Estimating risk of malignancy in adnexal masses: external validation of the ADNEX model and comparison with other frequently used ultrasound methods

Abstract

ObjectivesTo validate externally the performance of the Assessment of Different NEoplasias in the adneXa (ADNEX) model and compare this model with other frequently used models in the differentiation between benign and malignant adnexal masses.MethodsIn this retrospective diagnostic accuracy study, we assessed data collected prospectively from patients with adnexal pathology who underwent real‐time transvaginal or transrectal ultrasound by a single expert ultrasonographer in a tertiary care hospital between July 2011 and July 2015. The presence of a malignancy was determined by subjective assessment and use of four prediction models: the ADNEX model, simple ultrasound‐based rules (simple rules), Logistic Regression model 2 (LR2) and the Risk of Malignancy Index (RMI), of which three different variants were assessed. Pathology was the clinical reference standard.ResultsIn total, 851 consecutive patients underwent ultrasound examination for an adnexal mass. For 326 patients (128 premenopausal and 198 postmenopausal), pathology results were available (211 (64.7%) benign; 115 (35.3%) malignant) and these were included in the analysis. The area under the receiver–operating characteristics curve (AUC) of the ADNEX model for the discrimination between benign and malignant tumors was 0.93 (95% CI, 0.89–0.95). AUCs for the subtypes of malignancy (i.e. borderline, Stage I–IV and metastatic adnexal tumors) ranged between 0.60 and 0.90. Only subjective assessment (AUC, 0.96 (95% CI, 0.93–0.98)) was superior to the ADNEX model (P = 0.01) in differentiating malignant from benign tumors. AUCs for the other models were 0.92 (95% CI, 0.89–0.95) for LR2, 0.85 (95% CI, 0.81–0.89) for RMI‐I, 0.82 (95% CI, 0.77–0.86) for RMI‐II and 0.84 (95% CI, 0.80–0.88) for RMI‐III. At the proposed cut‐off of ≥ 10%, the ADNEX model had the highest sensitivity (0.98 (95% CI, 0.93–1.00)) but the lowest specificity (0.62 (95% CI, 0.55–0.68)) compared with the other models. Both subjective assessment (sensitivity, 0.90 (95% CI, 0.83–0.95); specificity 0.91 (95% CI, 0.86–0.94)) and the simple rules model with inconclusive cases classified by subjective assessment (sensitivity, 0.89 (95% CI, 0.81–0.94); specificity, 0.90 (95% CI, 0.85–0.94)) had lower sensitivity, but their sensitivity and specificity were better balanced.ConclusionsAlthough the test performance of subjective assessment by an expert remains superior, the ADNEX model can help in the differentiation between benign and malignant ovarian tumors. The advantage of the ADNEX model as a polytomous model remains to be shown. © 2016 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

External validation of ADNEX model for diagnosing ovarian cancer: evaluating performance of differentiation between tumor subgroups

“…The area under the receiver-operating characteristics curve (AUC) of the ADNEX model for discriminating between benign and all malignant tumors can be obtained by the standard approach, using the overall predicted risk of malignancy. Using this method, the reported AUC was 0.925 by Araujo et al 1 and 0.93 by Meys et al 2 , which is in line with other validation studies of the ADNEX model 3,5 .…”

“…External validation of ADNEX model for diagnosing ovarian cancer: evaluating performance of differentiation between tumor subgroups I would like to thank Araujo 1 and Meys 2 and their colleagues for their validation studies of the ADNEX model. For women with at least one persistent adnexal mass who are scheduled for surgery, the ADNEX model can predict their risk of having one of five different types of adnexal mass: benign, borderline, Stage I ovarian cancer (OC), Stage II-IV OC and secondary metastatic OC 3 .…”

Response to letter to the editor concerning validation of IOTA ADNEX model

“…In general, the results confirm good polytomous discrimination from the first study by Van Calster et al, and were comparable to later ADNEX model evaluation studies (Table 6) (Van Calster et al, 2014, Araujo et al, 2016, Meys et al, 2016, Sayasneh et al, 2016). Both polytomous discrimination indexes (PDI) were > 0.2, which in this case is a threshold for a random performance.…”

Validation of the Performance of International Ovarian Tumor Analysis (IOTA) Methods in the Diagnosis of Early Stage Ovarian Cancer in a Non-Screening Population

“…As a multiclass prediction model, ADNEX not only calculates the likelihood of malignancy in adnexal masses, but also divides this into the likelihood that the mass is borderline malignant, stage I primary invasive ovarian cancer, stage II–IV primary invasive ovarian cancer, or a metastasis in the ovary from another primary tumor [25]. The performance of ADNEX is at least as good as the performance of previous IOTA methods, as confirmed by external validation studies [26,27,28,29,30]. The ADNEX model is available online and in mobile applications ().…”