L. Valentin

Prospective geographical and temporal validation of three published mathematical models to calculate risk of endometrial malignancy in patients with postmenopausal bleeding

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 ADNEX and IOTA two‐step strategy and estimation of risk of complications during follow‐up of adnexal masses in low‐risk population

ABSTRACTObjectivesTo evaluate the ability of the Assessment of Different NEoplasias in the adneXa (ADNEX) model and the International Ovarian Tumour Analysis (IOTA) two‐step strategy to predict malignancy in adnexal masses detected in an outpatient low‐risk setting, and to estimate the risk of complications in masses with benign ultrasound morphology managed using clinical and ultrasound follow‐up.MethodsThis single‐center study was performed at Hospital Universitari Dexeus, Barcelona, Spain, using interim data from the ongoing prospective observational IOTA Phase‐5 (IOTA5) study. The primary aim of the IOTA5 study is to describe the cumulative incidence of complications during follow‐up of adnexal masses classified as benign on ultrasound examination. Consecutive patients with an adnexal mass detected between June 2012 and September 2016 in a private center offering screening for gynecological cancer were included and followed up until February 2020. Tumors were classified as benign or malignant based on histology (if patients underwent surgery) or the outcome of clinical and ultrasound follow‐up at 12 (range, 10–14) months. Multiple imputation was used when outcomes were uncertain. The ability of the ADNEX model without CA125 and of the IOTA two‐step strategy to distinguish benign from malignant masses was evaluated retrospectively using the prospectively collected data. We assessed performance with regard to discrimination (area under the receiver‐operating‐characteristics curve (AUC)), calibration, classification (sensitivity and specificity) and clinical utility (Net Benefit). In the group of patients with a mass judged to be benign who were selected for conservative management, we evaluated the occurrence of spontaneous resolution or any mass complication during the first 5 years of follow‐up by assessing the cumulative incidence of malignancy, torsion, cyst rupture and minor mass complications (inflammation, infection or adhesions) and the time to occurrence of an event.ResultsA total of 2654 patients were recruited to the study. After application of exclusion criteria, 2039 patients with a newly detected mass were included for the model validation. Of those, 1684 (83%) masses were benign, 49 (2%) masses were malignant and, for 306 (15%) masses, the outcome was uncertain and therefore imputed. The AUC was 0.95 (95% CI, 0.89–0.98) for ADNEX without CA125 and 0.94 (95% CI, 0.88–0.97) for the two‐step strategy. Calibration performance could not be meaningfully interpreted because the small number of malignancies resulted in very wide confidence intervals. The two‐step strategy had better clinical utility than did the ADNEX model at malignancy risk thresholds < 3%. There were 1472 (72%) patients whose mass was judged to be benign based on pattern recognition by an experienced ultrasound examiner and were managed with clinical and ultrasound follow‐up. In this group, the 5‐year cumulative incidence was 66% (95% CI, 63–69%) for spontaneous resolution of the mass, 0% (95% CI, 0–0.2%) for torsion, 0.1% (95% CI, < 0.1–0.4%) for cyst rupture, 0.2% (95% CI, 0.1–0.6%) for a borderline tumor and 0.2% (95% CI, 0.1–0.6%) for invasive malignancy.ConclusionsThe ADNEX model and IOTA two‐step strategy performed well to distinguish benign from malignant adnexal masses detected in a low‐risk population. Conservative management is safe for masses with a benign ultrasound appearance in this population. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Ultrasound examiners' ability to describe ovarian cancer spread using preacquired ultrasound videoclips from a selected patient sample with high prevalence of cancer spread

