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This article discusses the assessment of the fetal sylvian fissure (SF) in the context of cortical abnormalities detection during prenatal ultrasound. The study compares the accuracy of different techniques for evaluating the SF on the axial plane in normal cases and fetuses with confirmed malformation of cortical development (MCD). The results show that subjective evaluation of the SF is more accurate in predicting the presence or absence of MCD compared to objective methods. The study suggests that there is currently no accepted standard method for assessing the SF by prenatal ultrasound. [Extracted from the article]

The preoperative evaluation of myometrial tumors is essential to avoid delayed treatment and to establish the appropriate surgical approach. Specifically, the differential diagnosis of leiomyosarcoma (LMS) is particularly challenging due to the overlapping of clinical, laboratory and ultrasound features between fibroids and LMS. In this work, we present a human-interpretable machine learning (ML) pipeline to support the preoperative differential diagnosis of LMS from leiomyomas, based on both clinical data and gynecological ultrasound assessment of 68 patients (8 with LMS diagnosis). The pipeline provides the following novel contributions: (i) end-users have been involved both in the definition of the ML tasks and in the evaluation of the overall approach; (ii) clinical specialists get a full understanding of both the decision-making mechanisms of the ML algorithms and the impact of the features on each automatic decision. Moreover, the proposed pipeline addresses some of the problems concerning both the imbalance of the two classes by analyzing and selecting the best combination of the synthetic oversampling strategy of the minority class and the classification algorithm among different choices, and the explainability of the features at global and local levels. The results show very high performance of the best strategy (AUC = 0.99, F1 = 0.87) and the strong and stable impact of two ultrasound-based features (i.e., tumor borders and consistency of the lesions). Furthermore, the SHAP algorithm was exploited to quantify the impact of the features at the local level and a specific module was developed to provide a template-based natural language (NL) translation of the explanations for enhancing their interpretability and fostering the use of ML in the clinical setting., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Leiomyosarcoma (LMS) is a rare type of mesenchymal tumor. Suspecting LMS before surgery is crucial for proper patient management. Ultrasound is the primary method for assessing myometrial lesions. The overlapping of clinical, laboratory, as well as ultrasound features between fibroids and LMS makes differential diagnosis difficult. We report our single-center experience in ultrasound imaging assessment of LMS patients, highlighting that misleading findings such as shadowing and absent or minimal vascularization may also occur in LMS. To avoid mistakes, a comprehensive evaluation of potentially overlapping ultrasound features is necessary in preoperative ultrasound evaluations of all myometrial tumors.

Sonovaginography is a way of assessing gynaecological diseases that can be described as cheap yet accurate and non-invasive. It consists of distention of the vagina with ultrasound gel or saline solution while performing transvaginal sonography to clearly visualize and assess a host of local cervical, as well as any vaginal, disorders. With endometriosis being a steadily growing gynaecological pathology affecting 8-15% of women of fertile age, transvaginal sonography (TVS) can be considered as one of the most accurate and comprehensive imaging techniques in its diagnosis. Nevertheless, the accuracy may vary depending on scan sites. The purpose of this narrative review is to assess the performance of sonovaginography in detecting endometriosis in those sites where TVS has a low sensitivity.