explained by limited follow-up information. The overall area under the receiver operating characteristic curve was highest for ADNEX with CA125 (0.94, 95% confidence interval 0.92 to 0.96), ADNEX without CA125 (0.94, 0.91 to 0.95) and SRRisk (0.94, 0.91 to 0.95), and lowest for RMI (0.89, 0.85 to 0.92). Calibration varied among centres for all models, however the ADNEX models and SRRisk were the best calibrated. Calibration of the estimated risks for the tumour subtypes was good for ADNEX irrespective of whether or not CA125 was included as a predictor. Overall clinical utility (net benefit) was highest for the ADNEX models and SRRisk, and lowest for RMI. For patients who received at least one follow-up scan (n=1958), overall area under the receiver operating characteristic curve ranged from 0.76 (95% confidence interval 0.66 to 0.84) for RMI to 0.89 (0.81 to 0.94) for ADNEX with CA125.
conclusionsOur study found the ADNEX models and SRRisk are the best models to distinguish between benign and malignant masses in all patients presenting with an adnexal mass, including those managed conservatively.
trial registrationClinicalTrials.gov NCT01698632.
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