Large ovarian tumors in adolescents, a systematic review of reported cases, diagnostic findings and surgical management.

The purpose of this study was to analyze the published cases regarding large ovarian masses in adolescents, in order to find useful clinical implications for the diagnosis and management of the condition. The methodology employed a systematic review of reported cases of large/giant ovarian tumor in adolescence. The main objective was to assess the imagery findings, histology of the tumor, and the type of surgery performed. Our study included 58 patients with the tumor diameters ranged between 11 and 42 cm, with a median value of 22.75 cm and a mean diameter of 24.66 ± 8.50 cm. The lesions were benign in 47 cases (81%), borderline in 2 cases (3.4%), and malign in 9 cases (15.6%). We found no statistically significant difference (p > 0.05) between the size of the tumors and the ovarian markers levels. At the same time, a statistically significant difference was identified (p=<0,001) between elevated levels of the serum markers and the malignant tumor type (OR = 12.45; CI95 = 2.55-94.77). The main types of surgery performed were cystectomy (35 cases), oophorectomy (4 cases), salpingo-oophorectomy (18 cases). Open laparotomy was performed in 46 cases on tumors with a median diameter of 25 ± 8.8 cm, while laparoscopy was done in 12 cases on tumors with a median diameter of 23.7 ± 7.6 cm. For optimal surgical management, it is advisable to assess first the imagery findings and tumor markers. Then, the laparoscopic approach should be considered as a feasible option, especially in cases where there are no predictive factors for a malign disease.

Transradial Embolization, an Underused Type of Uterine Artery Embolization Approach: A Systematic Review.

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.

Logistic models and artificial intelligence in the sonographic assessment of adnexal masses - a systematic review of the literature.

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.