ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies
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 studiesDat...
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BMJ Publishing Group
2024-08-01
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| Series: | BMJ Medicine |
| Online Access: | https://bmjmedicine.bmj.com/content/3/1/e000817.full |
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| author | Jan Y Verbakel Lil Valentin Ben Van Calster Gary Collins Dirk Timmerman Laure Wynants Paula Dhiman Lasai Barreñada Ashleigh Ledger |
| author_facet | Jan Y Verbakel Lil Valentin Ben Van Calster Gary Collins Dirk Timmerman Laure Wynants Paula Dhiman Lasai Barreñada Ashleigh Ledger |
| author_sort | Jan Y Verbakel |
| collection | DOAJ |
| description | 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 studiesData 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. |
| format | Article |
| id | doaj-art-cf1c00d5882a42348b14ac0e096ed8a5 |
| institution | Kabale University |
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| language | English |
| publishDate | 2024-08-01 |
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| spelling | doaj-art-cf1c00d5882a42348b14ac0e096ed8a52024-12-28T17:25:08ZengBMJ Publishing GroupBMJ Medicine2754-04132024-08-013110.1136/bmjmed-2023-000817ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studiesJan Y Verbakel0Lil Valentin1Ben Van Calster2Gary Collins3Dirk Timmerman4Laure Wynants5Paula Dhiman6Lasai Barreñada7Ashleigh Ledger8Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKDepartment of Obstetrics and Gynaecology, Skåne University Hospital, Malmo, SwedenDepartment of Development and Regeneration, KU Leuven, Leuven, BelgiumCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UKQueen Charlotte`s Hospital, Imperial College Healthcare NHS Trust, London, UKDepartment of Development and Regeneration, KU Leuven, Leuven, BelgiumNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford, UKDepartment of Development and Regeneration, KU Leuven, Leuven, BelgiumDepartment of Development and Regeneration, KU Leuven, Leuven, BelgiumObjectives 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 studiesData 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.https://bmjmedicine.bmj.com/content/3/1/e000817.full |
| spellingShingle | Jan Y Verbakel Lil Valentin Ben Van Calster Gary Collins Dirk Timmerman Laure Wynants Paula Dhiman Lasai Barreñada Ashleigh Ledger ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies BMJ Medicine |
| title | ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies |
| title_full | ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies |
| title_fullStr | ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies |
| title_full_unstemmed | ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies |
| title_short | ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies |
| title_sort | adnex risk prediction model for diagnosis of ovarian cancer systematic review and meta analysis of external validation studies |
| url | https://bmjmedicine.bmj.com/content/3/1/e000817.full |
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