ABSTRACTObjectivesTo assess the ability, as well as factors affecting the ability, of ultrasound examiners with different levels of ultrasound experience to detect correctly infiltration of ovarian cancer in predefined anatomical locations, and to evaluate the inter‐rater agreement regarding the presence or absence of cancer infiltration, using preacquired ultrasound videoclips obtained in a selected patient sample with a high prevalence of cancer spread.MethodsThis study forms part of the Imaging Study in Advanced ovArian Cancer multicenter observational study (NCT03808792). Ultrasound videoclips showing assessment of infiltration of ovarian cancer were obtained by the principal investigator (an ultrasound expert, who did not participate in rating) at 19 predefined anatomical sites in the abdomen and pelvis, including five sites that, if infiltrated, would indicate tumor non‐resectability. For each site, there were 10 videoclips showing cancer infiltration and 10 showing no cancer infiltration. The reference standard was either findings at surgery with histological confirmation or response to chemotherapy. For statistical analysis, the 19 sites were grouped into four anatomical regions: pelvis, middle abdomen, upper abdomen and lymph nodes. The videoclips were assessed by raters comprising both senior gynecologists (mainly self‐trained expert ultrasound examiners who perform preoperative ultrasound assessment of ovarian cancer spread almost daily) and gynecologists who had undergone a minimum of 6 months' supervised training in the preoperative ultrasound assessment of ovarian cancer spread in a gynecological oncology center. The raters were classified as highly experienced or less experienced based on annual individual caseload and the number of years that they had been performing ultrasound evaluation of ovarian cancer spread. Raters were aware that for each site there would be 10 videoclips with and 10 without cancer infiltration. Each rater independently classified every videoclip as showing or not showing cancer infiltration and rated the image quality (on a scale from 0 to 10) and their diagnostic confidence (on a scale from 0 to 10). A generalized linear mixed model with random effects was used to estimate which factors (including level of experience, image quality, diagnostic confidence and anatomical region) affected the likelihood of a correct classification of cancer infiltration. We assessed the observed percentage of videoclips classified correctly, the expected percentage of videoclips classified correctly based on the generalized linear mixed model and inter‐rater agreement (reliability) in classifying anatomical sites as being infiltrated by cancer.ResultsTwenty‐five raters participated in the study, of whom 13 were highly experienced and 12 were less experienced. The observed percentage of correct classification of cancer infiltration ranged from 70% to 100% depending on rater and anatomical site, and the median percentage of correct classification for the 25 raters ranged from 90% to 100%. The probability of correct classification of all 380 videoclips ranged from 0.956 to 0.975 and was not affected by the rater's level of ultrasound experience. The likelihood of correct classification increased with increased image quality and diagnostic confidence and was affected by anatomical region. It was highest for sites in the pelvis, second highest for those in the middle abdomen, third highest for lymph nodes and lowest for sites in the upper abdomen. The inter‐rater agreement of all 25 raters regarding the presence of cancer infiltration ranged from substantial (Fleiss kappa, 0.68 (95% CI, 0.66–0.71)) to very good (Fleiss kappa, 0.99 (95% CI, 0.97–1.00)) depending on the anatomical site. It was lowest for sites in the upper abdomen (Fleiss kappa, 0.68 (95% CI, 0.66–0.71) to 0.97 (95% CI, 0.94–0.99)) and highest for sites in the pelvis (Fleiss kappa, 0.94 (95% CI, 0.92–0.97) to 0.99 (95% CI, 0.97–1.00)).ConclusionsUltrasound examiners with different levels of ultrasound experience can classify correctly predefined anatomical sites as being infiltrated or not infiltrated by ovarian cancer based on video recordings obtained by an experienced ultrasound examiner, and the inter‐rater agreement is substantial. The likelihood of correct classification as well as the inter‐rater agreement is highest for sites in the pelvis and lowest for sites in the upper abdomen. However, owing to the study design, our results regarding diagnostic accuracy and inter‐rater agreement are likely to be overoptimistic. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid morphology on ultrasound

ABSTRACT Objective The primary aim was to identify radiomics ultrasound features that can distinguish between benign and malignant adnexal masses with solid ultrasound morphology, and between primary malignant (including borderline and primary invasive) and metastatic solid ovarian masses, and to develop ultrasound‐based machine learning models that include radiomics features to discriminate between benign and malignant solid adnexal masses. The secondary aim was to compare the discrimination performance of our newly developed radiomics models with that of the Assessment of Different NEoplasias in the adneXa (ADNEX) model and that of subjective assessment by an experienced ultrasound examiner. Methods This was a retrospective, observational single‐center study conducted at Fondazione Policlinico Universitario A. Gemelli IRCC, in Rome, Italy. Included were patients with a histological diagnosis of an adnexal tumor with solid morphology according to International Ovarian Tumor Analysis (IOTA) terminology at preoperative ultrasound examination performed in 2014–2020, who were managed with surgery. The patient cohort was split randomly into training and validation sets at a ratio of 70:30 and with the same proportion of benign and malignant tumors in the two subsets, with malignant tumors including borderline, primary invasive and metastatic tumors. We extracted 68 radiomics features, belonging to two different families: intensity‐based statistical features and textural features. Models to predict malignancy were built based on a random forest classifier, fine‐tuned using 5‐fold cross‐validation over the training set, and tested on the held‐out validation set. The variables used in model‐building were patient age and radiomics features that were statistically significantly different between benign and malignant adnexal masses and assessed as not redundant based on the Pearson correlation coefficient. We evaluated the discriminative ability of the models and compared it to that of the ADNEX model and that of subjective assessment by an experienced ultrasound examiner using the area under the receiver‐operating‐characteristics curve (AUC) and classification performance by calculating sensitivity and specificity. Results In total, 326 patients were included and 775 preoperative ultrasound images were analyzed. Of the 68 radiomics features extracted, 52 differed statistically significantly between benign and malignant tumors in the training set, and 18 uncorrelated features were selected for inclusion in model‐building. The same 52 radiomics features differed significantly between benign, primary malignant and metastatic tumors. However, the values of the features manifested overlapped between primary malignant and metastatic tumors and did not differ significantly between them. In the validation set, 25/98 (25.5%) tumors were benign and 73/98 (74.5%) were malignant (6 borderline, 57 primary invasive, 10 metastatic). In the validation set, a model including only radiomics features had an AUC of 0.80, sensitivity of 0.78 and specificity of 0.76 at an optimal cut‐off for risk of malignancy of 68%, based on Youden's index. The corresponding results for a model including age and radiomics features were AUC of 0.79, sensitivity of 0.86 and specificity of 0.56 (cut‐off 60%, based on Youden's index), while those of the ADNEX model were AUC of 0.88, sensitivity of 0.99 and specificity of 0.64 (at a 20% risk‐of‐malignancy cut‐off). Subjective assessment had a sensitivity of 0.99 and specificity of 0.72. Conclusions Our radiomics model had moderate discriminative ability on internal validation and the addition of age to this model did not improve its performance. Even though our radiomics models had discriminative ability inferior to that of the ADNEX model, our results are sufficiently promising to justify continued development of radiomics analysis of ultrasound images of adnexal masses. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Imaging in gynecological disease (22): clinical and ultrasound characteristics of ovarian embryonal carcinomas, non‐gestational choriocarcinomas and malignant mixed germ cell tumors

