F. Ciccarone

Imaging in gynecological disease (30): clinical and ultrasound characteristics of usual‐type and variants of leiomyoma

ABSTRACT Objective To characterize the clinical and ultrasound features of usual‐type leiomyoma and variants of leiomyoma. Methods This retrospective, single‐center study included patients with a histologically confirmed diagnosis of benign mesenchymal uterine tumor, prospectively collected between January 2019 and December 2021 in the MYometrial Lesion UltrasouNd And mRi (MYLUNAR) study. Tumors were classified according to the Morphological Uterus Sonographic Assessment criteria and grouped according to the 2020 World Health Organization (WHO) classification of female genital tumors into usual‐type and variant leiomyomas. The variants of leiomyoma were further classified into specific histological subtypes as defined in the WHO classification. Two ultrasound examiners independently reviewed all available ultrasound images to identify patterns associated with usual‐type leiomyoma and variants of leiomyoma. Results A total of 1766 patients were included, of whom 1383 (78.3%) had usual‐type leiomyoma and 383 (21.7%) had a variant of leiomyoma. The median age at diagnosis was 45 (range, 15–88) years, with no statistically significant difference between the two groups. Most patients were premenopausal, although the variant group had a higher proportion of postmenopausal patients compared with the usual‐type group (21.5% vs 12.6%; P  < 0.001). On ultrasound examination, leiomyoma variants were larger than usual‐type leiomyomas (median maximum diameter, 82.5 mm vs 70.0 mm; P  < 0.001) and more frequently exhibited cystic areas (33.2% vs 12.8%; P  < 0.001). Acoustic shadows were present in 79.1% of variants, compared with 90.4% in usual‐type leiomyomas (P  < 0.001). Some variant subtypes appeared only in premenopausal women and had distinct morphological characteristics. Epithelioid leiomyomas were the largest variant, with a median diameter of 139.5 mm. Mitotically active leiomyomas showed regular margins and uniform echostructure, and lacked cystic areas in almost all cases. Lipoleiomyomas contained calcifications in some cases. After reviewing the ultrasound images, 13 patterns were identified, some of which were distinctive of specific variant subtypes. Conclusion Patients with usual‐type vs variant leiomyomas presented with some distinct clinical and ultrasound characteristics. Among variants of leiomyomas, some histotypes exhibited distinctive clinical and ultrasound features. © 2025 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.

Oregovomab: an investigational agent for the treatment of advanced ovarian cancer

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 (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.

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.