Comparison of ultrasound−based ADNEX model with magnetic resonance imaging for discriminating adnexal masses: a multi-center study

Hu, Yanli; Chen, Bin; Dong, Hongmei; Sheng, Bo; Xiao, Zhibo; Li, Jia; Wei, Tian; Lv, Fajin

doi:10.3389/fonc.2023.1101297

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…It is the rst multiclassi cation model that consists of three clinical indexes and six ultrasonic indexes [13]. A previous study demonstrated that the ADNEX model showed diagnostic performance for identifying adnexal lesions [14,15]. However, no studies have reported the validation of the ADNEX model in epithelial ovarian tumors.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumors

Xie,

Zhang,

Wang

et al. 2024

Preprint

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Background: The purpose of this study was to evaluate the ability of the International Ovarian Tumor Analysis-Assessment of Different NEoplasias in the adneXa (IOTA-ADNEX) model to distinguish between benign, borderline, and malignant epithelial ovarian tumors(BeEOTs, BEOTs, and MEOTs, respectively). Methods: The study included 813 patients with BeEOTs, BEOTs, and MEOTs who underwent ultrasound examinations and pelvic operations. Comparisons were made between the clinical information and ultrasonographic features of the three patient groups. Three clinical variables and six ultrasound variables were used to estimate malignancy risk. The sensitivity, specificity, positive predictive value, negative predictive value, and AUC (the area under the receiver operating characteristics [ROC] curve) of the ADNEX model were calculated. Results: Of the 813 patients, 257 (31.6%) had BeEOTs, 114 (14.0%) had BEOTs, and 442 (54.4%) had MEOTs. The most common type, serous and mucinous epithelial tumors, accounted for 81.3% of the total cases. In the MEOTs group, serous, mucinous, endometrioid, and clear cell tumors accounted for 85.3%, 2.5%, 4.5%, and 7.7% of the total cases, respectively. For a cut-off value of 10% to identify the overall risk for ovarian cancer (OC), the sensitivity and specificity were 99.1% and 73.2%, respectively. According to the ROC curves, the AUC was 0.987 (95% CI: 0.981–0.993) for BeEOTs compared with MEOTs, 0.820 (95% CI: 0.768–0.872) for BeEOTs compared with BEOTs, 0.912 (95% CI: 0.876–0.948) for BeEOTs compared with stage I OC, and 0.995 (95% CI: 0.992–0.998) for BeEOTs compared with stages II–IV OC. The AUC was 0.614 (95% CI: 0.519–0.709) for BEOTs compared with stage I OC, 0.903 (95% CI: 0.869–0.937) for BEOTs compared with stages II–IV OC, and 0.851 (95% CI: 0.800–0.902) for stage I OC compared with stages II–IV OC. Conclusions: The IOTA-ADNEX model demonstrated good diagnostic performance for the three categories of EOTs and can be helpful for clinical treatment management.

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Section: Introductionmentioning

confidence: 99%

Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumors

Xie,

Zhang,

Wang

et al. 2024

Preprint

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Comparison of ADNEX Model with GI-RADS Ultrasound Scoring System in Evaluation of Adnexal Mass

Yasmin,

Parveen,

Gupta

et al. 2024

J Obstet Gynecol India

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

Barrenada

Ledger

Dhiman

et al. 2023

Preprint

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Objectives: To conduct a systematic review of studies externally validating the ADNEX model for ovarian cancer diagnosis and perform a meta-analysis of its performance. Design: Systematic review, meta-analysis Data sources: Medline, EMBASE, WOS, Scopus, and EuropePMC up to 15/05/2023. Review methods: We included external validation studies of the performance of ADNEX using any study design and any study population comprising patients with an adnexal mass. Two independent reviewers extracted data. Disagreements were resolved through discussion. Reporting quality of the studies was scored using the TRIPOD reporting guideline and methodological conduct and risk of bias using the PROBAST tool. We performed random effects meta-analysis of the AUC, sensitivity and specificity at the 10% risk of malignancy threshold, and Net Benefit and Relative Utility at the 10% risk of malignancy threshold. Results: We included 47 studies (17,007 tumours) with median study sample size 261 (range 24-4905). On average, 61% of TRIPOD items were reported. Handling of missing data, sample size justification, and model calibration were rarely described. 91% of validations were at high risk of bias, mainly due to the unexplained exclusion of incomplete cases, low sample size, or absent calibration assessment. The summary AUC to distinguish benign from malignant tumours in operated patients was 0.93 (95% CI 0.92-0.94, 95% prediction interval 0.85-0.98) for ADNEX with CA125 as a predictor (9202 tumours, 43 centres, 18 countries, 21 studies) and 0.93 (95% CI 0.91-0.94, 95% prediction interval 0.85-0.98) for ADNEX without CA125 (6309 tumours, 31 centres, 13 countries, 12 studies). The estimated probability that the model has clinical utility in a new centre was 95% (with CA125) and 91% (without CA125). When restricting analysis to studies at low risk of bias, summary AUCs were 0.93 (with CA125) and 0.91 (without CA125), and estimated probabilities that the model has clinical utility were 89% (with CA125) and 87% (without CA125). Discussion: ADNEX performed well to distinguish benign from malignant tumours in populations from different countries and settings regardless of whether CA125 was used or not. A key limitation is that calibration was rarely assessed. Review registration: PROSPERO, CRD42022373182

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Comparison of ultrasound−based ADNEX model with magnetic resonance imaging for discriminating adnexal masses: a multi-center study

Cited by 5 publications

References 31 publications

Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumors

Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumors

Comparison of ADNEX Model with GI-RADS Ultrasound Scoring System in Evaluation of Adnexal Mass

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

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