ABSTRACTObjectiveTo describe the clinical and ultrasound characteristics of three types of rare malignant ovarian germ cell tumor: embryonal carcinoma, non‐gestational choriocarcinoma and malignant mixed germ cell tumor.MethodsThis was a retrospective multicenter study. From the International Ovarian Tumor Analysis (IOTA) database, we identified patients with a histological diagnosis of ovarian embryonal carcinoma, non‐gestational choriocarcinoma or malignant mixed germ cell tumor, who had undergone preoperative ultrasound examination by an experienced ultrasound examiner between 2000 and 2020. Additional patients with the same histology were identified from the databases of the departments of gynecological oncology in the participating centers. All tumors were described using IOTA terminology. Three examiners reviewed all available ultrasound images and described them using pattern recognition.ResultsOne patient with embryonal carcinoma, five patients with non‐gestational ovarian choriocarcinoma and seven patients with ovarian malignant mixed germ cell tumor (six primary tumors and one recurrence) were identified. Seven patients were included in the IOTA studies and six patients were examined outside of the IOTA studies. The median age at diagnosis was 26 (range, 14–77) years. Beta‐human chorionic gonadotropin levels were highest in non‐gestational choriocarcinomas and alpha‐fetoprotein levels were highest in malignant mixed germ cell tumors. Most tumors were International Federation of Gynecology and Obstetrics (FIGO) Stage I (9/12 (75.0%)). All tumors were unilateral, and the median largest diameter was 129 (range, 38–216) mm. Of the tumors, 11/13 (84.6%) were solid and 2/13 (15.4%) were multilocular‐solid; 9/13 (69.2%) manifested abundant vascularization on color Doppler examination. Using pattern recognition, the typical ultrasound appearance was a large solid tumor with inhomogeneous echogenicity of the solid tissue and often dispersed cysts which, in most cases, were small and irregular. Some tumors had smooth contours while others had irregular contours.ConclusionsA unilateral, large solid tumor with inhomogeneous echogenicity of the solid tissue and with dispersed small cystic areas in a young woman should raise the suspicion of a rare malignant germ cell tumor. This suspicion can guide the clinician to test tumor markers specific for malignant germ cell tumors. © 2020 International Society of Ultrasound in Obstetrics and Gynecology

Vessel morphology depicted by three‐dimensional power Doppler ultrasound as second‐stage test in adnexal tumors that are difficult to classify: prospective diagnostic accuracy study

ABSTRACTObjectivesTo assess whether vessel morphology depicted by three‐dimensional (3D) power Doppler ultrasound improves discrimination between benignity and malignancy if used as a second‐stage test in adnexal masses that are difficult to classify.MethodsThis was a prospective observational international multicenter diagnostic accuracy study. Consecutive patients with an adnexal mass underwent standardized transvaginal two‐dimensional (2D) grayscale and color or power Doppler and 3D power Doppler ultrasound examination by an experienced examiner, and those with a ‘difficult’ tumor were included in the current analysis. A difficult tumor was defined as one in which the International Ovarian Tumor Analysis (IOTA) logistic regression model‐1 (LR‐1) yielded an ambiguous result (risk of malignancy, 8.3% to 25.5%), or as one in which the ultrasound examiner was uncertain regarding classification as benign or malignant when using subjective assessment. Even when the ultrasound examiner was uncertain, he/she was obliged to classify the tumor as most probably benign or most probably malignant. For each difficult tumor, one researcher created a 360° rotating 3D power Doppler image of the vessel tree in the whole tumor and another of the vessel tree in a 5‐cm3 spherical volume selected from the most vascularized part of the tumor. Two other researchers, blinded to the patient's history, 2D ultrasound findings and histological diagnosis, independently described the vessel tree using predetermined vessel features. Their agreed classification was used. The reference standard was the histological diagnosis of the mass. The sensitivity of each test for discriminating between benign and malignant difficult tumors was plotted against 1 – specificity on a receiver‐operating‐characteristics diagram, and the test with the point furthest from the reference line was considered to have the best diagnostic ability.ResultsOf 2403 women with an adnexal mass, 376 (16%) had a difficult mass. Ultrasound volumes were available for 138 of these cases. In 79/138 masses, the ultrasound examiner was uncertain about the diagnosis based on subjective assessment, in 87/138, IOTA LR‐1 yielded an ambiguous result and, in 28/138, both methods gave an uncertain result. Of the masses, 38/138 (28%) were malignant. Among tumors that were difficult to classify as benign or malignant by subjective assessment, the vessel feature ‘densely packed vessels’ had the best discriminative ability (sensitivity 67% (18/27), specificity 83% (43/52)) and was slightly superior to subjective assessment (sensitivity 74% (20/27), specificity 60% (31/52)). In tumors in which IOTA LR‐1 yielded an ambiguous result, subjective assessment (sensitivity 82% (14/17), specificity 79% (55/70)) was superior to the best vascular feature, i.e. changes in the diameter of vessels in the whole tumor volume (sensitivity 71% (12/17), specificity 69% (48/70)).ConclusionVessel morphology depicted by 3D power Doppler ultrasound may slightly improve discrimination between benign and malignant adnexal tumors that are difficult to classify by subjective ultrasound assessment. For tumors in which the IOTA LR‐1 model yields an ambiguous result, subjective assessment is superior to vessel morphology as a second‐stage test. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Imaging in gynecological disease (24): clinical and ultrasound characteristics of ovarian mature cystic teratomas

