Jan Yvan Jos Verbakel

The Risk of Endometrial Malignancy and Other Endometrial Pathology in Women with Abnormal Uterine Bleeding: An Ultrasound-Based Model Development Study by the IETA Group

<b><i>Objectives:</i></b> The aim of this study was to develop a model that can discriminate between different etiologies of abnormal uterine bleeding. <b><i>Design:</i></b> The International Endometrial Tumor Analysis 1 study is a multicenter observational diagnostic study in 18 bleeding clinics in 9 countries. Consecutive women with abnormal vaginal bleeding presenting for ultrasound examination (<i>n</i> = 2,417) were recruited. The histology was obtained from endometrial sampling, D&C, hysteroscopic resection, hysterectomy, or ultrasound follow-up for >1 year. <b><i>Methods:</i></b> A model was developed using multinomial regression based on age, body mass index, and ultrasound predictors to distinguish between: (1) endometrial atrophy, (2) endometrial polyp or intracavitary myoma, (3) endometrial malignancy or atypical hyperplasia, (4) proliferative/secretory changes, endometritis, or hyperplasia without atypia and validated using leave-center-out cross-validation and bootstrapping. The main outcomes are the model’s ability to discriminate between the four outcomes and the calibration of risk estimates. <b><i>Results:</i></b> The median age in 2,417 women was 50 (interquartile range 43–57). 414 (17%) women had endometrial atrophy; 996 (41%) had a polyp or myoma; 155 (6%) had an endometrial malignancy or atypical hyperplasia; and 852 (35%) had proliferative/secretory changes, endometritis, or hyperplasia without atypia. The model distinguished well between malignant and benign histology (<i>c</i>-statistic 0.88 95% CI: 0.85–0.91) and between all benign histologies. The probabilities for each of the four outcomes were over- or underestimated depending on the centers. <b><i>Limitations:</i></b> Not all patients had a diagnosis based on histology. The model over- or underestimated the risk for certain outcomes in some centers, indicating local recalibration is advisable. <b><i>Conclusions:</i></b> The proposed model reliably distinguishes between four histological outcomes. This is the first model to discriminate between several outcomes and is the only model applicable when menopausal status is uncertain. The model could be useful for patient management and counseling, and aid in the interpretation of ultrasound findings. Future research is needed to externally validate and locally recalibrate the model.

Ultrasound‐based risk model for preoperative prediction of lymph‐node metastases in women with endometrial cancer: model‐development study

ABSTRACTObjectiveTo develop a preoperative risk model, using endometrial biopsy results and clinical and ultrasound variables, to predict the individual risk of lymph‐node metastases in women with endometrial cancer.MethodsA mixed‐effects logistic regression model for prediction of lymph‐node metastases was developed in 1501 prospectively included women with endometrial cancer undergoing transvaginal ultrasound examination before surgery, from 16 European centers. Missing data, including missing lymph‐node status, were imputed. Discrimination, calibration and clinical utility of the model were evaluated using leave‐center‐out cross validation. The predictive performance of the model was compared with that of risk classification from endometrial biopsy alone (high‐risk defined as endometrioid cancer Grade 3/non‐endometrioid cancer) or combined endometrial biopsy and ultrasound (high‐risk defined as endometrioid cancer Grade 3/non‐endometrioid cancer/deep myometrial invasion/cervical stromal invasion/extrauterine spread).ResultsLymphadenectomy was performed in 691 women, of whom 127 had lymph‐node metastases. The model for prediction of lymph‐node metastases included the predictors age, duration of abnormal bleeding, endometrial biopsy result, tumor extension and tumor size according to ultrasound and undefined tumor with an unmeasurable endometrium. The model's area under the curve was 0.73 (95% CI, 0.68–0.78), the calibration slope was 1.06 (95% CI, 0.79–1.34) and the calibration intercept was 0.06 (95% CI, –0.15 to 0.27). Using a risk threshold for lymph‐node metastases of 5% compared with 20%, the model had, respectively, a sensitivity of 98% vs 48% and specificity of 11% vs 80%. The model had higher sensitivity and specificity than did classification as high‐risk, according to endometrial biopsy alone (50% vs 35% and 80% vs 77%, respectively) or combined endometrial biopsy and ultrasound (80% vs 75% and 53% vs 52%, respectively). The model's clinical utility was higher than that of endometrial biopsy alone or combined endometrial biopsy and ultrasound at any given risk threshold.ConclusionsBased on endometrial biopsy results and clinical and ultrasound characteristics, the individual risk of lymph‐node metastases in women with endometrial cancer can be estimated reliably before surgery. The model is superior to risk classification by endometrial biopsy alone or in combination with ultrasound. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

Validation of ultrasound strategies to assess tumor extension and to predict high‐risk endometrial cancer in women from the prospective IETA (International Endometrial Tumor Analysis)‐4 cohort

