ABSTRACT
OBJECTIVE: Lesbian couples must resort to adoption or donated semen to achieve parenthood, the latter usually involving assisted reproductive technology. The aim of this study is to assess homosexual women's knowledge about assisted reproductive techniques, the importance of perceived genetic and gestational relationships for their future mother-child bond, as well as their reproductive plans. METHODS: This is an observational study based on an anonymous survey disseminated online in several countries on different continents, addressed to homosexual women. RESULTS: From the 549 participants, most reported being well informed about reproductive options including assisted reproductive technology. The majority want to be a mother as part of a couple, mainly through assisted reproduction or step adoption of their partner's child. The importance of a genetic or gestational relationships with their future child varies greatly between women. Among the sampled women, pregnancy was believed to have a slightly greater impact on the future mother-child connection compared to genetics. CONCLUSIONS: Homosexual women are well informed about the assisted reproductive technology treatments. The majority considers it important to become a mother as a couple, mainly through assisted reproduction or step adoption of their partner's child. The importance given to gestation or genetic mother-child relationships varies greatly between women, and it seems they believe pregnancy may have a slightly greater impact on the future mother-child connection compared to genetics.
ABSTRACT
Some aspects of endometrial cancer (EC) preoperative work-up are still controversial, and debatable are the roles played by lymphadenectomy and radical surgery. Proper preoperative EC staging can help design a tailored surgical treatment, and this study aims to propose a new algorithm able to predict extrauterine disease diffusion. 293 EC patients were consecutively enrolled, and age, BMI, children's number, menopausal status, contraception, hormone replacement therapy, hypertension, histological grading, clinical stage, and serum HE4 and CA125 values were preoperatively evaluated. In order to identify before surgery the most important variables able to classify EC patients based on FIGO stage, we adopted a new statistical approach consisting of two-steps: 1) Random Forest with its relative variable importance; 2) a novel algorithm able to select the most representative Regression Tree (RERT) from an ensemble method. RERT, built on the above mentioned variables, provided a sensitivity, specificity, NPV and PPV of 90%, 76%, 94% and 65% respectively, in predicting FIGO stage > I. Notably, RERT outperformed the prediction ability of HE4, CA125, Logistic Regression and single cross-validated Regression Tree. Such algorithm has great potential, since it better identifies the true early-stage patients, thus providing concrete support in the decisional process about therapeutic options to be performed.