ABSTRACTObjectiveTo describe the clinical and ultrasound features of ovarian mature cystic teratomas (MCTs).MethodsThis was a retrospective study. From the International Ovarian Tumor Analysis (IOTA) database, we identified patients with a histologically confirmed diagnosis of MCT who had undergone transvaginal ultrasound examination between 1999 and 2016 (IOTA phases 1, 2, 3 and 5) in one of five centers. Ultrasound was performed by an experienced examiner who used the standardized IOTA examination technique and terminology. In addition to extracting data from the IOTA database, available two‐dimensional grayscale and color or power Doppler images were reviewed retrospectively to identify typical ultrasound features of MCT described previously and detect possible new features using pattern recognition. All images were reviewed by two independent examiners and further discussed with two ultrasound experts to reach consensus.ResultsIncluded in the study were 454 patients with histologically confirmed MCT. Median age was 33 (range, 8–90)  years and 66 (14.5%) patients were postmenopausal. Most MCTs were described by the original ultrasound examiner as unilocular (262/454 (57.7%)) or multilocular (70/454 (15.4%)) cysts with mixed echogenicity of cystic fluid (368/454 (81.1%)), acoustic shadowing (328/454 (72.2%)) and no or little vascularization on color Doppler (color score 1, 240/454 (52.9%); color score 2, 123/454 (27.1%)). The median largest lesion diameter was 66 (range, 15–310)  mm. A correct preoperative diagnosis of MCT was suggested by the original ultrasound examiner in 372/454 (81.9%) cases. On retrospective review of ultrasound images of 334 MCTs that had quality sufficient for assessment, ‘dots and/or lines’ and/or ‘echogenic white ball’ (typical features according to the literature) were present in 271/334 (81.1%) masses. We identified four new ultrasound features characteristic of MCT: ‘cotton wool tufts’, ‘mushroom cap sign’, ‘completely hyperechogenic lesion’ and ‘starry sky sign’. At least one classical or novel ultrasound feature was present in 315/334 (94.3%) MCTs. Twenty‐nine (8.7%) MCTs manifested vascularized solid tissue, of which seven exhibited no typical features.ConclusionWe provide a comprehensive overview of conventional and newly described ultrasound features of MCTs. Only a small proportion of MCTs did not manifest any of the typical features. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Imaging in gynecological disease (23): clinical and ultrasound characteristics of ovarian carcinosarcoma

ABSTRACTObjectiveTo describe the clinical and ultrasound characteristics of ovarian carcinosarcoma.MethodsThis was a retrospective multicenter study. Patients with a histological diagnosis of ovarian carcinosarcoma, who had undergone preoperative ultrasound examination between 2010 and 2019, were identified from the International Ovarian Tumor Analysis (IOTA) database. Additional patients who were examined outside of the IOTA study were identified from the databases of the participating centers. The masses were described using the terms and definitions of the IOTA group. Additionally, two experienced ultrasound examiners reviewed all available images to identify typical ultrasound features using pattern recognition.ResultsNinety‐one patients with ovarian carcinosarcoma who had undergone ultrasound examination were identified, of whom 24 were examined within the IOTA studies and 67 were examined outside of the IOTA studies. Median age at diagnosis was 66 (range, 33–91) years and 84/91 (92.3%) patients were postmenopausal. Most patients (67/91, 73.6%) were symptomatic, with the most common complaint being pain (51/91, 56.0%). Most tumors (67/91, 73.6%) were International Federation of Gynecology and Obstetrics (FIGO) Stage III or IV. Bilateral lesions were observed on ultrasound in 46/91 (50.5%) patients. Ascites was present in 38/91 (41.8%) patients. The median largest tumor diameter was 100 (range, 18–260) mm. All ovarian carcinosarcomas contained solid components, and most were described as solid (66/91, 72.5%) or multilocular‐solid (22/91, 24.2%). The median diameter of the largest solid component was 77.5 (range, 11–238) mm. Moderate or rich vascularization was found in 78/91 (85.7%) cases. Retrospective analysis of ultrasound images and videoclips using pattern recognition in 73 cases revealed that all tumors had irregular margins and inhomogeneous echogenicity of the solid components. Forty‐seven of 73 (64.4%) masses appeared as a solid tumor with cystic areas. Cooked appearance of the solid tissue was identified in 28/73 (38.4%) tumors. No pathognomonic ultrasound sign of ovarian carcinosarcoma was found.ConclusionsOvarian carcinosarcomas are usually diagnosed in postmenopausal women and at an advanced stage. The most common ultrasound appearance is a large solid tumor with irregular margins, inhomogeneous echogenicity of the solid tissue and cystic areas. The second most common pattern is a large multilocular‐solid mass with inhomogeneous echogenicity of the solid tissue. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Terms, definitions and measurements to describe sonographic features of lymph nodes: consensus opinion from the Vulvar International Tumor Analysis (VITA) group

