L. Savelli

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 (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.

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.

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.