ABSTRACTObjectivesTo compare the performance of ultrasound measurements and subjective ultrasound assessment (SA) in detecting deep myometrial invasion (MI) and cervical stromal invasion (CSI) in women with endometrial cancer, overall and according to whether they had low‐ or high‐grade disease separately, and to validate published measurement cut‐offs and prediction models to identify MI, CSI and high‐risk disease (Grade‐3 endometrioid or non‐endometrioid cancer and/or deep MI and/or CSI).MethodsThe study comprised 1538 patients with endometrial cancer from the International Endometrial Tumor Analysis (IETA)‐4 prospective multicenter study, who underwent standardized expert transvaginal ultrasound examination. SA and ultrasound measurements were used to predict deep MI and CSI. We assessed the diagnostic accuracy of the tumor/uterine anteroposterior (AP) diameter ratio for detecting deep MI and that of the distance from the lower margin of the tumor to the outer cervical os (Dist‐OCO) for detecting CSI. We also validated two two‐step strategies for the prediction of high‐risk cancer; in the first step, biopsy‐confirmed Grade‐3 endometrioid or mucinous or non‐endometrioid cancers were classified as high‐risk cancer, while the second step encompassed the application of a mathematical model to classify the remaining tumors. The ‘subjective prediction model’ included biopsy grade (Grade 1 vs Grade 2) and subjective assessment of deep MI or CSI (presence or absence) as variables, while the ‘objective prediction model’ included biopsy grade (Grade 1 vs Grade 2) and minimal tumor‐free margin. The predictive performance of the two two‐step strategies was compared with that of simply classifying patients as high risk if either deep MI or CSI was suspected based on SA or if biopsy showed Grade‐3 endometrioid or mucinous or non‐endometrioid histotype (i.e. combining SA with biopsy grade). Histological assessment from hysterectomy was considered the reference standard.ResultsIn 1275 patients with measurable lesions, the sensitivity and specificity of SA for detecting deep MI was 70% and 80%, respectively, in patients with a Grade‐1 or ‐2 endometrioid or mucinous tumor vs 76% and 64% in patients with a Grade‐3 endometrioid or mucinous or a non‐endometrioid tumor. The corresponding values for the detection of CSI were 51% and 94% vs 50% and 91%. Tumor AP diameter and tumor/uterine AP diameter ratio showed the best performance for predicting deep MI (area under the receiver–operating characteristics curve (AUC) of 0.76 and 0.77, respectively), and Dist‐OCO had the best performance for predicting CSI (AUC, 0.72). The proportion of patients classified correctly as having high‐risk cancer was 80% when simply combining SA with biopsy grade vs 80% and 74% when using the subjective and objective two‐step strategies, respectively. The subjective and objective models had an AUC of 0.76 and 0.75, respectively, when applied to Grade‐1 and ‐2 endometrioid tumors.ConclusionsIn the hands of experienced ultrasound examiners, SA was superior to ultrasound measurements for the prediction of deep MI and CSI of endometrial cancer, especially in patients with a Grade‐1 or ‐2 tumor. The mathematical models for the prediction of high‐risk cancer performed as expected. The best strategies for predicting high‐risk endometrial cancer were combining SA with biopsy grade and the subjective two‐step strategy, both having an accuracy of 80%. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

Head-to-head comparison of the RMI and ADNEX models to estimate the risk of ovarian malignancy: a systematic review and meta-analysis of external validation studies

Objectives Assessment of Different NEoplasias in the adneXa (ADNEX) and Risk of Malignancy Index (RMI) are models that estimate the risk of malignancy in ovarian masses based on clinical and ultrasound information. The aim is to perform a meta-analysis of studies that compared the performance of the two models in the same patients (‘head-to-head comparison’). Design Systematic review and meta-analysis. Data sources Systematic literature search from publication of ADNEX model (15/10/2014) up to 31/07/2024 in Embase, Web of Science, Scopus, Medline (via PubMed) and EuropePMC. Eligibility criteria for selecting studies We included all studies that externally validated the performance of ADNEX (with or without CA125) and RMI on the same data. Data extraction and synthesis Two independent reviewers extracted data using a standardised extraction sheet. We assessed risk of bias using PROBAST. We performed random effects meta-analysis of the area under the receiver operating characteristic curve (AUC), sensitivity, specificity and clinical utility (net benefit, relative utility and probability of being useful in a hypothetical new centre) at thresholds commonly used clinically (10% risk of malignancy for ADNEX, 200 for RMI). Results We included 11 studies comprising 8271 tumours. Most studies were at high risk of bias. The summary AUC to distinguish benign from malignant tumours in operated patients for ADNEX with CA125 was 0.92 (95% CI 0.90 to 0.94) and for RMI it was 0.85 (0.81 to 0.89). Sensitivity and specificity for ADNEX with CA125 were 0.93 (0.90 to 0.96) and 0.77 (0.71 to 0.81) and for RMI, they were 0.61 (0.56 to 0.67) and 0.92 (0.89 to 0.94). The probability of the test being useful in a hypothetical new centre in operated patients was 96% for ADNEX with CA125 and 15% for RMI at the selected thresholds. Conclusions ADNEX has better discrimination and clinical utility than RMI.