ABSTRACTIn centers with access to high‐end ultrasound machines and expert sonologists, ultrasound is used to detect metastases in regional lymph nodes from melanoma, breast cancer and vulvar cancer. There is, as yet, no international consensus on ultrasound assessment of lymph nodes in any disease or medical condition. The lack of standardized ultrasound nomenclature to describe lymph nodes makes it difficult to compare results from different ultrasound studies and to find reliable ultrasound features for distinguishing non‐infiltrated lymph nodes from lymph nodes infiltrated by cancer or lymphoma cells. The Vulvar International Tumor Analysis (VITA) collaborative group consists of gynecologists, gynecologic oncologists and radiologists with expertise in gynecologic cancer, particularly in the ultrasound staging and treatment of vulvar cancer. The work herein is a consensus opinion on terms, definitions and measurements which may be used to describe inguinal lymph nodes on grayscale and color/power Doppler ultrasound. The proposed nomenclature need not be limited to the description of inguinal lymph nodes as part of vulvar cancer staging; it can be used to describe peripheral lymph nodes in general, as well as non‐peripheral (i.e. parietal or visceral) lymph nodes if these can be visualized clearly. The association between the ultrasound features described here and histopathological diagnosis has not yet been established. VITA terms and definitions lay the foundations for prospective studies aiming to identify ultrasound features typical of metastases and other pathology in lymph nodes and studies to elucidate the role of ultrasound in staging of vulvar and other malignancies. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Imaging in gynecological disease (29): clinical and ultrasound features of primary ovarian immature teratoma

ABSTRACT Objective To describe the clinical and ultrasound characteristics at the time of diagnosis of primary ovarian immature teratoma with no other germ cell tumor components described on histopathology. Methods This was a retrospective study of women with a histological diagnosis of primary ovarian immature teratoma who had undergone a preoperative ultrasound examination between 1998 and 2024. Cases were identified from the databases of 17 contributing ultrasound centers and the International Ovarian Tumor Analysis (IOTA) database. The descriptions of the ultrasound images of the tumors made by the original ultrasound examiners using IOTA terminology were reported. In addition, grayscale and color or power Doppler ultrasound images or videoclips were retrieved for all tumors. Two independent ultrasound examiners reviewed the retrieved material and searched for specific ultrasound characteristics of immature teratomas using pattern recognition. We present their agreed description of the tumors. Results In total, 64 patients with ovarian immature teratoma were included, of which 38 (59.4%) were obtained from the IOTA database (IOTA studies phase 1, 1b, 2, 3, 5 and 7). The median age of the patients at diagnosis was 24.5 (interquartile range (IQR), 18.8–31.0; range, 12–50) years. The most common presenting symptoms were abdominal or pelvic pain (38/60, 63.3%) and abdominal swelling (30/60, 50.0%). All immature teratomas were unilateral. The median largest diameter of the tumor was 149.5 (IQR, 125.0–183.8; range, 27–400) mm. Using IOTA terminology, most tumors were described as multilocular‐solid (32/64, 50.0%) or solid lesions (22/64, 34.4%). When present, the solid component had a median largest diameter of 98.5 (IQR, 59.8–146.8; range 6–400) mm. Most masses showed minimal (19/63, 30.2%) or moderate (35/63, 55.6%) vascularization on color or power Doppler ultrasound examination. Using pattern recognition, the most typical ultrasound feature was heterogeneous, bizarre echogenicity of the solid components, with hyperechogenic areas, cystic spaces and acoustic shadows. This feature, which we consider pathognomonic, was present in 48/57 (84.2%) immature teratomas in which the solid components were adequately assessable. Conclusions The typical ultrasound appearance of an ovarian immature teratoma is a large unilateral adnexal mass with large solid components that is poorly or moderately vascularized. The pathognomonic feature is heterogeneous echogenicity of the solid components with hyperechogenic areas, cystic spaces and acoustic shadows. Preoperative suspicion of immature teratoma can guide treatment, such as offering fertility‐sparing surgery. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Imaging in gynecological disease (27): clinical and ultrasound characteristics of recurrent ovarian stromal cell tumors

ABSTRACTObjectiveTo describe the clinical and ultrasound characteristics of recurrent granulosa cell and Sertoli–Leydig cell tumors.MethodsThis was a retrospective observational study performed at Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, IRCCS, Rome (Gemelli center), Italy. Patients with a histological diagnosis of recurrent granulosa cell tumor or Sertoli–Leydig cell tumor were identified from the database of the Department of Gynecological Oncology. Those who had undergone a preoperative ultrasound examination at the Gemelli center between 2012 and 2020 were included, and the data retrieved from the original ultrasound reports. In all of these reports, the recurrent tumors were described using International Ovarian Tumor Analysis (IOTA) terminology. If a patient had more than one episode of relapse, information from all episodes was collected. If there was more than one recurrent tumor at the same ultrasound examination, all tumors were included. One expert sonographer also reviewed all available ultrasound images to identify typical ultrasound patterns using pattern recognition.ResultsWe identified 30 patients with a histological diagnosis of recurrent granulosa cell tumor (25 patients, 55 tumors) or Sertoli–Leydig cell tumor (five patients, seven tumors). All 30 had undergone at least one preoperative ultrasound examination at the Gemelli center and were included. These women had a total of 66 episodes of relapse, of which a preoperative ultrasound examination had been performed at the Gemelli center in 34, revealing 62 recurrent lesions: one in 22/34 (64.7%) episodes of relapse, two in 4/34 (11.8%) episodes and three or more in 8/34 (23.5%) episodes. Most recurrent granulosa cell tumors (38/55, 69.1%) and recurrent Sertoli–Leydig tumors (6/7, 85.7%) were classified as solid or multilocular‐solid tumors, while 8/55 (14.5%) recurrent granulosa cell tumors and 1/7 (14.3%) recurrent Sertoli–Leydig cell tumors were unilocular cysts and 9/55 (16.4%) recurrent granulosa cell tumors were multilocular cysts. The nine unilocular cysts had contents that were anechoic (n = 2) or had low‐level echogenicity (n = 7), had either smooth (n = 4) or irregular (n = 5) internal cyst walls, and ranged in largest diameter from 8 to 38 mm, with three being < 20 mm and five being 20–30 mm. On retrospective review of the images, two typical ultrasound patterns were described: small solid tumor measuring < 2 cm (15/62, 24.2%) and tumor with vascularized echogenic ground‐glass‐like content (12/62, 19.4%).ConclusionsSome granulosa cell and Sertoli–Leydig cell recurrences manifest one of two typical ultrasound patterns, while some appear as unilocular cysts. These are usually classified as benign, but in patients being followed up for a granulosa cell tumor or Sertoli–Leydig cell tumor, a unilocular cyst should be considered suspicious of recurrence. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Imaging in gynecological disease (28): clinical and ultrasound characteristics of serous and mucinous cystadenomas in the adnexa

