F. Buonomo

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.

International multicenter validation of AI-driven ultrasound detection of ovarian cancer

Abstract Ovarian lesions are common and often incidentally detected. A critical shortage of expert ultrasound examiners has raised concerns of unnecessary interventions and delayed cancer diagnoses. Deep learning has shown promising results in the detection of ovarian cancer in ultrasound images; however, external validation is lacking. In this international multicenter retrospective study, we developed and validated transformer-based neural network models using a comprehensive dataset of 17,119 ultrasound images from 3,652 patients across 20 centers in eight countries. Using a leave-one-center-out cross-validation scheme, for each center in turn, we trained a model using data from the remaining centers. The models demonstrated robust performance across centers, ultrasound systems, histological diagnoses and patient age groups, significantly outperforming both expert and non-expert examiners on all evaluated metrics, namely F1 score, sensitivity, specificity, accuracy, Cohen’s kappa, Matthew’s correlation coefficient, diagnostic odds ratio and Youden’s J statistic. Furthermore, in a retrospective triage simulation, artificial intelligence (AI)-driven diagnostic support reduced referrals to experts by 63% while significantly surpassing the diagnostic performance of the current practice. These results show that transformer-based models exhibit strong generalization and above human expert-level diagnostic accuracy, with the potential to alleviate the shortage of expert ultrasound examiners and improve patient outcomes.

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.