Background and Objectives: The most utilized approach for the embolization of uterine arteries is the transfemoral path. However, the transradial approach (TRA) has been gaining popularity among cardiologic interventions in the last years but only few studies have shown its applicability in uterine myoma treatment. The objective of this paper is to assess the feasibility, safety and efficacy of TRA when compared with the transbrachial, transulnar or transfemoral approach (TFA) for uterine arteries embolization (UAE). Materials and methods: A systematic review of the literature that analyzes the TRA for UAE it was carried out, in order to assess its safety and effectiveness. It was systematically searched the literature (Google Scholar, PubMed/MEDLINE, Cochrane Library and Embase) using the words “uterine artery embolization”/“uterine embolization” and “transradial”/“radial”. All the relevant papers published until March 2020 were retrieved and analyzed. Results: Ten studies were considered eligible for this topic. TRA is a comparable method with TFA for uterine artery embolization. Conclusions: These studies allowed us to conclude that TRA is as safe and efficient as TFA. Its advantages include few complications, shorter hospitalization period, and rapid mobilization but a steeper learning curve has the disadvantage of a longer learning curve compared to TFA. Yet, these findings are built on few reports and more research is needed.
Adnexal masses are common, yet challenging, in gynecological practice. Making the differential diagnosis between their benign and malignant condition is essential for optimal surgical management, but reliable pre-surgical differentiation is sometimes difficult using clinical features, ultrasound examination, or tumor markers alone. A possible way to improve the diagnosis is using artificial intelligence (AI) or logistic models developed based on compiling and processing clinical, ultrasound, and tumor marker data together. Ample research has already been conducted in this regard that medical practitioners could benefit from. In this systematic review, we present logistic models and methods using AI, chosen based on their demonstrated high performance in clinical practice. Although some external validation of these models has been performed, further prospective studies are needed in order to select the best model or to create a new, more efficient, one for the pre-surgical evaluation of ovarian masses.
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