ABSTRACTObjectiveTo describe the clinical and ultrasound characteristics of serous and mucinous cystadenomas in the adnexa.MethodsThis was a retrospective international multicenter study. Using the International Ovarian Tumor Analysis (IOTA) database, patients with a histological diagnosis of serous or mucinous cystadenoma who had undergone preoperative ultrasound examination between 1999 and 2016 (IOTA studies phase 1, 1b, 2, 3 and 5) were identified. All masses were described using the standardized IOTA terminology. The diagnosis assigned by the original ultrasound examiner based on subjective assessment was recorded. Two reviewers assessed the available digital ultrasound images using pattern recognition to identify typical sonographic features of cystadenomas.ResultsA total of 1318 patients were included: 687 (52.1%) with serous cystadenomas and 631 (47.9%) with mucinous cystadenomas. Based on the data recorded prospectively in the IOTA database, for serous cystadenomas the median diameter of the largest tumor was 68 (range, 14–320) mm. Most serous cystadenomas were described as unilateral (588/687 (85.6%)), with unilocular (274/687 (39.9%)) or multilocular (221/687 (32.2%)) morphology, and most had anechoic cyst content (508/687 (73.9%)). Most serous cystadenomas were not vascularized (color score of 1; 327/687 (47.6%)) or were poorly vascularized (color score of  2; 253/687 (36.8%)) on color Doppler examination. The original ultrasound examiner correctly classified 91.1% (626/687) of serous cystadenomas as benign and suggested the correct specific diagnosis in 51.5% (354/687) of tumors. For mucinous cystadenomas, the median diameter of the largest tumor was 93 (range, 12–550) mm. Most mucinous cystadenomas were described as unilateral (594/631 (94.1%)) with multilocular morphology (357/631 (56.6%)), and most manifested low‐level echogenicity (334/631 (52.9%)). Most mucinous cystadenomas were poorly (color score of 2; 248/631 (39.3%)) or moderately (color score of 3; 194/631 (30.7%)) vascularized on color Doppler examination. The original ultrasound examiner correctly classified 87.5% (552/631) of mucinous cystadenomas as benign and suggested the correct specific diagnosis in 42.9% (271/631) of tumors. Based on pattern recognition (review of ultrasound images available for 433 tumors), the most typical sonographic features of serous cystadenomas were unilocular cyst (100/211 (47.4%)) or multilocular cyst with < 10 cyst locules (71/211 (33.6%)), whereas the typical features of mucinous cystadenomas were multilocular cyst with < 10 cyst locules (99/222 (44.6%)), unilocular cyst (78/222 (35.1%)) or multilocular cyst with > 10 cyst locules (31/222 (14.0%)). A honeycomb nodule was found in some mucinous cystadenomas (31/222 (14.0%)) but was not found in serous cystadenomas.ConclusionsSerous and mucinous cystadenomas exhibit typical sonographic features, allowing ultrasound examiners to assign a correct specific diagnosis to most tumors. Recognizing the ultrasound features of cystadenomas and avoiding misdiagnosing them as malignant can help prevent surgery for these benign tumors in asymptomatic patients. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Estimating risk of endometrial malignancy and other intracavitary uterine pathology in women without abnormal uterine bleeding using IETA‐1 multinomial regression model: validation study

ABSTRACTObjectivesTo assess the ability of the International Endometrial Tumor Analysis (IETA)‐1 polynomial regression model to estimate the risk of endometrial cancer (EC) and other intracavitary uterine pathology in women without abnormal uterine bleeding.MethodsThis was a retrospective study, in which we validated the IETA‐1 model on the IETA‐3 study cohort (n = 1745). The IETA‐3 study is a prospective observational multicenter study. It includes women without vaginal bleeding who underwent a standardized transvaginal ultrasound examination in one of seven ultrasound centers between January 2011 and December 2018. The ultrasonography was performed either as part of a routine gynecological examination, during follow‐up of non‐endometrial pathology, in the work‐up before fertility treatment or before treatment for uterine prolapse or ovarian pathology. Ultrasonographic findings were described using IETA terminology and were compared with histology, or with results of clinical and ultrasound follow‐up of at least 1 year if endometrial sampling was not performed. The IETA‐1 model, which was created using data from patients with abnormal uterine bleeding, predicts four histological outcomes: (1) EC or endometrial intraepithelial neoplasia (EIN); (2) endometrial polyp or intracavitary myoma; (3) proliferative or secretory endometrium, endometritis, or endometrial hyperplasia without atypia; and (4) endometrial atrophy. The predictors in the model are age, body mass index and seven ultrasound variables (visibility of the endometrium, endometrial thickness, color score, cysts in the endometrium, non‐uniform echogenicity of the endometrium, presence of a bright edge, presence of a single dominant vessel). We analyzed the discriminative ability of the model (area under the receiver‐operating‐characteristics curve (AUC); polytomous discrimination index (PDI)) and evaluated calibration of its risk estimates (observed/expected ratio).ResultsThe median age of the women in the IETA‐3 cohort was 51 (range, 20–85) years and 51% (887/1745) of the women were postmenopausal. Histology showed EC or EIN in 29 (2%) women, endometrial polyps or intracavitary myomas in 1094 (63%), proliferative or secretory endometrium, endometritis, or hyperplasia without atypia in 144 (8%) and endometrial atrophy in 265 (15%) women. The endometrial sample had insufficient material in five (0.3%) cases. In 208 (12%) women who did not undergo endometrial sampling but were followed up for at least 1 year without clinical or ultrasound signs of endometrial malignancy, the outcome was classified as benign. The IETA‐1 model had an AUC of 0.81 (95% CI, 0.73–0.89, n = 1745) for discrimination between malignant (EC or EIN) and benign endometrium, and the observed/expected ratio for EC or EIN was 0.51 (95% CI, 0.32–0.82). The model was able to categorize the four histological outcomes with considerable accuracy: the PDI of the model was 0.68 (95% CI, 0.62–0.73) (n = 1532). The IETA‐1 model discriminated very well between endometrial atrophy and all other intracavitary uterine conditions, with an AUC of 0.96 (95% CI, 0.95–0.98). Including only patients in whom the endometrium was measurable (n = 1689), the model's AUC was 0.83 (95% CI, 0.75–0.91), compared with 0.62 (95% CI, 0.52–0.73) when using endometrial thickness alone to predict malignancy (difference in AUC, 0.21; 95% CI, 0.08–0.32). In postmenopausal women with measurable endometrial thickness (n = 848), the IETA‐1 model gave an AUC of 0.81 (95% CI, 0.71–0.91), while endometrial thickness alone gave an AUC of 0.70 (95% CI, 0.60–0.81) (difference in AUC, 0.11; 95% CI, 0.01–0.20).ConclusionThe IETA‐1 model discriminates well between benign and malignant conditions in the uterine cavity in patients without abnormal bleeding, but it overestimates the risk of malignancy. It also discriminates well between the four histological outcome categories. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Radiomics‐based ultrasOund Model for differentiating Uterine Sarcomas from leiomyomas ( ROMUS ): a retrospective pilot Multicenter Italian Trials in Ovarian Cancer ( MITO ) study

ABSTRACT Objective To develop machine‐learning models that incorporate clinical information and radiomics features extracted from ultrasound images to distinguish uterine sarcomas from leiomyomas. Methods This retrospective, multicenter, pilot case–control study included 200 patients (100 with a uterine sarcoma and 100 with a usual‐type leiomyoma, i.e. including no benign leiomyoma variants) who underwent preoperative ultrasound examination between January 2010 and June 2022. The patient cohort was split (70:30) into training and validation sets, with the same proportion of leiomyomas and sarcomas in each subset. We extracted radiomics features belonging to different families: intensity‐based statistical features and textural features. The variables used in model building were patient age and the radiomics features that differed statistically significantly between sarcomas and leiomyomas and that were not redundant based on Spearman's correlation coefficient. Logistic regression, random forest, extreme gradient boosting (XGBoost) and support vector machine models were tested in the model development process. We evaluated the performance of the models in differentiating between sarcomas and leiomyomas using the area under the receiver‐operating‐characteristics curve (AUC), accuracy, sensitivity and specificity. We compared these results to those of subjective assessment by the original ultrasound examiner and to those of two independent expert ultrasound examiners who, blinded to clinical history, reviewed the same grayscale ultrasound images as those used for the radiomics analysis. Results Sixty‐three radiomics features were extracted. Of these, eight differed statistically significantly between sarcomas and leiomyomas and were not correlated, so were selected for inclusion in model building. In the validation set, the model that performed best in differentiating between sarcomas and leiomyomas was an XGBoost model integrating patient age and radiomics features. In the validation set, this model had an AUC of 0.93, sensitivity of 0.93 and specificity of 0.83, at a risk‐of‐malignancy cut‐off of 47% (the cut‐off that yielded the highest number of correct classifications based on Youden's index in the training set). The corresponding results for the model integrating only the radiomics features were: AUC of 0.87, sensitivity of 0.87 and specificity of 0.83. Subjective assessment by the original ultrasound examiner had a sensitivity of 0.87 and specificity of 1 in the validation set, while retrospective review of grayscale ultrasound images by ultrasound experts had a sensitivity of 0.87 and specificity of 0.80 (same results for both reviewers). Conclusion A model including eight radiomics features and patient age demonstrated reasonably good discriminative and classification performance for distinguishing uterine sarcomas from leiomyomas. Its classification ability was similar to that of subjective assessment by the original ultrasound examiner, being more sensitive but less specific. To confirm the role of radiomics for discriminating between uterine sarcomas and leiomyomas, large prospective studies including benign leiomyoma variants are needed. If good performance of radiomics models can be confirmed, integrating automated radiomics analysis into ultrasound machine software may help ultrasound examiners to discriminate between sarcomas and benign leiomyomas. © 2026 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Developing and validating ultrasound‐based machine‐learning models incorporating radiomics features to predict malignancy in adnexal masses

ABSTRACT Objective The primary aim of this study was to develop and internally validate ultrasound‐based radiomics models to discriminate between all types of benign and malignant adnexal masses. The secondary aim was to compare the performance of the radiomics models with that of the Assessment of Different NEoplasias in the adneXa (ADNEX) model. Methods This was a retrospective, observational, single‐center study, for which all patients with an adnexal mass that were included in the ongoing International Ovarian Tumor Analysis phase‐5 and phase‐7 studies and were examined using ultrasound between January 2012 and December 2023 at Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, were eligible for inclusion. Inclusion criteria were: adnexal mass detected by ultrasound; surgical removal of the adnexal mass within 180 days after the ultrasound examination; histological confirmation of an adnexal mass; and absence of a synchronous malignant tumor. Patients without digital ultrasound images saved in DICOM format were excluded. The patient cohort was split randomly into training and validation sets using a stratified split with a ratio of 70:30, to preserve the proportion of benign and malignant cases in the two sets. Two machine‐learning models for discriminating between benign and malignant adnexal masses were built using one image per tumor, with 5‐fold cross‐validation for hyperparameter tuning, and were tested on the validation set. The variables used in model building were patient age, serum CA 125 level and the radiomics features that differed significantly between benign and malignant tumors (determined using the Mann–Whitney U ‐test with Benjamini–Hochberg correction) and were not redundant based on Pearson correlation analysis. Histology was the reference standard. We assessed the discriminative performance of the radiomics models using the area under the receiver‐operating‐characteristics curve (AUC) and classification performance using sensitivity and specificity at the optimal cut‐off of each model to classify the mass as malignant, as determined by Youden's index. The diagnostic performance of the developed radiomics models was compared with that of the ADNEX model (AUC, sensitivity and specificity at the 10% risk‐of‐malignancy cut‐off, which is the recommended threshold for clinical use of the ADNEX model). Results In total, 4501 patients met the inclusion criteria. Among these, 2428 patients were excluded owing to an absence of ultrasound images or images unsuitable for radiomics analysis. Overall, a total of 2073 patients were included in the analysis, of whom 803 (38.7%) had a histologically confirmed malignant tumor. In the validation set ( n  = 622, including 254 malignancies), the clinical–radiomics model trained using the eXtreme Gradient Boosting algorithm, including age, serum CA 125 level and 14 selected radiomics features, achieved the highest performance, with an AUC of 0.89 (95% CI, 0.86–0.92), sensitivity of 0.83 (95% CI, 0.79–0.88) and specificity of 0.81 (95% CI, 0.77–0.85) at the optimal cut‐off (31% risk of malignancy, based on Youden's index). At a 10% risk‐of‐malignancy cut‐off, it had a sensitivity of 0.94 (95% CI, 0.91–0.97) and specificity of 0.48 (95% CI, 0.42–0.53). The ADNEX model had an AUC of 0.95 (95% CI, 0.93–0.97), sensitivity of 0.97 (95% CI, 0.95–0.99) and specificity of 0.72 (95% CI, 0.68–0.77) at the 10% risk‐of‐malignancy cut‐off in the validation set. Conclusions Our results support further exploration of radiomics analysis for distinguishing between benign and malignant adnexal masses in larger study populations. Future studies should consider using multiple images per tumor and testing alternative model‐building methods, and should perform external validation to assess the generalizability of the radiomics models. © 2026 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Imaging Study in Advanced Ovarian Cancer

The aim of the study is the assessment of tumour sites critical for the achievement of optimal cytoreduction in patients with advanced ovarian cancer using Ultrasound, CT and WB-DWI/MRI. The study uses an equivalence design with a hypothesis that cases with non-resectable disease identified by Index test (Ultrasound, CT and WB-DWI MRI) are equivalent to a portion of cases identified during surgery.

International Ovarian Tumour Analysis (IOTA) Phase 5

The purpose of this study is to learn more about the appearance and behavior of benign-looking adnexal masses. * Benign-looking means that when viewed here by ultrasound it has the appearance of looking not harmful or not malignant. * Adnexal refers to the 'adnexa', the space in the female pelvis on either side of the uterus (or where the uterus used to be if you previously had a hysterectomy). The adnexa includes, but is not limited to, the ovaries and the fallopian tubes. * Masses refers to a variety of structures, including but not limited to: * ovarian cysts that are fluid filled sacs within or attached to an ovary * ovarian tumors that can be solid tissue or a combination of cysts and solid tissue * hydrosalpinges that are fluid collections in the fallopian tube Many women have what appear to be benign adnexal masses. Many times, removal of the masses with surgery is not necessary. Often surgery is performed unnecessarily, for fear that these masses could be cancer. There is not much information available for doctors to know how and when to follow these masses, or which ones will become cancer. This study will combine information from centers all around the world regarding the behavior of all types of benign adnexal masses. The aim of this study is to develop decision tools for doctors to know the best way to treat these masses in order to improve the detection of ovarian cancer while at the same time reduce the number of unnecessary operations.